Simulation of robotic TIG-welding

Simulation of robotic TIG-welding Mikael Ericsson

Department of Technology University of Trollhättan/Uddevalla P.O. Box 957 SE-461 29 Trollhättan, Sweden

Division of Robotics Department of Mechanical Engineering Lund Institute of Technology Lund University, P.O. Box 118, SE-221 00 Lund, Sweden

CODEN: LUTMDN/(TMMV-5170)/1-96/(2003) ISBN 91-628-5702-9  2003 by Mikael Ericsson and Department of Technology University of Trollhättan/Uddevalla. All rights reserved Printed in Sweden KFS I Lund AB, Lund

Abstract

Robotised welding is one of the most important robot tasks used in manufacturing industry. The operator usually performs the programming of the robot manually, i.e. by jogging the robot arm to each coordinate pose in space. Programming can, however, be made more accurate by the use of simulation, using so called Computer Aided Robotics. Simulation can also be a powerful tool to evaluate and control welding heat effects, such as unwanted stresses and deformation. The objective of this thesis was to develop a simulation tool and a method by which robot trajectories, temperature histories, residual stresses and distortion can be analysed and optimised off-line. This was performed by integrating robot simulation software with finite element analysis software. A special interface was created allowing information exchange between the two software programs. The method was used to program welding trajectories both for planar plates and for a part of an aerospace component. The trajectories were downloaded to the finite element analysis software where temperature and residual stress prediction were performed. Good agreement was found between the programmed robot trajectory, and the actual trajectory and only small adjustments were necessary. Temperature measurements were performed using both thermocouples and infrared imaging. Good agreement was also found between the results using these two methods. The method developed provides a powerful tool to construct and optimise robot trajectories and welding process parameters off-line.

IV

Acknowledgments

First, I would like thank to Professor Gunnar Bolmsjö, Lund Technical University, for his support and help during this study. I would also like to express my indebtedness to my second supervisor, Dr. Per Nylén, University of Trollhättan/Uddevalla, for his support, contributions and his enthusiasm for this project. Without his help, none of this had been possible. I would like to express my appreciation to several people at Volvo Aero Corporation. Mr. Per Henrikson, for his help and support with temperature measurements, Mr. Börje Nordin for his willingness to share his knowledge about robotised TIG welding and Techn. Lic. Daniel Berglund for helping me to discover the wonderful world of finite element analysis. I would also like to express my gratitude to the members in the VIP research group at the University of Trollhättan/ Uddevalla for all their help, especially Mr. Xavier Guterbaum for all our valuable discussions in the robot laboratory. I thank too Mr. Alastair Henry and Dr. Anita Hansbo of University of Trollhättan/Uddevalla their careful linguistic revision. Thanks also to the research team of the robotic division at Lund Technical University for all their help and assistance. The project was funded by the Foundation for Knowledge and Competence Development and EC Structural Founds. Finally, I would like to take this opportunity to express my gratitude to my parents, Sten and Ingrid, my brother Stefan and my fiancée Anna, for all their support and understanding during this time

Mikael Ericsson March 2003 Trollhättan

VI

Contents

Abstract Acknowledgments Contents

III V VII

List of figures

IX

List of tables

X

List of acronyms

X

1 Introduction 1.1 Background and motivation ............................................................... 1.2 Objectives........................................................................................... 1.3 Scope and limitations ......................................................................... 1.4 Experimental equipment .................................................................... 1.5 Outline of thesis .................................................................................

1 1 2 2 3 4

2 TIG welding theory 5 2.1 Principle of TIG welding.................................................................... 5 2.2 Process parameters in TIG welding..................................................... 7 2.3 Heat effects of welding ....................................................................... 8 2.3.1 Temperature fields.................................................................. 9 2.3.2 Residual stresses and distortion ............................................... 15 3 Modelling Techniques 3.1 General principles of off-line programming of robots ......................... 3.2 Off-line programming in the present study......................................... 3.3 General principles of finite element modelling of welding................... 3.3.1 Boundary conditions .............................................................. 3.3.2 Material properties.................................................................. 3.4 FEM-modelling in the present study .................................................. 3.4.1 Boundary conditions .............................................................. 3.4.2 Material properties.................................................................. 3.4.3 Properties for the thermal-mechanical modelling .................... 3.5 Principle of the integration between the off-line programming model and the finite element analysis model .......................................

17 17 19 20 21 21 22 23 25 27 27

VIII

4 Model validation techniques 4.1 OLP calibration.................................................................................. 4.1.1 Signature calibration ............................................................... 4.1.2 Tool calibration ...................................................................... 4.1.3 Work cell calibration .............................................................. 4.2 Temperature measurements techniques............................................... 4.2.1 Thermocouple instrumentation on plates................................ 4.2.2 Infrared imaging measurements techniques............................. 4.3 Residual stress measurements techniques ............................................ 4.4 Distortion measurements....................................................................

31 31 31 32 32 33 33 34 35 36

5 Results

37

6 Summary and conclusion

39

7 Proposals for future work

41

References

43

Included papers

47

List of figures

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6

Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16

Figure 17 Figure 18 Figure 19 Figure 20

Principle for Tungsten Inert Gas (TIG) welding........................... Schematic diagram of the TIG process with its three parameter groups .......................................................................................... Weld characteristics for two butt-welded plates with thickness t ... A typical cross section of a weld.................................................... Schematic of the welding thermal model ...................................... Temperature contour plots with different welding parameters. Upper left: welding speed 2.0 mm/s, Upper right: welding speed 3.0 mm/s. Lower left: welding current 100 A, Lower right 80 A ............................................................................................. Important temperature characteristics........................................... Example of distortion that can occur during welding [2] .............. IGRIP model of the experimental setup........................................ Aerospace component, whole part left, 1/13 of the part to the right ............................................................................................. The non-uniform mesh used in paper one. Note the higher densities along the weld path ........................................................ Shell model of aerospace component. Note the higher densities along the weld path ...................................................................... a) Cross section of a plate mounted in a welding fixture. b) Applied boundary conditions in a heat transfer simulation ........... Heat flux Gaussian distribution with 5% cut off limit .................. Specific heat for Stainless Steel 361L ............................................ Conductivity for Stainless Steel 316L. Conductivity without considering convection (a) and conductivity when weld pool convection is considered by increasing the conductivity value above the melting point (b) .......................................................... Block chart showing the integration between OLP and FEM ....... Robot pose description for a path ................................................. Input file to the FEA simulation generated in the Robot simulation program ...................................................................... Overview of a cross section from a FEA simulation showing the penetration. The color represents a temperature interval close to the melting point..........................................................................

6 7 8 8 11

13 13 16 19 20 22 22 23 25 26

26 28 28 29 29

X

Figure 21 Figure 22 Figure 23

Schematic of a plate with thermocouples together with selected measurement line for the IR camera measurements....................... 34 Principle overview of the VarioScan 3021 high resolution ............ 35 Distorted welded plate measured in a CMM Machine. Results post processed in UniGraphics...................................................... 36

List of tables

Table 1 Table 2

Boundary conditions in the heat transfer analysis in paper I.......... 24 Material properties for Stainless Steel 316L and Greek Ascaloy..... 25

List of acronyms

Acronyms

Explanation

AC

Alternating Current

CAR

Computer Aided Robotics

DC

Direct Current

FEA

Finite Element Analysis

FEM

Finite Element Method

GTAW

Gas Tungsten Arc Welding

HAZ

Heat Affected Zone

OLP

Off-Line Programming

TCP

Tool Centre Point

TIG

Tungsten Inert Gas

1 Introduction 1.1 Background and motivation Arc welding equipment was originally designed to be used manually but during the industrial evolution and through the introduction of robots in industry in the 1970s, automatic welding was developed. It is today one of the most common tasks for an industrial robot. Examples of some of the driving forces for this automation are higher productivity, higher quality demands, and an increased demand of higher flexibility. Automatic TIG (Tungsten Inert Gas) welding is, however, still rather rare since it puts high demands on equipment and on part geometry accuracy. Manual TIG–welding is, on the contrary, one of the most common welding processes in the aircraft industry. This is due to high product requirements for materials with high heat and corrosion resistance, with good fatigue properties and with low weight. Examples of materials are Inconel 718 and Greek Ascaloy, which can be successfully joined by TIG welding resulting in joints with few defects and, comparative to other welding processes, low distortion. However, any welding process induces changes in the base material and generates unwanted stresses and deformation due to the heat input. The most common way to avoid this deformation is to use fixtures to clamp the part to be welded. Unfortunately these fixtures are difficult to design, time consuming to construct and very expensive. Another method to reduce deformation is to optimise the welding sequence to allow a more uniform heat distribution into the part. An optimal welding sequence can be hard to find and requires a very skilled operator. Therefore, a simulation tool that can be used to evaluate fixture solutions and to plan welding sequences early in the product development stage would be desirable. Such a simulation tool would reduce both the number of welding experiments and the need for welding operator experience. The tool should preferably be able to simulate the welding torch path, be capable of detecting collisions between the torch and workpiece, and of optimising the welding parameters with respect to penetration and component deformation.

2

Introduction

Research in arc welding simulation can be divided into two main fields, namely robot simulation, often refereed to as CAR (Computer Aided Robotics), and thermal-mechanical modelling [4, 8]. CAR concerns simulation and programming of a robot task using a virtual model of the workcell and the part to be welded. Examples of research in this area are the integration and development of virtual sensors and the optimisation of welding sequences and torch trajectories to avoid collisions and to increase productivity [5, 6]. Thermal-mechanical modelling concerns the modelling of the influence of the process on the component. It includes the prediction of temperature histories, microstructure phase transformations, residual stresses and distortion. This thesis addresses both these areas, i.e., both CAR and thermal-mechanical simulations, through the integration of an off-line programming system with a Finite Element Analysis (FEA) system. CAR is used to simulate TIG welding torch paths and to detect collisions between the torch and workpiece. FEA is used for the prediction of temperature histories and residual stresses and the optimisation of welding parameters as regards penetration. The industrial interest in manufacturing simulation tools has increased significantly in recent years, which is why simulation has become an increasingly common tool to test and verify different approaches prior to manufacturing. A simulation tool such as described would therefore be of great benefit to the industry.

1.2 Objectives The objective of this research is to develop and validate a simulation tool for the TIG welding process. The tool shall be capable of simulating torch paths, predicting temperature histories, residual stresses, and deformation, thus making it possible to optimise welding sequences and fixture solution prior to manufacture. Of particular interest is whether or not sufficiently complex models can be developed, that can be used industrially in the design and production engineering phases.

1.3 Scope and limitations The simulation of welding is a very wide field, which incorporates several techniques and disciplines. Models have been developed for the simulation of the robot path, the arc, the liquid pool, and for the solid part. The different models involve disciplines such as plasma physics, electromagnetics, fluid mechanics, material science and production technology. The different models also impose different demands concerning time and space resolutions. Time scales can range from microseconds to minutes and length scales from micrometers up to decimetres and meters. Limitations are therefore necessary when a simulation model is to be developed. The limitations in this work are:

1.4 Experimental equipment

3

•

No model of the arc or the molten pool. Convective heat transfer within the molten pool is considered by increasing the heat conductivity when the temperature increases beyond the melting point.

•

No models of fixtures. Reaction forces from these are considered in the boundary conditions.

•

No development of material models, such as phase transformations.

•

The study is limited to the TIG welding process, although results are expected to be generic and applicable to other welding processes.

•

The study is limited to two materials, namely Greek Ascoloy and stainless steel 316L. The methodology developed is, however, not material dependent.

Focus is placed on CAR and the integration of CAR with FEA. The thermomechanical simulations have been made in collaboration with Luleå Technical University, Sweden [14]. In the validation work focus is placed on temperature measurements.

1.4 Experimental equipment Different experimental equipment and software has been used in this research. The TIG welding experiments were carried out with a robotised welding cell consisting of a six-axis robot (ABB IRB 1400) supplied by ABB Automation Technology Products AB Robotics, Västerås Sweden, linked with a torch produced by Binzel AB (thoriated tungsten electrodes) and supplied by Abicor Binzel AB, Karlskrona, Sweden. The power source is a TIG Commander 400 AC/DC produced by Migatronic AB. The robot simulations were performed using IGRIP, commercial software produced by Dassault Systemes, Suresnes Cedex, France. The thermomechanical simulations were performed using the Marc system developed MSC Software Corporation, Santa Ana, USA. The calculations were performed using an in-house developed Linux cluster consisting of ten 1.0 GHz Pentium III processors. For temperature measurements, both thermocouples (type K) and infrared imaging were used. A PC based data acquisition system was used to sample the signals from the thermocouples and write the data to disk. The infrared camera is a VarioScan 3021-ST high resolution 16 bit stirling cooled camera produced by Jenoptic GMbH, Jena, Germany.

1.5 Outline of thesis In this thesis, a method for the off-line optimisation of welding by the use of simulation is proposed. The thesis commences in chapter 2 with an introduction to

4

Introduction

the theory of TIG welding and its heat effects, such as residual stresses and distortion. Chapter 3 describes methods for robot simulation as well as modelling techniques for the prediction of temperature histories, residual stresses and distortion. In chapter 4 different methods for the validation of temperature, residual stresses and distortion are discussed. Chapter 4 is followed by the results and discussions in chapter 5, and by the conclusions in chapter 6. Finally, the thesis ends with a proposal for future research. Papers published by the author are listed in the appendix.

2 TIG welding theory

This chapter provides a brief presentation of the theory of TIG welding and its heat effects on the base material.

2.1 Principle of TIG welding The TIG welding process was invented during the Second World War due to the need of the American aircraft industry for a method of joining magnesium and aluminium. Russell Meredith [7] demonstrated the first TIG process for the welding of magnesium using a Tungsten electrode and helium gas in the late 1930´s. TIG welding or GTAW (Gas Tungsten Arc Welding which is the common name in North America) uses a non-consumable tungsten electrode protected by an inert gas. The electrode is either made of pure tungsten or tungsten, mixed with small amounts of oxides (thoriumoxide, zirconiumoxide) improving the stability of the arc and makes it easier to strike. Since the process uses a non-consumable electrode, extra filler material is usually added. The principle of the process is schematically presented in Figure 1. The electrical discharge generates a plasma arc between the electrode tip and the work piece to be welded. The arc is normally initialised with a power source with a high frequency generator, which produces a small spark that provides the initial conducting path through the air for the low voltage welding current. The frequency of this ignition pulse is large, up to several MHz. This frequency, together with a high voltage (several kV), produces strong electrical interference around the welding cell, which is a disadvantage when sensors and measuring equipment are used. The arc consists of a high-temperature conducting plasma that produces the thermal energy needed to melt the base and the filler material. The arc temperature spans between 12000 K and 15000 K above the pool surface and

6

TIG Welding Theory

the temperature of the melted surface spans from 1700 K to 2500 K, dependent on the material. Current Conductor

Shielding gas

Tungsten Electrod

Filler Metal

Gas Shield Arc

Weld Center Line

Weld Pool

Solidified Weld Metal

Figure 1: Principle of Tungsten Inert Gas (TIG) welding. Three different alternatives of current can be used namely; direct current (DC) with a positive electrode, DC with a negative electrode or alternative current (AC). AC is mainly used for the welding of aluminium and magnesium since cleaning of the oxide layer on the surface can in this way be achieved. DC with a negative electrode is used for most other materials, including thick plates of aluminium. Pulsed and non-pulsed currents can be used. A non-pulsed current is most common. The use of a pulsed current has some advantages, such as increased penetration. Depending on the thickness of the base material, type of joint and certain other factors, extra filler material might be needed. In automatic TIG welding hot or cold wire can then be used. Cold wire is fed in the front of the melted pool and hot wire fed in the back. The filler material is usually the same as the base material. An inert gas is used to sustain the arc and to protect the melted pool and the electrode from atmospheric contamination. Depending on the welding parameters and welding materials, either argon, helium or a mix of the two gases can be used.

2.2 Process parameters in TIG welding

7

Argon is commonly used in welding unalloyed, low alloyed and stainless steels. However, a mixture of argon and hydrogen or helium can be used for mechanical welding. For duplex stainless steel, it is common to mix argon with nitrogen to ensure a correct ferrite/austenite balance. Aluminium and aluminium alloys are usually welded using argon. However, the addition of helium can be used to improve the heat transfer and is therefore sometimes used for the welding of thicker parts. Argon is suitable for welding copper and its alloys, and gives excellent results for thicknesses up to 6 mm. Helium, or a mixture of helium and argon (up to 35 %), are suitable for thicknesses greater then 6 mm. Titanium requires an extremely high purity of the shielding gas, usually not less then 99.99 %. Either argon or helium can be used here. Argon is the more common shielding gas for thicknesses less than 3 mm while helium is more commonly used for thicknesses in excess of 3 mm. In stainless steel and other easily oxidised materials, applications of a root gas can be used to protect the root side of the weld from oxidation. The root gas can be a mixture of nitrogen and hydrogen, or pure argon.

2.2 Process parameters in TIG welding Three main parameter groups can be defined in the TIG welding process. The first group (group 1 in figure 2) is the group of controllable process parameters. The second group consists of sensor variables for the supervision and control of the process, and the third comprises final weld characteristics. Group one can be divided in three sub groups; those that can be varied on-line during the process (such as arc-current, torch travel speed and wire feed speed), those that are set prior to the process (for example composition and flow rate of the shielding gas) and finally the last subgroup that consists of variables that cannot be modified, such as part geometry [15]. 1. PARAMETERS: - Current - Travel speed - Arc length - Wire feed speed

Inert gas Filler material Weld pool

2. SENSOR VARIABLES: - Pool geometry - Temperature - NDT W-electrode

3. WELD CHARACTERISTICS: - Bead geometry - Indications (NDT) - Distortion

Figure 2: Schematic diagram of the TIG process with its three parameter groups.

8

TIG Welding Theory

Examples of sensor variables of group two are weld bead width and weld pool geometry. Another example is seam tracking which is commonly used in robotised welding. The last parameter group, weld characteristics, is strongly dependent on the other two parameter groups. To this category belong weld geometry, metallurgy phase composition, residual stresses and distortion. Figures 3 and 4 show a schematic and a real cross-section for two butt-welded plates. The most important geometry characteristics are: weld width at topside ( B ) , weld width at root side (C ) , weld height at topside Bh (so called reinforcement) and weld height at root side C h (usually called drop through) [17].

B

Bh

t C

Ch

Figure 3: Weld characteristics for two butt-welded plates with thickness t.

Figure 4: A typical cross section of a weld.

2.3 Heat effects of welding Heat transfer phenomena play an important role in welding. Heat effects of welding refer to temperature fields, residual stresses and distortion that occur

2.3.1 Temperature fields

9

during or after welding. Since the focus of this thesis is placed on temperature prediction, a thorough description of the temperature fields is given below. 2.3.1

Temperature fields

One objective with heat transfer analysis in welding applications is to determine the temperature fields in an object resulting from conditions imposed on its boundaries [18]. The quantity that is sought is the temperature distribution, which represents how temperature varies within positions in the object. When this distribution is known, the conduction heat flux calculated at any point in the medium or at the surface may be computed from Fourier’s law. The temperature fields during welding are highly heterogeneous and transient. The temperature of a component can vary from below zero to 3000 centigrade, i.e. the evaporation temperature of the metal. Within this range phase changes, micro structural transformations and thermal strains take place, all of which determine residual stresses and distortion. Fourier’s law of heat conduction describes the heat propagation in mechanism in the solid material. The law states that the heat flow ∂T density q  J mm 2  is proportional to the negative temperature gradient   ∂n [K mm ] by equation q = −λ

∂T ∂n

(2.1)

where λ [ J mm s K ] denotes the thermal conductivity and T [ K ] the temperature. Consider a homogenous medium expressed in one dimension x with a temperature distribution T ( x ) expressed in Cartesian coordinates with infinitesimally small control volume dx . The condition heat rate at the control area can then be expressed as q x . The condition heat rate at the opposite surface can be expressed as a Taylor series expansion neglecting higher order terms as q x + dx = q +

∂q x dx ∂x

(2.2)

Inside the control medium an energy source term (2.3) and an energy storage term (2.4) can be expressed as  E g = qdx

(2.3)

∂T Est = ρ C p dx ∂t

(2.4)

Using the law of energy conservation equations 2.1, 2.3 and 2.4 can be substituted to

10

TIG Welding Theory

 − q x −dx = ρ C p q x + qdx

∂T dx ∂t

(2.5)

Substitution from equation 2.2 and Fourier’s law the heat diffusion equation can be written in a general form, in three dimensional Cartesian coordinates as, ∂  ∂T λ (T )  ∂x  ∂x

∂T  ∂   + ∂y  λ (T ) ∂y 

 ∂  ∂T  +  λ (T ) ∂z  ∂z 

∂T   = ρ C p (T ) ∂t − Q

(2.6)

where the Cartesian coordinates x , y and z denote the welding direction, the transverse direction, and the normal direction to the weld melt surface, respectively, see figure 5. Q  W mm3  stands for internal heat generation rate   and the material properties, thermal conductivity, density, and specific heat, are denoted by λ , ρ and C p respectively. Several possibilities for initial conditions exist. The most common is: T = T0 at t = 0

A general boundary condition can be written as:

λ

∂T ∂T ∂T lx + λ ly + λ l z − q + h(T − T∞ ) = 0 ∂x ∂y ∂z

(2.7)

where h denotes surface heat loss coefficient, l x , l y and l z the direction cosines to the boundary surface. The surface temperature and environment temperature are denoted T and T∞ respectively. The heat diffusion equation (2.6) can be solved both analytically and numerically (in the latter case, the FEM is commonly used, which is further presented in section 3.2). The equation can be analytically solved assuming the following conditions [2]: •

The energy from the welding heat source is applied at a uniform rate.

•

The heat source is moving with a constant speed.

•

The cross section of the work piece is constant.

•

Constant material properties are used.

•

The end effects resulting from the initiation and termination of the arc weld are neglected (quasi-static solution).

Different analytical solutions exist depending on the plate thickness and welding positions. The plate can be assumed to be thick, thin and finite, respectively. The dimensionless τ is used to determine whether the plate is to be considered as thin

2.3.1 Temperature fields

11

or thick. In a thick plate the heat flow is considered to be three dimensional, through the plate thickness and lateral from the weld. The thin plate equation can be applied where the heat flow is essentially lateral. This means that the difference in temperature between the bottom and top surface is small in comparison to the melting temperature.

τ =h

ρ C p (Tc − T0 )

(2.8)

H net

where h denotes the plate thickness. H net is net energy input equal to

ηEI . T0 υ

stands for the initial plate temperature, Tc denotes the temperature at which the cooling rate is calculated. The plate is considered to be thin if τ is less the 0.75 and to be thick if τ is larger than 0.75. For an analytical quasi-static solution of the heat transfer model it is assumed that the material properties are independent of the temperature, that the metallurgy zones are homogenous and that the thermal model is linear in the welding direction. The solution gives the temperature in a specific point if the welding speed (υ ) , energy heat input ( E , I , η ) and the material properties ( ρ , λ , C p ) are known. This point is defined by r (2.9): r = ξ 2 + y2 + z2

(2.9)

where ξ denotes a moving coordinate (2.10),

ξ = x − υt

(2.10)

Here the origin of the moving coordinates ( ξ , y , z , se figure 5) is fixed at the centre of the heat source see figure 5. This means that the coordinates move with the heat source at the same speed. Solutions are usually derived for the thin and thick plate separately. Welding direction Y

Z X Weld Centre Line

Figure 5: Schematic of the welding thermal model.

12

TIG Welding Theory

Analytical solutions were first presented by D. Rosenthal 1935 [1, 2 and 3]. T − T0 =

 −v ( ξ + r )  e  2πλ r  2κ  q

(2.11)

for a thick plate and T − T0 =

q 2πλ d

−νξ e 2d K

 vr    2d 

0

(2.12)

for a plate considered to be thin, where κ denotes the thermal diffusivity of the base metal, T0 denotes the preheat temperature in the base metal, d stands for plate thickness and K 0 denotes the modified Bessel function of the second kind, zero order. This relationship for the temperature heat flow is not accurate close to the welding arc. Since a point or a line source is assumed for thick and thin plates respectively, singularities will occur at the sources location where the temperature tends to infinity. Figure 6 shows the influence of welding parameters on temperature. The welding speed and welding current have been varied and the temperature distribution has been solved using equation 2.12. In the upper two figures the welding speed has been varied and in the lower the welding current. It can be seen that both the welding speed and current have a strong influence on the heat distribution. Several welding defects, such as residual stresses and distortion (see section 2.3) are dependent on the heat input. If the heat input can be minimised, maintaining a full penetration, these defects will decrease. The thermal condition in, and close to, the weld is very important since it controls the metallurgical events in the weld. Interesting parameters to control are; the distribution of peak temperature in the heat affected zone (HAZ), cooling rates in the weld metal and in the HAZ, and the solidification rate of the weld metal, figure 7.

2.3.1 Temperature fields

0.03

0

400

1200 1700

800

400

800 1200 1700

0.03

13

0

400

400

−0.03

−0.03

−0.2

0.03

0

0.1

0

0.1

400

1200 1700

800

−0.2

0.03

400

0

−0.4

800 1200 1700

−0.4

0

400

400 −0.03

−0.03

−0.4

−0.2

0

0.1

−0.4

−0.2

0

0.1

Figure 6: Temperature contour plots with different welding parameters. Upper left: welding speed 2.0 mm/s, Upper right: welding speed 3.0 mm/s. Lower left: welding current 100 A, Lower right 80 A.

Temperature (K)

Peak temperature

Cooling rate

Time (s)

Figure 7: Important temperature characteristics.

14

TIG Welding Theory

The peak temperature in the weld pool is given by [2]

2π e ρ C p tY 1 1 = + T p − T0 H net Tm − T0

(2.13)

where peak temperature, base metal melting temperature, initial base temperature are denoted by T p , Tm , and T0 , respectively. The material properties, density, and specific heat are denoted by ρ , and C p . Y denotes the distance from the weld fusion boundary where the peak temperature is calculated, t states the plate thickness and e denotes the base of the natural logarithm. H net is net energy input equal to

ηEI . This equation can be used to predict peak temperature in a υ

specific point in the HAZ, the width of the HAZ, as well as the effect of preheat on the width of the HAZ. If the cooling rate in a specific point along the weld line is known, a prediction of the metallurgy in the welded area can be made. Cooling rates are important in the welding of heat-treatable steels. This is due to the formation of martensite in the welded area. In the case of carbon and low alloy steels, the temperature at which the cooling rate is calculated is not critical. Therefore, the major use of cooling rates is to calculate the preheating temperature [2]. The general cooling rate equation can be defined, using the moving coordinate ξ (2.9), which gives

∂ξ = −ν . Using the chain rule the cooling rate equation can be written as [3] ∂t

∂T ∂T = −ν ∂t ∂ξ

(2.14)

An analytical solution of the cooling rate can be defined for both a thick (2.14) and a thin plate (2.15), respectively,

R=

2πλ (Tc − T0 )3 H net

(2.14) 2

 t   (Tc − T0 )3 R = 2πλρ C p   H net 

(2.15)

where R is the cooling rate at a point at the weld centreline just at the moment when the point is cooled past the Tc temperature. Tc denotes the critical temperature for phase changes in the welded metal. The material properties,

2.3.2 Residual stresses and distortion

15

thermal conductivity, density, and specific heat are denoted by λ , ρ , and C p . H net is net energy input equal to

ηEI . υ

The solidification rate can have an important impact on the metallurgical structure, properties, and material response to heat treatment. The solidification time St of the weld metal, measured in seconds is given by

St =

LH net

(

2πλρ C p Tm − T0

)

2

(2.16)

where L is the heat of fusion. 2.3.2

Residual stresses and distortion

Residual stresses are self balanced internal stresses, which exist in the component without any external loads and can be classified as macro stresses and micro stresses [16]. The definition of macro stresses is that they are self-equilibrated in a cross section of the manufactured part. Micro stresses can be defined as stresses that are homogenous or inhomogeneous in a micro scale [16]. They are introduced in the component as results of manufacturing process such as welding and drilling, generated either on purpose or not, as the case may be. Since the welding process heats the material locally, the temperature distribution is not uniform. In the melted weld pool stresses are released and can be assumed to zero. During the solidification of the melted weld pool the metal starts to shrink and to exert stresses on the surrounding weld metal and HAZ. These stresses remain in the material after welding and result in unwanted distortion. A typical example of distortion is given in figure 8. The stress level in the solidification area is proportionately low, but the stress level in the weld area increases and can be as high as the yield limit of the base material, which can cause unwanted fractures. Stresses in a welded plate are usually divided in two directions, transverse and longitudinal to the weld. Longitudinal residual stresses can arise from different causes. The most common cause is the longitudinal contraction of the weld as it cools down. Another cause is superimposed by opposing transformation processes. Transverse residual stresses are generated by the transverse contraction of the weld during the cooling phase. It can also be generated indirectly due to the longitudinal contraction [10]. Three different types of residual stress induced distortion can be found in manufactured structures, figure 8. Longitudinal and transverse shrinkage can cause in plane distortion of the workpiece. Plane or axisymmetrical angular shrinkage can cause distortion perpendicular to the plane of the welded component. Another distortion is bending due to grids with longitudinal and transverse welds [2].

16

TIG Welding Theory

Figure 8: Example of distortion that can occur during welding [2]

3 Modelling techniques

This chapter describes the modelling techniques used in this thesis. A presentation of the off-line programming of robots is provided, followed by a presentation of temperature and residual stress prediction by the use of the Finite Element Method.

3.1 General principles of the off-line programming of robots Computer Aided Robotics is a graphical tool for manufacturing simulation that can be used in several applications such as the off-line programming of robots, teleoperation of robots and simulation of general kinematic systems. The off-line programming of robots using a graphical simulation tool was first demonstrated in the beginning of the 1980. Developments in computer technology have significantly improved this technology. The simulation of the production of a component makes it possible to test, verify and optimise robot motions and to design fixtures and automation equipment before the real production process begins [25, 26]. Accessibility, collisions and timing can be verified before expensive machine tools and robots are purchased. Further, production accuracy can be improved by using off-line programming of robots, thus avoiding the complexity of manually programming a robot in three dimensions with a high degree of accuracy. The most common method to manually program a robot is still that an operator jogs the robot arm to each coordinate pose in space, a procedure which is error prone and depends heavily on the experience of the operator. Threedimensional computer graphics tool for production planning makes it possible to achieve high accuracy for complex parts.

18

TIG Welding Theory

The technique of off-line programming is commonly used in industry. Several commercial software packages exist such as IGRIP, RobCad and Grasp. All of these systems have principally the same work order [8, 25]: 1. 2. 3. 4. 5. 6.

Modelling of the work cell Work cell calibration Programming of robot and other optional work cell equipment Down loading of the program to the control system Additional robot programming Test running

In the first step the geometrical model of the work-piece, together with a geometric and a kinematic model of the workcell including the most important parts such as fixtures, robots and positioners are created. The geometrical model can either be created in a CAD/CAM software and imported to the robot simulation software or directly created in the robot simulation software. Most of the robots on the market have been predefined in the robot simulation softwares and can be retrieved from a library. If a new design of a robot or a new kinematic device is to be used, a kinematic model has to be created. The different parts of the kinematic devices, such as robot arms, have then to be modelled. These devices are usually successively created from the base part to the tool tip by connecting the parts in joints and describing movement patterns and boundary conditions for each joint. The second step is the calibration of the workcell. It can include several sub-steps, such as TCP- (Tool Centre Point), workpiece- and signature calibration. A tool calibration is usually performed by rotating the real robot TCP around a sharp fixed point. Each new position is then stored and uploaded into the robot simulation program and a “best fit” is calculated using statistical regression. Similarly the position of the workpiece is calibrated by moving the robot to identified positions. To find errors in the geometrical model of the robot, an arm signature calibration can be used. This calibration finds the deviations between the physical and virtual model in the lengths between the robot joints and in the zero poses for the joints. Corrections to the virtual model are then made. A more detailed description of calibration is given in the OLP modelling validation section in chapter 4. In the third step the programs for all the different devices are written. Three categories of commands are commonly used. The first category includes commands for the visualisation of the simulation. Examples of such commands are graphical commands such as viewpoint rotation and zooming towards or outwards from an object. These commands are not included in the robot code and consequently not downloaded to the robot. The second category concerns commands that are used in the simulation but also included in the robot code. Movement commands for the robot are typical

3.2 Off-line programming in the present study

19

examples of this category. For a welding application, this category will also include commands such as ignition and termination of the arc. The last category concerns commands that are not used in the simulation but which are directly downloaded to the robot. Examples of commands in this category are I/O´s, such as a gas protection set to on or off in welding. Step four is to translate the simulation program to the specific robot code and to download it to the real robot control system. Step five concerns additional robot programming and adjustments that have to be performed on-line at the robot. In step 6 a test run of the program is performed. If the test result is satisfactory the production can be executed. Steps 1-6 might however have to be repeated iteratively until the required accuracy is achieved. Accuracy does not only depend on how well steps 1-6 have been performed but also on how well the simulation control system emulates the physical control system. A special module called RRS (realistic robot simulation) [19] can be used to increase the agreement between the physical and the virtual control system. Through RRS the original control system software for motion interpolation and transformation is integrated in the CAR system.

3.2 Off-line programming in the present study The method to program robots off-line described above has been used in this study. An in house welding cell, see figure 9, was modelled in IGRIP (Interactive Graphics Robot Instruction Program, Deneb Robotics). Figure 9 shows a snapshot of the experimental setup.

Figure 9: IGRIP model of the experimental setup.

20

TIG Welding Theory

Both plane plates and a component with complex geometry were modelled and used as test cases. A tool calibration and a workpiece calibration were both performed. A high level graphical simulation language in IGRIP, GSL, was used for programming all devices in the cell. The part was a section of an aerospace part, a turbine component from the V2500 engine produced by Volvo Aero Corporation, figure 10, left. 1/13 was cut out of the real part, which originally consisted of an inner and an outer ring and 13 vanes, figure 10, right. All components were created in the UniGraphics CAD/CAM system and imported using both a direct translator and the neutral interface IGES. No RRS model was used. Outer Ring

a) Vane

b)

Vane

Outer Ring Inner Ring

Inner Ring

Figure 10: Aerospace component, whole part left, 1 13 of the part right.

3.3 General principles of finite element modelling of welding Numerical methods have been used since the beginning of the 1970’s to simulate welding processes. The focus has been to predict thermal histories, residual stresses and distortion before the real welding process begins. FEA simulations have been the most common numerical method and many papers have been presented [12, 14, 27, 28]. Large complex simulation models of three-dimensional components are, however, still rare, mainly due to the lack of computational power. One reason is that to be able to compute the temperature and residual stress fields in the affected zone a very fine discretization of the space variable is required. Another complexity concerns the heat transfer between the electrode and the part to be welded [20]. This is a complex phenomenon with several interaction effects. The phenomenon can be divided into three different groups, the plasma arc, the weld pool and the solid material. Modelling the plasma arc is complicated since chemical reactions, ionizations, and vaporisations of both electrodes and the surface of the weld pool have to be considered. Developing a model of the weld pool is also complicated since, driven by various forces such as surface tension forces, electromagnetic and buoyancy forces the melted material undergoes vigorous circulation. The weld pool surface is also strongly influenced by a drag force that

3.3.1 Boundary conditions

21

depresses the surface and induces a surface flow. The solidification process in the liquid–solid boundary region is also complicated to model. Different assumptions and simplifications have therefore to be considered when building an FEA model of welding of a complex part. Examples of areas that have to be considered are part geometry simplifications, the type of material models to be used, load conditions, heat transfer and other boundary conditions and numerical strategy [14]. The most important considerations that have to be made are described in the following section. 3.3.1

Boundary conditions

All FEA problems are defined in terms of initial and boundary conditions. A typical type of initial condition for a welding application is the initial temperature that, in most cases, is set to room temperature. Examples of the most important boundary conditions are fixture forces, and heat transfer coefficients between the part and its surroundings. Since the heat transfer between the electrode and the part to be welded is usually too complex to be integrated in the same model, an ad hoc heat source with parameters that are adjusted. Different types of heat sources have with this purpose in mind, been suggested [9, 10]. Three main methods are usually used. The first method is to apply an energy source within the part to be welded. The size and energy density are then adjusted to retrieve a fusion zone with good agreement with the real weld. The second method is to use a surface distribution to simulate the arc. Here the energy source heat flux depends on the distance from the centre of the arc. A very common distribution used is the Gaussian method. The last method is to use a double ellipsoid heat source, which was first recommended by Goldak [9]. This method combines the first two types since it includes a surface distribution as well as a heat source within the material. Although this method is the most realistic it needs more parameters to be fitted with experiments. 3.3.2

Material properties

The material properties that have to be included in the proposed simplified model when temperature simulations are to be performed are specific heat, heat conductivity, density, liquidus and solidus temperature. The conductivity is usually temperature dependent. Weld pool convection is a complex phenomenon that is difficult to simulate. This convection is therefore simulated by multiplying the thermal conductivity with a certain factor when the temperature exceeds the liquidus temperature. Different factors have been proposed in the literature. Common values are eight and ten. The specific heat for most welding materials are strongly temperature dependent. The value increases significantly during fusion due to the latent heat of transformation.

22

TIG Welding Theory

3.4 FEM-modelling in the present study The welding paths, including initial weld velocities, were exported from the CAR model to the finite element software where predictions of temperature histories, residual stresses and fixture reaction forces were performed. The same CAD/CAM model as in the CAR model was imported to and meshed with the FEA software. The commercial FEA program MARC, from MSC Software, was used. In paper I a model with solid elements was created. Since this type of model is computationally very expensive, a small part of the component had to be selected for modelling. The model was divided by a non-uniform mesh with higher densities close to the weld path (where the highest temperature gradients were assumed to occur), figure 11. Eight-node brick elements were used. In paper III a shell model of the whole section of the aerospace part was created, see figure 12.

weld path Figure 11: The non-uniform mesh used in paper one. Note the higher densities along the weld path

Weld path

Flange

Figure 12: Shell model of aerospace component. Note the higher densities along the weld path

3.4.1 Boundary conditions

3.4.1

23

Boundary conditions

Different boundary types have been used. The boundary conditions used in paper I are given in table 1. Since the model had to be restricted to a subsection of the part, a “metal-metal” boundary condition was introduced which models the heat conduction through these interfaces, table 1. Different heat transfer coefficients were used for the surfaces with natural and forced convection i.e. forced convection on surfaces where root gas (see section 2.1) was applied. A typical set up for a plate is illustrated in figure 13 a. The applied boundary conditions for the plate are shown in figure 13 b. A “metal-meal” convection boundary condition which models the clamping of the plate to the fixture is used in group 1. Natural convection is used for group 2 and forced convection is used for group 3. The heat input from the arc, group 4 simulates the heat source. Fixture

Fixture

Weld gun

Air

a)

Arc

Air Plate

Air Air

Weld pool

Shielding gas 1 b)

2

2

4

Plate

2

1

Weld pool 1

3

2

1

Figure 13: a) Cross section of a plane plate mounted in a welding fixture. b) Applied boundary condition in a heat transfer simulation. Natural convection was only used as heat transfer boundary condition between the part and the surrounding environment in paper III. The flanges on the inner- and outer- rings were assumed to be clamped since no fixture was used (the inner- and outer ring were welded on a steel plate). This was simulated by locking the FEM elements in all directions.

24

TIG Welding Theory

Table 1: Boundary conditions in the heat transfer analysis in paper I Type of condition

Interface

Face film

Metal – Metal

Face film

Metal – Air

Face film

Metal – Gas (Argon as root gas)

(

Value W

mm 2 K

)

Corresponding number in figure 11b

1000 ⋅ 10−6

1

20 ⋅ 10−6

2

200 ⋅ 10−6

3

As a heat source, a Gaussian surface distribution was used in all of the simulations. This distribution was selected since it requires fewer parameters to be calibrated and since the plates that were welded could be considered as to be thin. User subroutines had to be developed to simulate the moving heat source. The heat flux was expressed according to [10] q = q0 ⋅ e q0 = q=

−α q r 2

η EI α q

π η EI α q π

e

(3.1) −α q r 2

where q denotes the heat transferred to the workpiece, E the voltage, I the current, η the efficiency factor, α q the concentration factor, and r the radial distance from the centre of the heat source. Figure 14 shows a typical Gaussian heat flux distribution with a 5 % cut off limit. This means that the distribution is truncated when the heat flux reaches five percent of the maximum heat flux permitted i.e. q min = 0.05 ⋅ q max . This truncation was proposed by Radaj [10] and was used in both paper I and paper III.

2

Heat flux (W/mm )

3.4.2 Material properties

25

25 20 15 10 5 −5

0 5 Radial Position (mm)

Figure 14: Heat flux Gaussian distribution with 5% cut off limit. 3.4.2

Material properties

Two stainless steels, namely Greek Ascaloy and SS316L, have been used. Temperature dependent properties were used for thermal conductivity and specific heat. The properties were taken from [11, 12, 13] and are given in table 2. Table 2: Material properties for Stainless Steel 316L and Greek Ascaloy Nomenclature

Symbol

SS 316L

Greek Ascaloy

Density

ρ

7.3 10-6 kg mm -3

7.3 10-6 kg mm -3

Latent heat of fusion

+H

2.47 10-5 J/kg

2.47 10-5 J/kg

Solidus temperature

Tsol

1673 K

1673 K

Liquidus temperature

Tliq

1523 K

1523 K

Heat Capacity

Cp

See chapter 3.4.2

Thermal conductivity

λ

See chapter 3.4.2

26

TIG Welding Theory

The specific heat for Greek Ascaloy and 316L are strongly temperature dependent. The values used for 316L are presented in figure 12 [13].

700 650

o

C (J/(kg C))

750

p

600 550 500 450 400

500

1000

1500

2000

2500

3000

o

Temperature ( C)

Figure 15: Specific heat for Stainless Steel 316L

o

λ (W/(mm C))

Simulations were performed with and without the consideration of weld pool convection. Figure 10 presents conductivity values used for 316L [13]

0.03 0.02 0.01 0

500

1000

1500

2000

2500

3000

2500

3000

o

λ (W/(mm oC))

Temperature ( C) 0.3 0.2 0.1 0

500

1000

1500

2000 o

Temperature ( C)

Figure 16: Conductivity for Stainless Steel 316L. Conductivity without considering convection (a) and conductivity when weld pool convection is considered by increasing the conductivity value above the melting point (b).

3.4.3 Properties for the thermal-mechanical modelling

27

The fusion interval selected in this study is in accordance with analyses conducted by Toselo et al. [12]. 3.4.3

Properties for the thermal-mechanical modelling

The Greek Ascaloy’s initial microstructure consists of a mixture of ferrite and pearlite. In the numerical model in paper III the ferrite/pearlite to austenite transformation was assumed to occur only if the highest temperature experienced by the material was greater then a limit temperature, see paper III for further details. A thermal-elastoplastic model based on von Mises’s theory was used [14]. It was assumed that no creep strains occur during welding since the material is exposed to a high temperature for a very short period of time. The hardening behaviour of the material was assumed to be isotropic and piecewise linear. Transformation plasticity was not accounted for in the model. The principles that underpin the thermal-mechanical modelling are further described in paper III.

3.5 Principle of the integration between the off-line programming model and the finite element analysis model Since two different softwares were used in the Off-line programming and in the FEA work, an interface between the softwares had to be developed. Figure 17 shows a block chart of the work principle. The same part geometry was used in both softwares. The principle is that the part to be manufactured is created in a CAD/CAM software and then, using either a direct translator or a neutral interface such as IGES or Step, imported to the robot simulation program. Here the engineer plans the production, following the steps described in section 3.1 above. A robot path is generated which includes the welding parameters to be used. The welding program is then exported to the FEA program where the thermal history, residual stresses and distortion are predicted. An optimisation can also be performed to reach a full penetration weld with minimised distortion (further described beneath). If such an optimisation is performed a new weld parameter is generated by adjusting the robot speed. This new speed, together with the simulation program for the robot motion, is then translated to a complete robot code. The robot code is finally downloaded to the robot control system and a test weld can be performed.

28

TIG Welding Theory

CAD/ CAM

Robot simulation Simulation program for the robot motion

Welding path

Translator

wv

Complete robot code

Geometry e.g. IGES

FEA

Weld velocity (wv)

Thermal history Residual stresses

IRB Controller Full penentration weld with low distortion

Figure 17: Block chart showing the integration between OLP and FEM. The information exchange between the different softwares is based on the method of adding attributes to a pose, the same principle as in the ABB operative system S4. Examples of arguments are robot speed and welding data, such as welding current and welding speed. Figure 18 shows three robot poses used in a welding application. When the robot passes a pose it will use the arguments belonging to the next pose, for example when the robot passes pose P2 towards P3 the welding speed is increased to 3.0 mm/s. P1 Vw=2.0 mm/s I=100 A

P2 Vw=2.5 mm/s I=100 A

P3 Vw=3.0 mm/s I=100 A

Figure 18: Robot pose description for a path. The information exchange between the softwares is based on text files using pose coordinates (right-handed Cartesian coordinate system [23]) together with the attribute’s welding speed and welding current. Figure 7 shows an example of an input file to the FEA simulation generated in the OLP software. The columns denote x, y and –z coordinates, welding speed and welding current respectively.

3.5 Principle of the integration between the off-line programming model and the finite element analysis model 29

-75.0 -45.1 0.0 45.5 75.0

0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0

2.5 2.5 3.0 3.5 2.5

100.0 100.0 100.0 80.0 100.0

Figure 19: Input file to the FEA simulation generated by the Robot simulation program If an optimization is to be performed each node in the finite element mesh along the welding path is considered as a welding pose. During the calculation, a text file is generated with node numbers and corresponding node temperatures for each time step. Figure 8 shows a typical temperature cross section of a welded plate. The nodes along the weld path at the top and bottom surfaces are marked with dots.

Figure 20: Overview of a cross section from an FEA simulation showing the penetration. The colour represents a temperature interval close to the melting point. A stand-alone Matlab program was constructed that reads this file and suggests a new weld speed. This weld speed is then used in a new input file for a new simulation. The optimisation algorithm used is presented in equation 3.2 where the input welding speed is denoted by S0 . Here λ is a relaxation parameter, Tmelt the liquidus temperature and Tmax the maximum temperature at each node. Tmaxi − Tmelt   nsi = si  1 + λ  Tmelt  

(3.2)

The weld speed is iteratively adjusted by the program until the temperature on the root side is sufficiently close to a target temperature. This target temperature is usually set to the liquidus temperature. When an optimal velocity vector i.e. the velocity vector that maximises the speed while keeping full penetration is found, the velocity vector is exported to the weld program.

30

TIG Welding Theory

4 Model validation techniques

This chapter describes validation techniques for the off-line programming of robots, for temperature predictions and for residual stress predictions. Since a central part of off-line programming is calibration, a more extensive description of this is presented first.

4.1 OLP calibration Several processes of calibration have to be performed to increase the accuracy in a CAR application. A CAR model consists of several different components that have to be calibrated, such as the robot, fixtures, manipulators and external positioning equipment, see chapter 3.1. All these components have to be modelled with the required accuracy and positioned according to their locations in the real work cell. The calibration of the cell can be divided into three different groups, namely signature calibration, tool calibration and work cell calibration [8]. In this thesis tool calibrations and work cell calibrations have been performed. 4.1.1

Signature calibration

The purpose of signature calibration is to increase the accuracy in the robot arm’s kinematic chain, both for the real robot and for the modelled version. The signature calibration can be divided into three different levels, namely joint level calibration, calibration of the robot kinematic and non-kinematic parameter calibration. The selected level of calibration depends on the type of robot and the process. In a welding application with a low weight robot and, comparatively to other processes, low robot speed, the joint level calibration is the most important signature calibration method. In the joint level calibration the joint values of the physical robot are compared with the corresponding values for the robot model.

Model Validation Techniques

32

A common way to perform this calibration is to rotate the robot arm around a measuring stick located at several positions in the work cell. The tool tip is rotated around the stick with different joint configurations and the joint values stored in a robot program. The model and the real robot values are then compared. 4.1.2

Tool calibration

Probably the most important part of the calibration is the tool calibration. When such a calibration has been performed the robot arm, together with the tool tip, can be used as a measuring tool with a high degree of accuracy. The tool calibration is usually performed using a measuring stick with a sharp point that is positioned in the work cell. The tool tip of the robot is moved towards this point in different directions. When the tool tip makes contact with the edge of the stick the position is stored in a robot program. It is important to move as many joints as possible during this positioning process. For each positioning at least five locations have to be recorded to achieve the required accuracy. The external axis in the same kinematic change such as servo track or position equipment must not be moved. They have to be calibrated separately. In a tool calibration for a welding application, the rotation of the tool is of critical importance since penetration and weld quality are strongly dependent on the angle between the electrode and the object to be welded. A second tool point calibration is therefore usually performed using a long tool-tip in the welding direction. This type of calibration can also be used to calculate the orientation of the tool. An alternative to using the robot to perform the tool calibration is to use external measuring equipment such as a theodolite or a laser interferometer. Most of the common CAR softwares have a pre-defined function to calculate the tool tip position based on this calibration. In IGRIP, this operation is performed using statistical regression. 4.1.3

Work cell calibration

The position for each object in the work cell has to be determined in the virtual world. This can be achieved using the robot arm or by using external measuring equipment, such as measuring tape or a theodolite system. Objects that require less position accuracy, such as walls that are not critical for collisions with a kinematic device, are located most easily by using a measuring tape. Objects that need high position accuracy, i.e. those that are critical for collision or in a need of precise positioning, such as weld paths, are usually measured using the calibrated robot arm. A work cell calibration is made by selecting critical points in the work cell. Points on the work-piece are typical examples. Using a calibrated tool, the robot is moved to these points. The co-ordinates of each point are stored in a program und uploaded in the CAR software. In the CAR model the same points have been

4.2 Temperature measurement techniques

33

identified. By comparing measured and modelled co-ordinates, the software calculates a best fit of the position of the parts using statistical regression.

4.2 Temperature measurement techniques Temperature can be measured with a wide range of sensors. All of them measure the temperature by sensing a change in a physical characteristic. The most common methods are thermocouple, resistance temperature devices (RTD’s and thermistors), infrared radiators, bimetallic devices, liquid expansion devices and change of state devices [21]. In this work both thermal couple- and infrared imaging measurements have been used to measure thermal time histories on plates and on a complex shaped part. The main purpose of the thermocouple measurements was to obtain reference data by which the infrared imaging measurements could be calibrated [21]. 4.2.1

Thermocouple instrumentation on plates

Thermocouple is the most common method used to measure temperature. This is due to the fact that they are cheap, interchangeable and can measure a wide range of temperatures. A thermocouple consists of two wires, of different metals, that are joined at one end. A change in the temperature at the connection of the two wires will induce a change in the electromotive force (emf) between the other ends. As the temperature changes, the emf will change. Often the thermocouple wires are located inside a metal or ceramic shield that protects it. The most commonly used thermocouple type is type K. It has one wire of nickel-chromium and one of nickel-aluminium. The contact point of the thermocouple is spot welded on the plate at the desired position where the temperature history is to be recorded [22]. Thermocouples of type K with a wire diameter of 0.11 mm have been used in all experiments in this work on the plates. Six thermocouples were positioned perpendicular to the weld seam. The first gauge was mounted as closely as possible to the melted zone at a distance of 4 mm from the centre of the weld. The remaining gauges were located at 4.5, 5, 6, 7 and 8 mm from the weld centre line, figure 9. A PC based data acquisition system was used to sample the signals from the thermocouples and to monitor the signals on a screen. The measurement data was simultaneously written to disk. The complete measurement system was calibrated in the temperature range 0 – 1350 °C

Model Validation Techniques

34

Welding direction

Line scan

Figure 21: Schematic diagram of a plate with thermocouples together with selected measurement line for the IR camera measurements. 4.2.2

Infrared imaging measurement techniques

The infrared (IR) temperature sensor technique is a non-contact measuring method. It measures the temperature by recording the IR energy emitted by the object. As the temperature increases in the object the amount of infrared radiation also increases. Different materials radiate different amounts of IR energy at the same temperature. This efficiency factor is called the emissivity, which is defined as the fraction of radiation emitted by an object as compared to the radiation emitted by a perfect radiator, called the blackbody, at the same temperature. The emissivity may vary from close to 0 (highly reflected mirror) to almost 1 (for a blackbody). The problem with the emissivity is that it can vary with wavelength, component curvature, component surface roughness, viewing angle, and due to surface film effects. An accurate temperature can’t be measured if the object’s emissivity is unknown. The infrared camera used in this work is a VarioScan 3021 high resolution 16 bit Stirling cooled camera produced by Jenoptic GMbH. The camera has two scanning mirrors to image an object on a point detector of MCT type (HgCdTe), see figure 22. The camera operates in the wavelength range 8-12 µm and has an image resolution on 360(h) × 240(v) pixels.

4.3 Residual tress measurements techniques

35

Vertical scanner Lens

Horizontal scanner Detector

Figure 22: Principle overview of the VarioScan 3021 high resolution camera. Temperature measurements have been performed in full frame mode (1 Hz) and in line scan mode (270 Hz). The temperature measurements using the line scan mode were performed in combination with thermocouple measurements, see figure 21. The selected measuring line was then scanned continuously at a rate of 270 lines/s. The position of each thermocouple was registered after welding using a microscope. These positions were used to make comparisons with the IR recordings where the corresponding image pixels were selected. Different techniques of surface treatments were evaluated in order to overcome the problem with surface emissivity variations due to oxidation. Soot from an acetylene flame was found to give a high temperature resistant black surface with a constant emissivity value [24]. Using this technique, reliable measurements could be performed on the complete part, with the exception of the region where fusion had occurred.

4.3 Residual stress measurements techniques After welding, residual stresses can be measured. Both destructive and nondestructive measurement techniques exist. The techniques can be divided into three main groups namely stress-relaxation, X-ray or neutron diffraction and cracking methods. Tentative measurements of residual stresses using neutron diffraction have been performed in this work. However no results have, as yet, been published.

36

Model Validation Techniques

The stress-relaxation method is a destructive method. It can be divided in two subgroups. The first group is based on mechanical or electrical strain gauge measurements. The second group uses no electrical or mechanical strain gauges. The residual stresses are, instead, measured by estimation of the elastic strain release. By cutting the test object in several pieces or by drilling and removing a piece of the part, the residual stresses can be relaxed and measured. The stress relaxation is the most common method used since reliable, quantitative data can be obtained. The x-ray and neutron diffraction methods are very similar. Both methods measure crystal lattice parameters in the welded material and produce interference phenomena that are related to the interplanar spacing of the lattice. The residual stresses are then determined from the change in the interplaner spacing via Bragg’s law and Hooke’s law and compared to the stress-free state. The difference between the methods is that neutron diffraction uses neutrons scattered by a nuclear power source whilst X-rays are used to scatter the electrons in the latter method. The neutron diffraction method provides deeper penetration depths, approximately 30 mm in steel as compared to X-ray penetration depths of about 10 µm. The cracking residual stress measurement method determines residual stresses by studying the cracks in the melted zone. The cracks may have been introduced by hydrogen or stress corrosion. The method is particularly useful for analyses of components with complex stress distributions.

4.4 Distortion measurements Distortion of a welded component can be measured using common simple length and angular measuring techniques. Depending on the accuracy desired measuring tapes, Co-ordinate Measuring Machines (CMM) or other more advanced techniques, such as laser- and vision- based systems can be used. Tentative measurements of distortion using CMM have been performed in this work. However, no results have, as yet, been published. Figure 15 shows an example of a typical distorted plate after welding. The surface has been measured using a CMM machine and post processed in UniGraphics.

Figure 23: Distorted welded plate measured using a CMM machine. Results post processed in UniGraphics.

5 Results In paper I, a CAR software package was used to simulate welding operations and to program robot motions off-line for plates as well as for an aerospace component. The results showed a high degree of accuracy and very little fine-tuning after calibrations had to be made. The method seems to be a powerful tool, specifically in small batch production such as within the aerospace industry. FEA was used in the same paper to predict temperature time histories. The peak temperature was shown to be strongly dependent on the distance from the weld centre line. Good agreements between predicted and measured temperatures, both in peak temperatures and in the cooling histories, were found. The overall conclusion from the simulations was that the model predicted the thermal cycle very well. In paper III, residual stress distributions were predicted and evaluated along three different sampling lines. The stresses were recorded after 200 s from the start time of welding. The stresses were analysed longitudinally and perpendicularly to the weld seam. The longitudinal stress level and distribution was discovered to be very similar along all three sampling lines. The stress components perpendicular to the weld direction were, however, discovered to have significant differences. The reason for these differences was most probably the varying component stiffness along the weld seam. An integration between the CAR software and FEA software was also constructed. Using this interface, the optimization of the welding parameters and sequences could be performed. It was shown that penetration control could be achieved by maximizing the weld velocity while maintaining full penetration. Validations of the FEA simulations were performed by using both thermocouples and IR camera measurements on both plates of Greek Ascoloy and Stainless Steel 316L. An Acetylene/Oxygen soot deposition method was evaluated in paper II, making the IR measurements emissivity independent. Good agreement between IR and T/C temperature measurements was found in the same paper. It was concluded that IR imaging is a useful non-contact method to measure temperatures on complex shaped parts.

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Results

Several developments of the FEA model are possible. One simplification in the present model is that the tack-welding (performed before the main weld) was not considered. This tack welding will most probably affect the stress level. Including transformation plasticity in the material model will also change the stress state. The present model can however, provide a powerful tool to qualitatively evaluate different weld parameters and fixture designs off-line.

6 Summary and conclusion In this thesis a method and a simulation tool by which robot trajectories can be defined and thermal, residual stress distributions can be predicted on parts with complex shapes have been developed. The method was evaluated on a piece of an aerospace component where robot weld paths were defined off-line and automatically downloaded to a Finite Element Model, where temperatures and residual stress distributions were predicted. The temperature predictions were compared with experimental measurements using both thermocouple and infrared emission measurements and good agreements were found. No residual stress distributions have as yet been validated but measurements using neutron spectroscopy have been performed which are to be compared with corresponding predictions. The method described provides a powerful means to construct and optimise torch trajectories and process parameters off-line. Using this system, thermal histories and most probably residual stresses can be predicted on complex shaped parts and in this way resulting changes in the microstructure and mechanical properties can be estimated. Using the developed interface between the Off-line programming and the Finite Element software in combination with the developed optimization, algorithm penetration control can be achieved. Using this method productivity can be maximised whilst still maintaining full penetration.

40

Summary and conclusion

7 Proposals for future work Several extensions of the modelling work described in this thesis are possible. To extend the work to include filler wire, pulsed current, as well as considering tackwelding would be valuable. To include transformation plasticity in the material model would also be of interest. The predicted residual stress distributions have not been validated. Measurements on the Aerospace component have recently been performed at Studsvik Neutron Research Laboratory, Uppsala University. A comparison between measured and predicted distributions is to be performed. To develop a good validation method for distortion simulations would be of interest. Using such a method the optimisation of deformation patterns could be performed. Of specific interest is to evaluate the possibility to use forced cooling during welding and thereby affecting changes in the residual stress-state. Using the developed simulation tool specific cooling profiles which minimise distortion could then be designed.

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Proposals for future work

References

1

K. Easterling, “Introduction to the Physical Metallurgy of Welding”, second edition, Oxford: Butterworth Heinenmann Ltd., 1992, ISBN 0-7506-0394-1

2

L.P. Connon, “Welding handbook, Welding technology”, Eight edition, Miami: American Welding Society, pp 67-87, 218-264, 1991, ISBN 087171-281-4

3

D. LeRoy Olsen, T. A. Siewert, S. Liu, G. R. Edwards, “ASM handbook, Volume 6, Welding, Brazing and Soldering”, pp 7-24, Library of Congress Cataloging-in-Publication Data, 1997, ISBN 0-87170-382-3

4

M. Olsson, “Simulation and Execution of Autonomous Robot Systems”, PhD thesis, Department of Mechanical Engineering, Lund University, Sweden, pp 7-86, 2002,

5

P. Cederberg, M. Olsson, G. Bolmsjö, "Virtual Triangulation Sensor Development, Behaviour Simulation and CAR Integration Applied to Robotic Arc-Welding", Journal of Intelligent and Robotic System, 34(4), pp. 365-379, 2002

6

Y. Ting, W. I. Lei, H.C. Jar, "A path planning algorithm for industrial robots", Computers & Industrial Engineering , 42, pp. 299-308, 2002

7

R. Meredith, U.S. Patent 2,274,631

8

G. Bolmsjö, M. Olsson, K. Brink, "Off-line programming of GMAW robotic systems - a case study", Int J. for joining of Materials, 9, pp. 86-93, 1997

9

J. Goldak, A. Chakravarti, M. Bibby, "A new finite element model for welding heat sources", Metallurgical Transactions, 15B, pp. 299-305, 1984

44

References

10

D. Radaj, "Heat effects off welding", Berlin: Springer Verlag , pp. 18-67, 1965

11

R.T.C Choo, J. Szekely, R. C. Westhoff, "On the calculation of the free surface temperature of gas-tungsten-arc weld pools from the first principles Part II: Modelling the weld pool and comparison with experiments", Metallurgical transaction B, 23B, pp. 376 - 384, 1992

12

Toselo, F. X. Tissot, M. Barras, "Modelling of the weld behaviour for the control of GTA process by computer aided welding ", Matehematical Modelling of Weld Phenomena 4, pp 80-103, 1997

13

S. K. Choong, "Thermophysical properties of stainless steel", Argonne national laboratory, Illinois, 1975

14

D. Berglund, "Simulation of welding and stress relief heat treating in development of aerospace components", Licentiate thesis, Department of Mechanical Engineering, Luleå University of Technology, Sweden, pp 1-19, 2001

15 P. Sicard, M. Levine, "An approach to an expert robot welding system", IEEE Transactions on System, Man and Cybernetics, 18, pp. 204-222, April, 1988 16 R. Lin, "On residual Stresses and Fatigue of Laser Hardened Steels", PhD thesis, Department of Mechanical Engineering, Lidköpings University, Sweden pp. 62-83, 1992 17 P. Nylen, X. Guterbaum, P. Jonsson, B. Nordin, L. Pejeryd, "Relationship between arc welding parameters asn weld bead geometry in pulsed and nonpules TIG welded IN718", Trends in weding research, pp 408-413, 2002 18 F. P. Incropera, D. P. DeWitt, "Fundamentals of heat and mass transfer, Fourth edition", Fourth edition, New York: John Wiley & Sons, pp. 52-55, 1996, ISBN 0-471-30460-3 19 R. Bernhardt, G. Schreck, C. Willnow, "Realistic robot simulation", Computing and control engineering journal, pp. 174-176, 1995 20 T. Zacharia, J. M. Vitek, J. A. Goldakt, T. A. DebRoy, M. Rappaz, H. K. D. H. Bhadeshia, "Modeling of fundamental phenomena in welds", Modelling Simulation Material, Sci. Eng., 3, pp. 265-288, 1995 21 A. S. Morris, “Principles of Measurement and Instrumentation”, New Jersey: Prentice-Hall, pp 235-267, 1993

Reference

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22 W. Bolton, “Measurement and Instrumentation systems”, Butterworth Heinemann, pp 346-348, 1996 23 American National Standard, "For Industrial Robots and Robot System, Point-to-Point and Static Performance Characteristics - Evaluation" ANSI/RIA R15.05-2-1992 (R1999) 24 "Table of Various Surfaces", www.mikroninst.com, Micron Instrument Company, USA 25 Y. F. Yong, J. A. Gleave, J. L. Green, and M. C. Bonne, “Off-line programming of robots, Handbook of Industrial Robotics”, New York, John Wiley & Sons, 1985 26 G. C. Carvalho, M. L. Siqueira, S. C. Absi-Alfaro, “ Off-line programming of flexible welding manufacturing cells” Journals of materials processing technology 78, pp 24-28, 1998 27 Y. Ueda, T. Yamakawa, “Analysis of thermal elastic-plastic stress and strain during welding by finite element method”, Trans JWRI, Vol 2, pp 90-100 1971 28 H. D. Hibbit, P. Marcal, “A numerical thermo-mechanical model for the welding and subsequent loading of a fabricated structure”, Comp. & Struct, Vol 3, pp 1145-1174, 1973

Included Papers

Paper I M. Ericsson, P. Nylen, and G. Bolmsjo,"Three-Dimensional Simulation of Robot Path and Heat Transfer of a TIG-Welded Part with Complex Geometry." In Proceedings of the 11th International Conference on Computer Technology in Welding, pp 309-316, December 6-7, 2001, Colombus, Ohio, USA. Also published as SME Technical Paper AD02-292 (Dearborn, Mich. Society of Manufacturing Engineers, 2002). Paper II P. Henrikson and M. Ericsson “Non-contact Temperature Measurements using an Infrared Camera in Aerospace Welding Applications” Trends in Welding Research: Proceedings of the 6th International Conference, pp 930-936, 15-19 April, 2002, Pine Mountain, Georgia, USA. Paper III M. Ericsson, D. Berglund and P. Nylén “Three Dimensional Simulation of Robot path, Heat Transfer and Residual Stresses of a TIG-welded Part with Complex Geometry” Trends in Welding Research: Proceedings of the 6th International Conference, pp 973 – 979, 15-19 April, 2002, Pine Mountain, Georgia, USA.

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Included papers

Paper I Three-dimensional simulation of robot path and heat transfer of a TIG-welded part with complex geometry M. Ericsson*, P. Nylén** G. Bolmsjö*** * University Trollhättan/Uddevalla Box 957 S-461 29 Trollhättan Sweden ** Volvo Aero Corporation, S-461 81 Trollhättan, Sweden. ***Department of Production Engineering, Lund Institute of Technology, Box 118, S-221 00 Lund, Sweden

In Proceedings of the 11th International Conference on Computer Technology in Welding, pp 309-316, December 6-7, 2001, Colombus, Ohio, USA. Also published as SME Technical Paper AD02-292 (Dearborn, Mich. Society of Manufacturing Engineers, 2002).

Three-dimensional simulation of robot path and heat transfer of a TIG-welded part with complex geometry M. Ericsson*, P. Nylén** G. Bolmsjö*** * University Trollhättan/Uddevalla Box 957 S-461 29 Trollhättan Sweden ** Volvo Aero Corporation, S-461 81 Trollhättan, Sweden. ***Department of Production Engineering, Lund Institute of Technology, Box 118, S-221 00 Lund, Sweden

Abstract The applications of commercial software (OLP) packages for robot simulation, and programming, us interactive computer graphics, provide powerful tools for creating welding paths off-line. By the use of such software, problems of robot reach, accessibility, collision and timing can be eliminated during the planning stage. This paper describes how such software can be integrated with a numerical model that predicts temperature-time histories in the solid material. The objective of this integration is to develop a tool for the engineer where robot trajectories and process parameters can be optimised on parts with complex geometry. Such a tool would decrease the number of weld trials, increase productivity and reduce costs. Assumptions and principles behind the modeling techniques are presented together with experimental evaluation of the correlation between modeled and measured temperatures.

1 Introduction The metallurgical structure of a metal, which determines its mechanical properties, is a function of its chemical composition, its initial structure and the thermal effects of the welding process. Theoretically, if both the thermal events and the response of the material to the thermal process is known, the resulting changes in microstructure and properties can be predicted. Several papers have been published concerning numerical modeling of thermal histories, residual stresses, and distortion (Ref. 1-7). Mainly two-dimensional studies have been performed. Threedimensional studies are still restricted to simpler shapes such as plates and pipes. The use of robots for arc welding started in the early 70´s and is now extensively used in the MIG/MAG processes. Using robots for TIG (GTAW) welding is however still rare. One of the reasons is the increased demand for precise

52

programming and control. Programming of welding robots is usually done manually by the jog teach method. Using this method the robot is off-line, the part stationary, and the robot arm jogged through the program under reduced power and at reduced speed, via a joystick. Generating a path by hand in this way can be time consuming. On a complex geometry, it is virtually impossible for a programmer to maintain constant gun velocity, distance from, and orientation to, the part. However, by using computer simulation this problem can be overcome. Using this method, the programming is moved away from the robot to a graphical computer system, often referred to as a “off-line programming” system (OLP). The technology in this area is well established and has been a research area (Ref. 8-11) for some ten years. Despite these extensive investigations, the two different simulation techniques (numerical process modeling and OLP) seems only to have been studied separately. The need for a better simulation tool for arc welding was the starting point for a research program at the University Trollhättan/Uddevalla in collaboration with Volvo Aero Corporation. The objective of this program is to provide temperature time histories and metallurgical- and mechanical-properties predictions on robot welded parts. The program is divided into four parts: 1. to off-line program parts with complex shapes, 2. to numerically predict the shape of the molten pool by the use of Computational Fluid Dynamics (CFD) techniques, 3. to numerically solve the energy equations in the solid material with sufficient accuracy that metallurgical predictions can be made, as well as to link the off-line programming model with this numerical model, and 4. to empirically establish relationships between temperature-time history and metallurgical and mechanical properties This paper is concerned with parts 1 and 3 above; namely methods of programming robots off-line and of predicting temperature-time histories on parts with complex shapes.

2 Principle of Off-line Programming (OLP) Several commercial software packages for off-line programming of robots exist (CATIA, IGRIP, Robcad GRASP etc.). A brief description of the methodology using such systems is given below. A more detailed description is given in (Ref. 8). The methodology of OLP includes the following steps (Ref. 8): 1. 2. 3. 4. 5.

modeling of the work cell, modeling of the work-piece, calibrating the work-cell, adjusting and fine-tuning up and down loading of programs, programming, and

53

6. test runs and macro programming enhancements The first step to model the work cell concerns the construction of a geometric and a kinematic model of the robot, positioner etc. This demands access to design drawing of the cell together with measurements of critical dimensions in the cell. The workcell model is usually constructed directly in the OLP system. The IGRIP (Interactive Graphics Robot Instruction Program, Deneb Robotics) system was used in this study. In the second step a geometrical description (CAD data) of the part to be welded is generated either in a CAD/CAM software or in the off-line programming software (OLP). If this model is created in a CAD system the data is imported to the OLP software either using a neutral interface (for instance IGES) or a direct reader. The accuracy of the modeled workcell is usually not high and the third step is therefor to make a calibration by measuring different points in the physical welding cell. This procedure might include several sub-steps depending on the complexity of the workcell. In this work a tool calibration and a calibration of the workpiece were performed. Tool calibration is performed to determine the tool center point and to determine the orientation of the weld torch. The procedure used in this study was to have a measuring arrow in a fixed position in the work cell and to move the robot to this position in different directions. The positions from the real robot cell were then uploaded to the OLP software and a “best fit” was performed by the system. The calibration of the workpiece was performed similarly by moving the robot to clearly identified positions on the workpiece. These positions were recorded and uploaded to the OLP software where the difference between model and measurements was calculated and an adjustment of the model using least squares fitting was done. The motion of the robot is then programmed in steps four and five, either in a high level programming language (for instance GSL, which is the graphical simulation program in IGRIP) or in a specific robot language (such as RAPID, which is the program language for ABB robots). A robot trajectory is then defined by a set of coordinate frames specifying locations and gun orientation. After that, the motion may be simulated to check the results on the computer. High level languages are then translated and the program finally downloaded to the robot controller where in the final step, test runs are performed. Figure 1 shows a screencapture during the simulation in the OLP system.

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Figure1: OLP model.

3 The Computational Heat Transfer Model The primary aim of the numerical finite element model is to predict the temperature evolution outside the molten zone on a part with complex geometry. Here, the software ICEM CFD, HEXA, which is a commercial pre-processor for CFD and structural applications, was used to mesh the part. The commercial FEM program Marc from MSC Software was used in the heat transfer predictions. User subroutines had to be developed to simulate the moving heat source. A Gaussian surface distribution was used to model the heat source from the weld. This distribution was preferred to a volumetric one (Ref. 4) since it reduces the number of parameters (unknown variables) to be fit and because the plates to be welded were considered thin (850°C

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Heating, If T pea k