Development of a new staking process for an automotive part ...

Int J Adv Manuf Technol DOI 10.1007/s00170-016-9132-0

ORIGINAL ARTICLE

Development of a new staking process for an automotive part Hong-Seok Park 1 & Trung-Thanh Nguyen 2

Received: 3 February 2016 / Accepted: 4 July 2016 # Springer-Verlag London 2016

Abstract This paper systematically investigates the infrared staking (IS) process via process modeling, numerical simulations, and experimental validation. The objective of this work is to optimize process parameters for improving the joint strength of polypropylene car door trim. A holistic approach based upon numerical simulation was proposed considering the manufacturing history of sequential processing steps, including heating, forming, and cooling. The process parameters evaluated were heating time, cooling time, and airflow rate, while the structural testing force was considered as the objective. Firstly, numerical simulations were applied in conjunction with the Box-Behnken design (BBD) experimental method and a response surface methodology (RSM) to create the quadratic mathematical model of the testing force. An analysis of variance (ANOVA) was then conducted to investigate the adequacy of the model and to identify significant factors. Finally, a multi-island genetic algorithm (MIGA) was applied to determine optimal values of process parameters and the resulting response. The testing force was maximized at the optimal parameters of 14 s, 14 s, and 60 ft3/h for heating time, cooling time, and airflow rate, respectively. Correlation between simulated and experimental results was conducted to illustrate the effectiveness of the proposed approach. This work is expected to contribute toward improving the manufacturing efficiency of the infrared staking process.

* Hong-Seok Park [email protected]

1

Lab for Production Engineering, School of Mechanical and Automotive Engineering, University of Ulsan, San29, Mugeo 2-dong, Namgu, Ulsan 680-749, South Korea

2

Faculty of Mechanical Engineering, Le Quy Don Technical University, 236-Hoang Quoc Viet Street, Hanoi 100000, Vietnam

Keywords Infrared staking process . Joint strength . Car door trim . Polypropylene . Holistic numerical simulation

1 Introduction Heat staking is a well-known method of heating and forming a plastic stud to join similar or dissimilar materials. This process offers numerous benefits, such as high productivity and cost effectiveness, by eliminating fasteners and adhesives. Until recently, traditional staking methods based upon the use of hot air, ultrasonication, or heated tools were widely used in a variety of industrial applications for joining plastic materials. Unfortunately, these methods have weaknesses, such as inducing stress in the formed stud, damage to components, plastic stringing or sticking to the punches, and inconsistent processing. Therefore, the development of new staking processes is desirable. The concept of infrared staking (IS) technology, which can be considered a new joining technique to assemble different plastic parts, is illustrated in Fig. 1 [1]. The IS process involves four basic phases: clamping, heating, forming, and cooling. During the IS process, the stud is heated evenly by infrared irradiation from a halogen lamp. At the end of the heating cycle, the semi-molten stud is then formed by a staking punch. Subsequently, ambient air is used to assist in regulating the punch and stud temperature. At the end of the hold time, the staking punch is retracted from the molded part, completing the cycle. This advanced process offers some advantages compared to traditional staking methods, such as higher efficiency and productivity and better mechanical properties. However, recent researchers have not focused on the infrared staking process, and simulation as well as parameter optimizations have not been conducted to improve the mechanical strength of IS joints.

Int J Adv Manuf Technol

Fig. 1 Infrared staking concept [1]

Physical experimental studies of the infrared staking process can be both time-consuming and expensive. An understanding of the effects of process parameters on the resulting joint strength is necessary during the early stages of process planning. Fortunately, finite element method (FEM)-based approaches using well-defined material properties and numerical models can be excellent alternatives to physical experiments, producing reliable simulations of joining processes [2–5]. Results indicate that FEM is a powerful technique for predicting the performance of a joining technique. To increase the application potential of the joining technique, the development of a new staking process using infrared energy for an automotive door trim with polypropylene (PP) material (Fig. 2) is considered herein. Door trim is a common component in the automotive industry and is manufactured in large quantities. It is essential to have a reliable numerical model for conducting parametric studies in order to improve joint strength. Within a process chain, the outputs of preceding processes are considered to be inputs for succeeding steps. Moreover, we found that altering the process parameters, such as heating time, cooling time, and airflow rate, leads to variations in the mechanical behavior of the infrared staking joint. Therefore, an effective approach that describes the process chain behavior and allows the optimization of process parameters in terms of the resulting mechanical strength is an important research goal. Accordingly, the aim of the present study was first to propose a holistic numerical simulation approach to simulate the infrared staking process, considering the interdependencies between the processing steps. Subsequently, a metamodelingbased optimization method was developed to obtain optimal values of the process parameters (inputs) that would maximize the testing force (output). Finally, an infrared staking machine was designed and fabricated in order to implement a physical experiment for evaluating the optimization results. The remainder of the paper is organized as follows. The scientific methodology used is first introduced. Then, we present a holistic numerical approach to simulate the entire infrared staking process. Next, we discuss numerical experiments, describe the data analysis, and present the optimization results. Finally, we draw conclusions and suggest future research.

Fig. 2 Car door trim with IS joints

2 Research methodology 2.1 Optimization framework To our knowledge, there is no commercial simulation tool that focuses on parameter optimization for improving joint strength. Additionally, the usual analytical methods to determine the optimal parameters of the infrared staking process are ineffective [5]. For these reasons, we have developed a framework to facilitate the optimization process based on FEM, numerical simulations, and metamodeling (Fig. 3). Instead of using direct numerical optimization, we applied a metamodel to reduce the number of simulations required. The theory of design of experiments (DOE), the approximation method, and the metamodel are discussed in detail in [6, 7]. First, we defined the design variables and constraints and performed a design of experiments. An effective method based on a multidimensional experimental design, namely the BoxBehnken design [8, 9], was used to organize the combination of process parameters. Secondly, a numerical model of the infrared staking process was developed, and virtual experiments were carried out using different combinations of inputs in the design matrix. Thirdly, response surface methodology (RSM) was used to develop a second-order regression model of the testing force (the output) with respect to various process parameters (the inputs). We verified the fidelity of the approximate model before carrying out the optimization process. Then, we conducted an analysis of variance (ANOVA) to determine the fitness of the model and to identify significant design variables. The optimization problem can be solved by any optimization algorithm; however, the genetic algorithm (GA) [10] is preferred for global optimization. GA is a global optimization search that tends to be more effective in avoiding local optima than other gradient search methods such as the quadratic

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Fig. 4 Cause-and-effect diagram used to select the process parameters for optimization

3 Numerical simulation approach 3.1 FEM-based simulation framework Fig. 3 Systematic procedure for the simulation-based design of experiments and optimization

programming, generalized reduced gradient, and modified feasible direction methods. Although the GA method can find global optima, it requires many iterations of function evaluation. Fortunately, a computer can perform thousands of function evaluations per second when the explicit metamodel (RSM model) is adopted. Therefore, the combination of RSM and GA is the best choice for intensive simulationbased optimization. In this work, optimal values of process parameters to maximize the testing force were obtained using a multi-island genetic algorithm (MIGA) based on an explicit equation obtained by means of the previous approximation. The accuracy of the optimized results was validated through physical experiments conducted using the prototype machine.

2.2 Parameters considered The parameters most likely to influence the joint strength were chosen on the basis of literature reviews [1–3, 11], as shown in Fig. 4. The practical analysis showed that the parameters of the machine, forming tools, and material properties should be considered fixed factors. Therefore, the process parameter optimization is an appropriate approach for determining the maximum joint strength. Table 1 lists the levels of three key process parameters, including heating time (th), cooling time (tc), and airflow rate (Af). The parameter ranges were selected according to common technical values used in current staking processes, the capacities of the devices used (i.e., the halogen lamp, staking punch, and airflow supplier), the properties of PP, and available literature information. The different simulation approaches used for the respective processing steps are described in detail in the following section.

To precisely describe the infrared staking process behavior, a holistic simulation approach that considers the manufacturing history of the various processing steps was developed, as shown in Fig. 5. Integrative simulation of the infrared staking process was performed including consideration of the technological interfaces, which represent the transfer parameters between processing steps. The transfer variables describe the different workpiece characteristics arising from the effects of the manufacturing steps. In the process chain, the transfer parameters changed owing to the impacts of the preceding steps and influenced the working conditions and the performance of subsequent steps. Therefore, interdependencies among manufacturing steps should be taken into account with regard to the process chain optimization. During this procedure, the processing steps of heating, forming, and cooling were sequentially analyzed with the boundary conditions. The resulting outputs of the preceding processes were transferred as inputs to the subsequent steps. Thus, it can be stated that the final mechanical behavior of the infrared staking joint is a function of the results of the preceding steps. Herein, we propose an approach based on DEFORM-3D [12, 13] code to simulate individual manufacturing steps and the entire infrared staking process, as shown in Fig. 6. A CAD model of the workpiece and the staking punch was first designed to meet the requirements of the package geometry. Transient heat transfer analysis was then used to perform a heating stage simulation, in which a positive heat flux was applied to the workpiece walls. Subsequently, simulation of

Table 1

Process parameters and their levels

Symbol

Process parameter

Unit

Level −1

Level 0

Level +1

th

Heating time Cooling time Airflow rate

s s ft3/h

8 8 20

14 14 40

20 20 60

tc Af

Int J Adv Manuf Technol

punch were generated using CATIA V5R20 and then transferred to DEFORM 3D by means of an STL-format file. 3.2 Material model

Fig. 5 Holistic numerical simulation approach

the heated stud during the forming stage was performed with appropriate boundary conditions to create the joint shape. Next, these results were used in a cooling analysis based on heat transfers between the joint and the staking punch and those with the environment. Finally, a structural analysis based on the results of previous stages was performed to obtain testing force values. Material states such as stress history, strain history, geometric model, and temperature were considered as the interfaces between processing steps. The holistic simulation process was performed sequentially with varying input parameters to obtain response values. Thus, the proposed approach resulted in a comprehensive and realistic simulation process. To obtain reliable results, in each simulation, the updated Lagrangian finite element formulation was used in conjunction with continuous and adaptive meshing techniques. Fournode elements in the workpiece model were adopted for deformations occurring during the simulation process. The movements of the workpiece in the x-, y-, and z-directions were restricted. The isotropic hardening law was used during the simulation process, followed by the von Mises stress yield criterion. The geometric models of the workpiece and staking

Fig. 6 Block diagram of the simulation methodology

The thermal-physical properties of the workpiece and staking punch were assumed to be constant and are presented in Table 2. Flow stress curves of the PP material changed as a function of the strain, strain rate, and temperature, as shown in Fig. 7 [14]. The following law was used owing to its ability to model the true behavior of a material:   σ ¼ ε; ε; T

ð1Þ

where σ, ε, ε, and T are the flow stress, effective plastic strain, effective strain rate, and temperature, respectively. 3.3 Process simulation via FE model 3.3.1 Heating stage simulation As shown in Fig. 1, during the first stage (the heating stage), radiant energy generated by a halogen lamp was used to increase the plastic temperature. The lamp was composed of a coiled tungsten filament contained in a bulb enclosure filled with argon gas. Moreover, the lamp was coated with a reflector to increase the heat flux received by the product. A schematic representation of the halogen lamp, as shown in Fig. 8a, was used to calculate the filament and bulb temperatures based on the following nonlinear system of equations [16]:

Int J Adv Manuf Technol Table 2

Material properties of PP and S45C [15]

Parameters

PP

S45C

Density (g/mm3) Young’s modulus (MPa) Poison ratio

0.892 × 10−3 1300 0.45

7.85 × 10−3 250 × 103 0.29

Thermal conductivity (W/(m °C)) Specific heat (J/(g °C))

0.16 2

49.8 0.486

P−ε F ðT F ÞS F σT 4F −2πLB k Argon

*

T






T F −T B lnðd B =d F Þ

αB ðT F Þε F ðT F ÞS F σT 4F

þ 2πLB k Argon T

−εB S B σT 4B −hS B ðT B −T Þ

¼0

*






 ¼ 0 ð2Þ

T F −T B lnðd B =d F Þ



ð3Þ where P, ε, S, L, and d represent the lamp power, emissivity, area, length, and diameter, respectively. T, k, α, and h denote the temperature, conductivity, absorption, and convection coefficient, respectively. T* is assumed to be equal to (TF + TB)/2. The subscripts F, B, and Argon refer to the filament, bulb, and argon gas in the halogen lamp, respectively. Because the objective functions and constraints were in the form of analytic equations, an MS Excel spreadsheet including a solver tool was used to

solve these equations. The bulb temperature TB was calculated to be 412 K (139 °C). The radiant heat flux from the halogen lamp to the thermoplastic stud during the heating stage was calculated using the following equation:
qradiation ¼ σF εB T 4B −εP T 4P ð4Þ where σ and F are the Stefan–Boltzmann coefficient and the view factor, respectively. The subscript P refers to the plastic. The view factor value was taken from Table 3.1 of Incropera et al.[17]. For each heating stage simulation, the transient heat transfer mode was used and the positive heat flux was added onto the stud walls. A number of cooling steps equal to 100 and a step increment of 5 were used in order to increase the accuracy of simulation results. The free surfaces of the plastic were under free convection at an ambient temperature of 20 °C with a convection heat transfer rate of 2 W/m2 (Fig. 8b). 3.3.2 Forming stage simulation The staking punch geometry and boundary conditions used in the forming analysis are shown in Fig. 9. The forming tool (Fig. 9a) was set to move in the z-direction with a velocity of 100 mm/s. To minimize the simulation time and increase the

Fig. 7 Stress-strain curves of polypropylene (PP) Fig. 8 Simulation model of the heating stage. a Schematic representation of the halogen lamp. b Boundary conditions for the heating stage

(a)

(b)

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(a)

(b)

Fig. 9 Simulation model of the forming stage. a Staking punch geometry. b Boundary conditions for the forming stage

heat flux was applied to the mold and the workpiece. Furthermore, as evident in the figure, the magnitude of the airflow rate significantly influenced the final workpiece temperature and the joint strength. Thus, it was necessary to derive the relationship between the airflow rate and the cooling flux. During the cooling process, the polymer was assumed to extend infinitely in the z-direction, as shown in Fig. 11a. The polymer was assumed to be at T0 at time t = 0 and at Tf after cooling. The heat fluxes at subsequent times (t) for such a case were given by the following equation: Q ¼ h  ΔT ¼


f  Nu T f −T 0 Dh

ð5Þ

where h, Nu, f, and Dh denote the convection coefficient, Nusselt number, friction factor, and hydraulic diameter, respectively, and f and Dh can be calculated using Eqs. (6) and (7) [18]:   ρ  um  Dh −1=4 ð6Þ f ¼ 0:316 μ ð7Þ Dh ¼ DC

Fig. 10 Types of heat transfer during the cooling stage

accuracy, the staking punch was modeled as perfectly rigid, whereas elasto-plastic material behavior was assigned to the workpiece. A forming stroke of 6 mm was used to reach the final step (Fig. 9b). A constant shear friction of 0.37 was assumed in the simulation to obtain the best results in terms of product quality. 3.3.3 Cooling stage simulation In the cooling step, the mold and the workpiece were held together while they cooled to room temperature. The workpiece temperature was modeled to decrease due to conductive heat exchange with the staking punch, whereas forced convection was used to regulate the punch temperature (Fig. 10). To take into account these aspects of the simulation, a negative

where ρ, um, and μ, respectively, denote the density, airflow velocity, and dynamic viscosity, and DC is the inner diameter of the circular tube. The change in velocity (um) depends on Af according to the following equation. um ¼

4A f πD2h

ð8Þ

For the cooling simulation, the negative heat flux in the transient heat transfer mode was added at the contact area between the mold and the workpiece. In this simulation, 100 cooling steps were used with a step increment of 5. Similar to the heating stage analysis, a convection heat transfer rate of 2 W/m2 and an ambient temperature of 20 °C were also used for the free surface (Fig. 11b). 3.3.4 Structural stage simulation To obtain the testing force, a structural analysis of the infrared staking joint was performed. The testing tool and workpiece

Fig. 11 Simulation model of the cooling stage. a Polymer faces and coordinate system. b Boundary condition for the cooling stage

(a)

(b)

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(a)

(b)

(c)

Fig. 12 Simulation model of the structural stage. a Boundary conditions for structural analysis. b The deformed shape of the joint specimen. c Testing force extracted from the simulation result

(a)

(b)

(c)

Fig. 13 Workpiece temperatures after heating. a Heating time 8 s. b Heating time 14 s. c Heating time 20 s

were modeled as perfectly rigid and plastic materials, respectively. The velocity of 100 mm/s in the z-direction was applied to the testing tool (Fig. 12a). A testing stroke of 6 mm was used to reach the final step. A constant shear friction of 0.42 was assumed to obtain the best results in view of the testing forces. During the structural simulation, the load produced by the relative displacement between the testing tool and the workpiece is increased. A further increase in the load increases

(a)

the internal stresses which reach the material ultimate tensile. After each structural analysis, the testing force was observed by extracting the simulation result (Fig. 12c). 3.4 Simulation results analysis Figures 13 and 14 show the respective temperature history and deviation for these three heating times. The

(b)

Fig. 14 Temperature deviations after heating. a Heating time 8 s. b Heating time 14 s. c Heating time 20 s

(c)

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(a)

(b)

(c)

Fig. 15 Deformation results after the forming stage for a heating time of 8 s. a Effective strain. b Temperature history. c Effective strain history

(a)

(b)

(c)

Fig. 16 Deformation results after the forming stage for a heating time of 14 s. a Effective strain. b Temperature history. c Effective strain history

final workpiece temperatures for 8, 14, and 20 s were approximately 49, 59.9, and 66.7 °C, respectively (Fig. 13a–c), and the temperature deviations were 3.2, 2.4, and 1.8 °C (Fig. 14a–c). The small temperature deviations between the two points (P1 and P2) indicated that the final temperature distribution was uniform. It can be observed from the figures that the temperature deviation gradually decreased with increasing heating time. This reduced temperature deviation arose from the greater heat input to the workpiece under longer heating times. Furthermore, the temperature at the

(a)

workpiece side (point P 2) was clearly higher than that at the central point (point P 1). Figures 15a, 16a, and 17a show the contours of effective strain after the forging stage, for the heating times of 8, 14, and 20 s, respectively. The effective strain was most uniform for the shortest heating time of 8 s. The workpiece temperature was unchanged at the points checked in the forging process (Figs. 15b, 16b, and 17b). The behavior was due to the short forging time, resulting in little heat transfer between the workpiece and staking punch. The amount of effective

(b)

(c)

Fig. 17 Deformation results after the forming stage for a heating time of 20 s. a Effective strain. b Temperature history. c Effective strain history

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(a)

(b)

Fig. 18 Temperature distribution and histories for a heating time of 14 s. a Temperature distribution for a cooling time of 14 s and an airflow rate of 40 ft3/h. b Temperature history for a cooling time of 14 s and an airflow

(a)

(c)

rate of 40 ft3/h. c Temperature history for a cooling time of 20 s and an airflow rate of 60 ft3/h

(b)

(c) 3

Fig. 19 Temperature histories for a heating time of 8 s. a Cooling time of 8 s and an airflow rate of 40 ft /h. b Cooling time of 14 s and an airflow rate of 20 ft3/h. c Cooling time of 20 s and an airflow rate of 40 ft3/h

(a)

(b)

(c)

Fig. 20 Temperature histories for a heating time of 20 s. a Temperature distribution at a cooling time of 8 s and an airflow rate of 40 ft3/h. b Cooling time of 8 s and an airflow rate of 40 ft3/h. c Cooling time of 14 s and an airflow rate of 60 ft3/h

Int J Adv Manuf Technol Table 3

Experimental results for testing force

No.

Heating time (s)

Cooling time (s)

Airflow rate (Af)

Testing force (N)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

14 14 20 14 14 14 14 14 20 8 8 14 8 20 20 8 14

14 8 8 20 14 20 14 14 20 8 14 8 20 14 14 14 14

40 60 40 60 40 20 40 40 40 40 60 20 40 20 60 20 40

325.65 252.36 245.46 340.23 325.65 270.86 325.65 325.65 310.94 158.64 235.12 212.62 220.52 275.64 316.23 200.42 325.65

Table 4

Values of the coefficients in Eq. (9)

a0

a1

a2

a3

a4

a5

a6

a7

−418.0856 46.5238 33.8317 3.4835 0.0617 −1.4434 −1.1055 −0.0420

strain at point 3 was higher than that at point 4 for all three heating times (Figs. 15c, 16c, and 17c). The higher temperature at point 3 resulted in greater softening of the material and thus to greater material deformation. Additionally, the material at point 3 moved vertically and then remained longer under the staking punch.

Table 5

Figures 18, 19, and 20 show the temperature histories after the cooling stage for the heating times of 14, 8, and 20 s, respectively. It can be seen from the figures that an increase in cooling time or airflow rate for each of the three heating times reduced the workpiece temperature. Increasing the cooling time or airflow rate until it reaches its central value significantly decreased the workpiece temperature (Figs. 18b, 19a, b, and 20b). In contrast, the plastic temperature gradually reduces as the cooling time or airflow rate tends to increase above the central limit (Figs. 18c, 19c, and 20c).

4 Numerical results 4.1 Regression model of the response In this work, a Box-Behnken experimental design with 17 trials for three factors and three levels was chosen. Among the 17 experiments, 12 trials were performed on the edge of the experimental space cube and 5 replicate runs were conducted at its central point. In each simulation, the inputs were th, tc, and Af; and the output considered was testing force (F). Based on the Box-Behnken design scheme, we performed the 17 simulation runs and obtained response data to determine the relationships between the process parameters and the testing force; Table 3 lists the results. The response surface model showing the joint strength is expressed as follows: F ¼ a0 þ a1 t h þ a2 t c þ a3 A f þ a4 t c A f þ a5 t h 2 þ a6 t c 2 þ a7 A f 2 ð9Þ

The coefficients of Eq. (9) were determined using a regression method, and their values are shown in Table 4.

ANOVA table for testing force

Source

Sum of squares

Mean square

f value

p value

Model A-Heating time B-Cooling time C-Airflow rate AB AC BC A2 B2 C2 Residual Lack of fit Pure error Core total

48,782.90 13,908.62 9348.23

5420.32 13,908.62 9348.23

157.06 403.03 270.88