performance verification and monitoring of nc machine-tools for in ...

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596.

PERFORMANCE VERIFICATION AND MONITORING OF NC MACHINE-TOOLS FOR IN PROCESS PART MEASUREMENT BY TOUCH PROBING J. R. RENE MAYER*, R. ANDRIAN*, C. FORTIN*, G. M. CLOUTIER* AND T. LUONG** *Ecole Polytechnique, Montréal, Canada **Pratt & Whitney, Québec, Canada

Keywords : Monitoring, NC machine tool, touch probing, diagnostic, process capability. SUMMARY We present a methodology for the rapid diagnostic of a machine-tool using data acquired by touch probing. The probing may be performed either on existing portions of the machine such as the machine pallet, the part fixture or a brought in artefact depending on their geometry and accessibility. The method aims at providing frequent quick checks on the machine geometric health. An initial probing test provides a reference set of readings against which future probing tests are compared. The data is analysed to provide three levels of diagnostic. The first level concentrates on the resultant errors and pinpoints potentially faulty axes. The second level suggests potential causes in terms of axis parameters such as straightness, scale etc. Finally, the third level indicates how the part features to be machined or probed by the machine may be affected.

1.

OBJECTIVES

The production of quality parts on a machining centre depends on multiple factors, some related to the machine geometry, dynamic effects, thermal errors, control software, set-up procedures, clamping and fixtures, environmental effects, tool wear, spindle errors, the part itself etc. Furthermore, the machine status will vary over time and may be subject to instant variations, for example after an unexpected event such as a collision. Hence, it is desirable to monitor the machine condition. The monitoring

results may be used for predictive maintenance purposes or to detect conditions which could result in the potential production of out of tolerance parts. The purpose of the project is twofold. Firstly, it aims at providing timely information on the geometric health of the machine showing trends, and identifying the particular axes and axes parameters at fault. Secondly, it aims at predicting the ability of the machine to position its tool or measuring probe at the locations defined by the process and which could possibly impair process integrity or the reliability of the in-process probing data. The reason for this second objective is that the geometric deviation of a machine may or may not be significant for a particular operation depending on the machine movements for that process and the part feature tolerance. These objectives are accompanied by practical constraints. The test and the diagnostic should be sufficiently easy and fast that they could be performed daily. As such the tests do not aim at replacing currently available methods capable of thorough machine verification. In order to keep cost to a minimum the method will use the touch trigger probe already available on many machining centres. 2.

REVIEW OF LITERATURE

2.1.

Machine tool modelling and diagnostic

Models of the machine tool pertaining to the trajectory of the tool with respect to the part come

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596. in three categories: geometric, kinematic, and dynamic. Their main use is to establish predictions. A model may be more or less representative of reality depending on its complexity. Its validity heavily depends on the sampling space covered during the data gathering phase of the study. The data can be acquired directly on the machine [1], indirectly on the workpiece [2, 3], or indirectly on an artefact [4]. The amount of data suitable for model identification is impressive. When possible, special devices and instruments are developed and used. Whatever the case, data gathering plans must choose configurations that excite all parameters to allow their identification. An issue that also hinges on complexity [5]. It is unreasonable to strive for complexity and overcome our ability to identify the parameters. The data should also suitably excite parameters and achieve isotropic uncertainties from the predictions established with the model. Practical constraints do not always allow these conditions to be met. When only an error prediction is sought, the user is not limited to mechanically tractable models, hinging on some detailed knowledge about the mechanisms at hand. As a result, one cannot necessarily establish representative model assisted diagnostics. Chen, Ulsoy and Koren support the proposal of a mechanically tractable model that could contribute to a causal diagnostics [6]. Diagnostics improvements usually aim at the broader ability to pinpoint the faulty process, or provide assistance in the event of a breakdown. Seldom has it been applied to the diagnostics of workpiece errors geometrically, kinematically, or dynamically induced [7]. Pragmatic users find that a “reasonable” prediction bears a competitive advantage with economic consequences. They are less interested in the scientific elegance provided by model building and identification, than in the ability to diagnose if possible, but certainly to forecast and prevent adverse effects [8]. 2.2. Conventional machine diagnostic (nonprobing) Direct measurements of the individual machine axes is generally accomplished using mechanical

devices such as dial gauges, straight edges, mechanical squares, step gauges etc. or other means such as laser interferometers, autocollimators, electronic levels etc. These methods are useful for detailed characterisation of the axis to axis squareness errors as well as for the six position dependent axis parameters (scale, straightness (2), yaw, pitch and roll for linear axes and scale, radial (2), tilt (2) and axial for rotary axes). They are usually used for machine acceptance on purchase, after maintenance and at regular but relatively spaced intervals. They are demanding in terms of operator knowledge and set-up time so that they could not be used to monitor the machine geometry evolution on a weekly or daily basis. Indirect methods such as telescopic double ball bar systems have the advantage of a more rapid diagnostic although they are limited in terms of the geometric parameters they can detect. Backlash, straightness, squareness and scale mismatch will affect the readings. However, the relationship between the effects and their causes may be difficult to establish especially when a combination of these effects is present. 2.3.

Probing methods

Probing methods involve mounting a touch trigger probe instead of a cutting tool at the machine spindle and performing measurements on the part, a fixture, a pallet or features of the machine itself much like a co-ordinate measuring machine (CMM). Any measurement taken this way is affected by a resultant error which results from the combination of a number of machine parameters. Trapet and Wäldele describe methodologies to determine the individual axis parameters of three-axis machines from touch probing measurements on an artefact [9]. For example, a hole plate or a ball plate, made of a 2D arrangement of holes or spheres, may be measured at a number of pre-defined locations within the machine work envelope. Use is made, mathematically, of the redundancies amongst the gathered data in order to solve for the values of the axis parameters at pre-defined and discrete locations along each axis. However, this approach was not retained because it imposes that an external artefact be brought in.

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596.

Figure 1 Diagram of the machine diagnostic tool concept. 3.

THE PROPOSED METHOD

3.1.

The concept

Since we do not wish to impose a specific artefact for our project, it will not provide the necessary system of equations required to obtain a unique solution for the machine parameters. The difficulty consists in deducting as much as possible in terms of machine geometry and process capability from an insufficient amount of data taken on a geometrically imperfect artefact. Thus, a solution technique is needed to go from the probing results to the machine axes parameters and then to the process integrity. Figure 1 shows an overall diagram of the proposed diagnostic tool. The inputs to the system are : 1. A file containing the artefact probing strategy, the latest probing results and reference probing results taken after a machine thorough diagnostic ; 2. A file containing the description of the machine kinematics and nominal geometry ; 3. A file containing the process requirements as a detailed description of the features to be manufactured by the machine, including their type, location and tolerance.

The first operation consists in a pre-processing of the probing data of each test in order to isolate the effect of the axis which was moving during each test. These are the resultant probing errors, {∆Ω}, which can be immediately plotted with results from past tests to provide a rapid feedback to the machine operator as to the evolution in the machine condition. These resultant errors can then be associated to subsets of the six position dependent geometric parameters of the axis under test in order to further refine the diagnostic whenever an unexpected change in the resultant errors has been detected. This requires the computation of a Jacobian matrix here called the sensitivity matrix, [S], of the probing tests to the axis parameters. The final step in the diagnostic is to make an assessment on the likelihood that the process integrity has been jeopardised. A second Jacobian matrix, [J], is computed which quantifies the effect of the parameters onto the part features. A number of schemes are under study to combine {∆Ω}, [S], [J] and the process requirements to issue a red, green or yellow flag to the machine operator to indicate whether or not to proceed with production. 3.2.

The artefact

Probing will be done on non-varying features of a nominally known artefact. Ideally, these features should already be available on the machine. Existing features may be those on the machine

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596. pallet or on the part fixtures. Figure 2 shows one of the machines studied, an OMNIMILL 60 (OM60) which has a BXFZYA configuration. Figure 3 shows a typical pallet used on the OM60 machining centre. The pallet has many features which can form the basis of probing tests. Alternatively, an artefact could also be brought in although this increases the time required to perform the tests.

Figure 2 OM60 machining centre.

the artefact is probed by the machine. This initial probing defines a reference set of measurements. Any future probing of the artefact only aims at measuring the variation in the probing results. 3.3.

The probing test

A number of probing tests must now be defined. Each test aims at detecting errors in the smallest possible number of machine parameters thus reducing the model complexity. This helps the diagnostic by facilitating the association of the resultant errors with the axis parameters. Table 1 describes a probing test for the X axis which involves probing in the z direction inside one of the parallel T-slots with the slot aligned with the machine X axis. Ideally, each test involves probing the artefact in a particular direction at a number of positions along a moving axis. The moving axis is the one under test. By keeping the probing direction constant, it isolates a small number of parameters (up to three out of six) of that axis which can affect the readings. Table 1 Probing tests for the X axis of the OM60 measured in the z direction. Nominal probed coMoving axis Surface S.D. O.D. ordinates referred to 0=non-moving normal (in) (in) the artefact reference 1=moving frame (in and deg) X

Figure 3 OM60 pallet. Since the objective is not to replace conventional machine evaluation methods, it is assumed that at some point in time, a thorough metrological investigation has been done on the machine. Our objective is then to monitor the machine changes from that time. As such, the precise geometry of the reference artefact is not required. Instead, immediately following a machine thorough check,

Y

Z

A

B X Y Z A B X Y Z

-12 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

-8 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

-4 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

0 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

4 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

8 -0.5 16 300 0 1 0 0 0 0 0 0

-1

0.1 0.05

12 -0.5 16 300 0 1 0 0 0 0 0 0 -1 0.1 0.05 Moving axis : The axis with changing nominal values. (The axis evaluated by the test) S.D. = Standoff Distance: The distance before a touch after which a touch is regarded as valid. O.D. = Overtravel Distance : The distance that the machine is allowed to travel, after reaching the target, for a touch.

3.4.

Pre-processing

The raw probing results are the differences between the nominal and actual probed coordinates projected along the surface normal. Their value is influenced by the geometric

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596. imperfections of the artefact, the error in the location of the artefact on the machine and by a number of machine parameters, moving and nonmoving ones. A pre-processing of the probing data aims at isolating the moving axis parameters. The pre-processing subtracts the raw readings from an initial reference probing which gives a variational probing. Then it either removes the mean and in some cases the slope of the variational probing. This is effective since the axis parameters are position dependent and so invariant for non-moving axes, thus their effect is either a constant or proportional to the moving axis displacement. A proportionality relation may exist if there is a relative motion between the probe tip and a non-moving axis since in this case any angular parameter acts through a varying lever length. Figure 4 shows simulated raw and pre-processed data (offset and slope removal) for the probing test described in Table 1. Realistic values for the parameters of each axis are simulated as polynomial functions. The resultant errors caused by the moving axis are successfully isolated even in the presence of errors on all axes as shown by the solid curve.

causes at the axis level can be evaluated from the knowledge of the probing location, its direction and from the knowledge of the machine kinematics. This can be done by analysing the [S] matrix relating the machine parameter vector {q} to the probing error vector {∆Ω}.

{∆Ω} = [S]{ q}

The Jacobian matrix may be obtained analytically directly from the kinematic definition of the machine using 4x4 homogenous transforms and the Newton-Euler equations. For instance, the machine shown in Figure 2 has a kinematic chain with two serial branches in parallel, i.e. the workpiece and the tool branches. Each branch originates at the frame (F). The relative location between consecutive axis frames of a branch is defined by three linear offsets and three angular offsets. Table 2 represents the machine kinematics and is an input to the analysis software. Table 2 Branch

Machine kinematic model. Axis

Offsets (mm)

Offsets (deg)

-3

2.5

x 10 Resultant error of X axis in z direction (in)

x Part

2 1.5

Tool

1 0.5 0 -0.5 -1 -15

-10

-5 0 5 10 Position along the X axis (in)

15

LEGEND: - - -

simulated errors on the X axis only, before processing; ...... simulated errors on all axes, before processing; ___ processed simulated errors on all axes superposed on the processed simulated errors on the X axis only.

Figure 4 Examples of simulated probing data. 3.5. From probe position errors to machine parameters Once the resultant variational errors due to the moving axis have been isolated, their potential

(1)

y

X

0

B

0

Z

α

z 1400 1500

β

γ

0

0

0

0

0

0

0

0

1000 -1500

0

0

0

Y

0

1950

0

0

0

0

A

0

0

200

0

0

0

600

It is then possible to produce graphs of potential parameter values which could cause the observed resultant variational errors. These graphs take into account the effect of each individual machine parameter onto the resultant probing deviation. Such information is directly available from the Jacobian matrix [S]. This information can be produced for the tests and is also used for the design of the artefact since the artefact should be sensitive to deviations in all the important machine geometric parameters. The importance of a machine geometric parameter is determined by making a similar analysis, but this time replacing the probe by the tool and the artefact by the workpiece.

32nd International MATADOR Conference – 10-11 July 1997, Manchester, United Kingdom, pp. 591-596. 3.6. The link between probing and feature errors The final stage of the diagnostic consists in making some judgement on the integrity of the process that the particular machine would generate given the results of the probing tests. A process requirements file is generated on the basis of the manufacturing process plan. The process plan contains detailed information on the type, location, size and tolerance of all features to be machined. This information is used to compute the appropriate Jacobian matrix, here called [J], which describes the impact of the machine geometric parameter vector, {q}, on the feature error vector,{f}

{f} = [J]{q}

(2)

levels of diagnostic information i.e. axis level, parameter level and process level. Simulations are now being conducted to establish the rate of success of the process level prediction in order to determine the best scheme. The ability of the machine to perform the probing tests has been verified experimentally and initial results are expected shortly. ACKNOWLEDGEMENT This project is conducted under research contract for Pratt and Whitney of Canada Inc., Longueuil (Québec). J. R. R. Mayer wishes to acknowledge the financial support of an individual research grant from the Natural Sciences and Engineering Research Council of Canada. REFERENCES

The final step is to combine eqs. 1 and 2 to conclude on {f} and compare it with the associated tolerance vector {t}. Note that {∆Ω} must be interpolated to correspond to the coordinates of the part features. Even so, a unique solution does not exist for {f} because of the structure of the data gathered. So, a number of schemes are under study to generate representative solutions. They generally involve associating the probing errors with machine parameters on the basis of {t}, [J] and [S] in order to provide best case and worst case scenarios. A further scheme consists in a conventional solution. Since the number of unknown exceeds the number of equations, the scheme uses a pseudo-inverse and provides a minimum norm solution for the parameters resulting in the following system :

{f} = [J][S]−1 {∆Ω} 4.

(3)

CONCLUSION

The early diagnostic of a potential problem on a machining centre is crucial to its economical use. In this paper, we have presented an approach which uses the results of probing tests on the machine itself or on a low precision artefact. The probing strategy is carefully designed to be sensitive to subsets of the machine geometric parameters. The data processing generates three

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