A new concept of continuous measurement and error correction in Coordinate Measuring Technique using a PC

Abstract This article presents a new method of daily accuracy measurement with the employment of a Coordinate Measuring Machine (CMM) based on the mapping error procedure and the reduction of the error level through automatic corrections saved onto the matrix of CAA (Computer Aided Accuracy). Currently, measurements conducted using the Coordinate Measuring Machine do not guarantee credible and accurate results, owing to the fluctuation of CMM parameters over time as a consequence of its considerable dynamics of functioning. It would appear that the period in between the reverification test and interim checks is excessively long in the case of CMM. Automatic corrections saved onto the matrix of CAA are only optimal for the particular day on which those measuring procedures were conducted and there is no guarantee whatsoever that the above-mentioned corrections would be optimal for every single measurement conducted over time. For the purpose of establishing whether repeatedly performed measurements conducted in the same conditions would also retain the same value over time, four measurements were carried out, consisting in the verification of results of establishing the effective radius of the stylus tip over the course of ten consecutive working days. Those tests were conducted with the use of two CMMs (Kemco 600 CNC and Dea Global Image Clima (simplified test)) at five measurement speeds (5–50 mm/s). The results were presented of unadjusted and adjusted stylus/probing system qualification procedures. The procedure suggested and then tested in practice was based on the analysis of results in an external and independent Certification Centre. In addition, a practical test of the integration of the PS-20plus electronic module and a commercial measuring probe (Bipropol) was conducted.

[1]  A. Balsamo,et al.  A Method for Thermal-Deformation Corrections of CMMs , 1990 .

[2]  Inmaculada García,et al.  Floating point arithmetic teaching for computational science , 2003, Future Gener. Comput. Syst..

[3]  Jun Ni,et al.  On-line error compensation of coordinate measuring machines , 1995 .

[4]  Robert Schmitt,et al.  Geometric error measurement and compensation of machines : an update , 2008 .

[5]  Phj Piet Schellekens,et al.  Accuracy Limitations of Fast Mechanical Probing , 1996 .

[6]  John A. Bosch,et al.  Coordinate Measuring Machines and Systems , 1995 .

[7]  Yin-Lin Shen,et al.  Error compensation of coordinate measurements in computer-integrated manufacturing using neural networks , 1996 .

[8]  Robert J. Hocken,et al.  Characterization and compensation of dynamic errors of a scanning coordinate measuring machine , 2007 .

[9]  Christoph W. Ueberhuber.  Numerical computation : methods, software, and analysis , 1997 .

[10]  Simon X. Yang,et al.  Automatic sequence of 3D point data for surface fitting using neural networks , 2009, Comput. Ind. Eng..

[11]  Steven C. Chapra,et al.  Numerical Methods for Engineers , 1986 .

[12]  P M Harris,et al.  Report to the National Measurement System Policy Unit, Department of Trade & Industry GUIDELINES TO HELP USERS SELECT AND USE SOFTWARE FOR THEIR METROLOGY APPLICATIONS , 2000 .

[13]  Rudolf Drieschner.  Chebyshev approximation to data by geometric elements , 2005, Numerical Algorithms.

[14]  W. G. Weekers,et al.  Compensation for dynamic errors of coordinate measuring machines , 1997 .

[15]  John R. Wolberg,et al.  Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments , 2005 .

[16]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[17]  Åke Björck,et al.  Numerical methods for least square problems , 1996 .

[18]  Alistair B. Forbes,et al.  Reference software for finding Chebyshev best-fit geometric elements , 1996 .

[19]  Israel Koren.  Computer arithmetic algorithms , 1993 .

[20]  S. Sartori,et al.  Geometric Error Measurement and Compensation of Machines , 1995 .

[21]  Chen Yanling,et al.  The geometric dynamic errors of CMMs in fast scanning-probing , 2011 .

[22]  Kyo-Il Lee,et al.  Integrated real time compensation system for errors introduced by measurement probe and machine geometry in commercial CMMs , 1996 .

[23]  Russell Beale,et al.  Handbook of Neural Computation , 1996 .

[24]  Maurice G Cox,et al.  Chebyshev best-fit geometric elements , 1993 .

[25]  Nicholas J. Higham,et al.  INVERSE PROBLEMS NEWSLETTER , 1991 .

[26]  Takeaki Kariya,et al.  Generalized Least Squares , 2004 .

[27]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[28]  William Stallings.  Computer Organization and Architecture: Designing for Performance , 2010 .

[29]  A B Forbes,et al.  Simulated instruments and uncertainty estimation. , 2000 .

[30]  Bryan Kok Ann Ngoi,et al.  Dynamic Error Compensation of Coordinate Measuring Machines for High-Speed Measurement , 1999 .

[31]  C. M. Shakarji,et al.  Reference Algorithms for Chebyshev and One-Sided Data Fitting for Coordinate Metrology , 2004 .

[32]  Salah Hamed.  The influence of fitting algorithm and scanning speed on roundness error for 50 mm standard ring measurement using CMM , 2008 .

[33]  Pawel J Swornowski.  Mapping and correction of the CMM workspace error with the use of an electronic gyroscope and neural networks--practical application. , 2013, Scanning.

[34]  Michael L. Overton,et al.  Numerical Computing with IEEE Floating Point Arithmetic , 2001 .

[35]  Kathleen V. Diegert,et al.  Error and uncertainty in modeling and simulation , 2002, Reliab. Eng. Syst. Saf..

[36]  A. Forbes.  Least-squares best-fit geometric elements. , 1991 .