Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

The phenomenon of coordinate measuring machines has led to a significant improvement in accuracy, adaptability, and reliability for measurement jobs. The coordinate measuring machines with scanning capabilities provide the alternative to output precise acquisition at a faster rate. However, they are less accurate as compared to discrete probing systems and slower than the noncontact techniques. Therefore, the data acquisition using a scanning touch probe needs improvement, so that it can provide commendable performance both in terms of accuracy and scanning time. The determination of appropriate scanning parameters is crucial to minimize the inaccuracy and time associated with the scanning process. However, it can be demanding as well as unreliable owing to the presence of uncertainty from a multitude of factors that may influence the measurement process. The optimization of data acquisition using a scanning touch probe is a multiresponse process which involves definite uncertainties from various sources. Therefore, multioptimization tools based on grey relational analysis coupled with principal component analysis and fuzzy logic were employed to enhance the utilization of the scanning touch probe. The work described here has the objective to identify the appropriate combination of scanning factors which can simultaneously boost the accuracy and lessen the scanning time. This study demonstrates the capability and effectiveness of the uncertainty theory based optimization methods in coordinate metrology. It also suggests that the uncertainty associated with the parameter optimization can be significantly reduced using these techniques. It has also been noticed that the results from the two techniques are in accord, which corroborates their application in coordinate metrology. The result from this study can be applied to other probing systems and can be broadened to include more experiments and parameters in various scenarios as needed by the specific application.

[1]  Miguel Cervera,et al.  Experimental validation of an FSW model with an enhanced friction law: application to a threaded cylindrical pin tool , 2017 .

[2]  N Tosun,et al.  Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis , 2006 .

[3]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[4]  J. Lilly Mercy,et al.  Multi response optimisation of mechanical properties in self-healing glass fiber reinforced plastic using grey relational analysis , 2017 .

[5]  Kiyoshi Takamasu,et al.  Present problems in coordinate metrology for nano and micro scale measurements , 2011 .

[6]  Juchi Hou,et al.  Grey Relational Analysis Method for Multiple Attribute Decision Making in Intuitionistic Fuzzy Setting , 2010, J. Convergence Inf. Technol..

[7]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[8]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[9]  Zone-Ching Lin,et al.  Analysis and Application of Grey Relation and ANOVA in Chemical–Mechanical Polishing Process Parameters , 2003 .

[10]  K. Palanikumar,et al.  Analysis on Drilling of Glass Fiber–Reinforced Polymer (GFRP) Composites Using Grey Relational Analysis , 2012 .

[11]  Vinay Sharma,et al.  Taguchi-fuzzy based mapping of EDM-machinability of aluminium foam , 2011 .

[12]  Masaharu Mizumoto,et al.  Fuzzy controls under various fuzzy reasoning methods , 1988, Inf. Sci..

[13]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[14]  C.-X. J. Feng,et al.  Experimental study of the effect of digitizing parameters on digitizing uncertainty with a CMM , 2002 .

[15]  Dongzhou Jia,et al.  Experimental evaluation of MoS2 nanoparticles in jet MQL grinding with different types of vegetable oil as base oil , 2015 .

[16]  Luis Magdalena,et al.  Fuzzy Rule-Based Systems , 2015, Handbook of Computational Intelligence.

[17]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[18]  R. Ramanujam,et al.  Optimization of Machining Parameters Using Fuzzy Based Principal Component Analysis during dry turning operation of Inconel 625 – A hybrid approach , 2014 .

[19]  Dongzhou Jia,et al.  Process parameter optimization and experimental evaluation for nanofluid MQL in grinding Ti-6Al-4V based on grey relational analysis , 2018 .

[20]  Meenu,et al.  Grey relational analysis coupled with principal component analysis to optimize the machining process of ductile iron. , 2018 .

[21]  C. L. Lin,et al.  Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method , .

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

[23]  B. Bouchon-Meunier,et al.  ON THE CHOICE OF MEMBERSHIP FUNCTIONS IN A MAMDANI-TYPE FUZZY CONTROLLER , 2007 .

[24]  Arun Kumar Srirangan,et al.  Optimisation of laser welding parameters for incoloy 800HT joints using Grey-fuzzy Taguchi approach , 2018 .

[25]  Dongzhou Jia,et al.  Experimental evaluation of surface topographies of NMQL grinding ZrO2 ceramics combining multiangle ultrasonic vibration , 2018, The International Journal of Advanced Manufacturing Technology.

[26]  Abdulrahman Al-Ahmari,et al.  Accuracy of a reverse-engineered mould using contact and non-contact measurement techniques , 2015, Int. J. Comput. Integr. Manuf..

[27]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[28]  Arshad Noor Siddiquee,et al.  Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding , 2010 .

[29]  Yang Min,et al.  Experimental evaluation of the lubrication properties of the wheel/workpiece interface in MQL grinding with different nanofluids , 2016 .

[30]  B. Bose,et al.  Evaluation of membership functions for fuzzy logic controlled induction motor drive , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[31]  Enrico Savio,et al.  Metrology of freeform shaped parts , 2007 .

[32]  Wen-Shing Lee,et al.  Evaluating and ranking energy performance of office buildings using Grey relational analysis , 2011 .

[33]  Douglas C. Montgomery,et al.  Partial replication of small two-level factorial designs , 2017 .

[34]  N. C. Hwang,et al.  Grey relational analysis coupled with principal component analysis for optimization design of the cutting parameters in high-speed end milling , 2009 .

[35]  Arshad Noor Siddiquee,et al.  An Investigation of the Micro-Electrical Discharge Machining of Nickel-Titanium Shape Memory Alloy Using Grey Relations Coupled with Principal Component Analysis , 2017 .

[36]  Gary C. Lin,et al.  Design and analysis of experiments in CMM measurement uncertainty study , 2007 .

[37]  Amit Kumar Singh,et al.  Optimization of morphological process parameters in contactless laser scanning system using modified particle swarm algorithm , 2017 .

[38]  Antonio Piratelli-Filho,et al.  CMM uncertainty analysis with factorial design , 2003 .

[39]  K. Palanikumar,et al.  Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the taguchi method with fuzzy logics , 2006 .

[40]  Shan-Jen Cheng,et al.  Investigating the effects of operational factors on PEMFC performance based on CFD simulations using a three-level full-factorial design , 2012 .

[41]  Robert G. Wilhelm,et al.  Task Specific Uncertainty in Coordinate Measurement , 2001 .

[42]  P. Torino,et al.  Reverse Engineering : Selective Sampling Acquisition Approach , 2022 .

[43]  Ming-Chang Jeng,et al.  Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis , 2009 .

[44]  Enrico Vezzetti,et al.  Reverse engineering: a selective sampling acquisition approach , 2007 .

[45]  Ahmet Taskesen,et al.  Experimental investigation and multi-objective analysis on drilling of boron carbide reinforced metal matrix composites using grey relational analysis , 2014 .

[46]  Marjan Korosec,et al.  Process modeling of non-contact reverse engineering process , 2007 .

[47]  Reza Tavakkoli-Moghaddam,et al.  An Improved Hybrid Grey Relational Analysis Approach for Green Resilient Supply Chain Network Assessment , 2017 .