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The sampling strategy for CMM inspection processes is a property of the operator while the accuracy level is a property of the machine itself. The advancements in hardware technology over the last few years allowed for the production of a new generation of CMM machines that are capable of high-precision measurements, yet, the inspection quality of these machines are impaired by improper sampling strategy. This paper discusses the research work done on the development of a fuzzy logic based decision-making system as a means for soft computing for CMM sampling strategies. It also presents the use of fuzzy logic to relate the machine tool accuracy to the part measurement accuracy, and to make a knowledge data base which contains machine tool accuracy and part measurement data to be used for prediction of the sampling strategy for subsequent parts. Finally, at the end of the paper, system implementation, theoretical analysis, and experimental work are presented and discussed.
Keywords: CMM measurement, Fuzzy logic, Fuzzy Knowledge Based Control (FKBC), Sampling strategies
1 IntroductionEver since the introduction of Coordinate Measuring Machines (CMMs), there has always been debate on the determination of proper sampling strategies (sampling size and distribution) and the accuracy level or uncertainty level. The sampling strategy is a property of the operator i.e. different operators might use different strategies to measure the same part while the accuracy level is a property of the machine itself. The advancements in hardware technology over the last few years allowed for the production of a new generation of CMM machines that are capable of high-precision measurements which have earned them popularity over traditional hard gauging equipment. However, the inspection quality can be impaired by an inappropriate data analysis technique or an improper sampling strategy. Therefore, there is a need for automatic determination of the sampling strategy based on proper data analysis.
The advancement of computer technology has led to establishment of highly sophisticated data acquisition and analysis systems. An outcome of this technology is the decision making systems. These systems are software systems that can be developed to carry out intelligent decision based on data collected during an experiment. The decision making system architecture is indeed a multi-dimensional problem that has to be tackled carefully. In order to apply this technology to CMMs, the quantitative estimation of CMM measurement error, evaluated with uncertainty as suggested by NIST, which could be critical to make the exact accept/reject decision of a machined part. In general, we are faced with attempting to measure a part feature or true position using CMM. A sampling strategy and fitting algorithm have to be adopted prior to completing this task. Eventually, uncertainty theory and estimation technique are expected to be used to give an estimation of the accuracy of the results. In production coordinate measurement using CMMs, however, due to the limitations in speed of most machines, much more limited sampling, i=2Ee., under-sampling, is desirable. Therefore difficulties are introduced to the decision-making methodology of CMM sampling strategy, algorithms and uncertainty estimation because of the following factors:
=B7 The measurement system (CMM) contains systematic and random errors.
=B7 The feature deviates from ideal over a range of wavelengths and amplitudes that are representative of manufacturing processes. These deviations are usually unknown prior to accomplishing an error-free measurement which is believed impossible from metrology point of view.
=B7 Algorithms are used to fit these measurement data that can never be completely tested and, in some cases, are quite sensitive to "outliers" [Orady, 1996].
=B7 In production lines, efficiency is a major consideration. It would be required to measure a part feature as quickly as possible, i=2Ee., with the minimum number of points. At the same time, the measurement accuracy has to be controlled within an acceptable level.
Unfortunately, it is the CMM operator who faces these difficulties. As a matter of fact, one can not expect him to deal with all these difficulties because of his limited knowledge of coordinate metrology and inability to relate the measurement to the accuracy of the manufacturing process. This human-originated decision-making procedure is recognized as one of the major uncertainty factors to the practical CMM measurement in quality control.