Abstract
The stringent customer demands and competitive market emphasize the importance of efficient and effective inspection in industrial metrology. Therefore, the implementation of an appropriate sampling strategy, i.e., the number of points and their distribution, has become very important in the inspection process using a coordinate measuring machine. Moreover, the quality of inspection results has frequently been influenced by sampling plan as well as workpiece size and surface characteristics. It has been an indispensable problem in the present-day measurement processes. Thus, this paper investigates various sample sizes and different point distribution algorithms that can be employed in the evaluation of form error. The effect of specimen size and surface quality on the sampling strategy has also been investigated. Furthermore, this work employs a fuzzy based Technique for Order Performance by Similarity to Ideal Solution approach to realize the best sampling strategy. The results have demonstrated the significance of robust optimization techniques as well as the importance of a suitable sampling strategy in coordinate metrology. This study has also established that Poisson point distribution achieved the best accuracy and the Grid point distribution had taken the least measurement time.
Highlights
Coordinate Measuring Machine (CMM) has turned up as a leading technology to accomplish inspection in manufacturing industries
The results have demonstrated the significance of robust optimization techniques as well as the importance of a suitable sampling strategy in coordinate metrology
The initial observation was that of closeness to actual value (CA) which depicted significant variation with the sample size and its value decreased as the number of points increased in the case of all regions and surfaces
Summary
Coordinate Measuring Machine (CMM) has turned up as a leading technology to accomplish inspection in manufacturing industries. The entire process of an automated CMM inspection begins with the gathering of dimensions and geometrical information from the computer-aided design (CAD). This knowledge from the CAD is utilized to generate a computer-aided inspection plan (CAIP), which provides an integration link between CAD and CMM. While devising the CMM inspection plan, it is crucial to consider important factors, which may influence the performance of the final part measurement. These factors may include sampling strategy, part positioning, and orientation, surface characteristics, probing system, and environmental conditions [6]. The sampling strategy can be considered as an essential parameter to accurately assess the tolerance information [9]
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