Abstract
Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.
Highlights
Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity
Effective measurement of components using coordinate measuring machine (CMM) can only be done if the preceding steps are properly optimized to automate the whole inspection process. is paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan
A path planning model was established by Han et al [5] to minimize inspection time by estimating the detection direction of the probe. e ability of coordinate measuring machines (CMMs) to measure any part in a minimal amount of time is highly dependent on the development of an efficient inspection plan [6]. erefore, an inspection plan that can limit the number of component setups and probe orientations, as well as the length of the inspection path, should be adopted for measurements using CMM
Summary
Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. Erefore, an inspection plan that can limit the number of component setups and probe orientations, as well as the length of the inspection path, should be adopted for measurements using CMM. To achieve these objectives, Lu et al [7] applied a genetic algorithm- (GA-) based technique and generated a collision-free measurement route for component inspection using CMM. Hwang et al [6] developed a viable inspection strategy In their research, they adopted a greedy heuristic approach and minimized the part setups and probe adjustments. According to Moroni and Petro [10], any measurement can be associated with cost, where inspection costs are proportional to the time that is required to measure any given part
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