Path Planning and Setup Orientation for Automated Dimensional Inspection Using Coordinate Measuring Machines

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.

Ontology Development for Knowledge Representation of a Metrology Lab

“…The digital transformation applied in the metrology field fosters the adoption of information technologies [41]. It supports the integration of sensor measurements to assess product and control quality [42], along with path planning for automated inspections [43]. In this sense, ontologies enable the knowledge representation of industries and measuring processes in quality inspection and control [44].…”

Free-form surface inspection path planning using improved ant colony optimisation algorithm

“…Lu et al [3] applied the genetic algorithms to path planning for multiple inspection targets and experimentally compared genetic algorithm models with traditional models, verifying that genetic algorithm inspection path optimisation improved the results by at least a quarter. Abdulhameed et al [4] applied an artificial neural network algorithm and a genetic algorithm to optimise the inspection path, reducing the length of the CMM inspection path by half and therefore improving the inspection efficiency of the CMM. Yi et al [5] proposed a comprehensive free-form path planning method, which applied an image processing algorithm, a measurement point classification algorithm, and a measurement point sequencing algorithm to significantly improve the inspection efficiency of the CMM compared to that of traditional inspection methods.…”

“…The genetic algorithm applied in [3] suffers from low operational efficiency and a tendency to converge prematurely. The artificial neural network algorithm applied in [4] suffers from the disadvantages of being prone to local optimal solutions and low operational efficiency. The ant colony optimization algorithm applied in [6][7][8][9][10] suffers from the disadvantages of being prone to local optimal solutions and high stochasticity.…”

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

“…There are three popular research trends for path optimization algorithms: genetic algorithms, ant colony algorithms, and discrete particle swarm algorithms. Osama et al [7] simultaneously applied an artificial neural network and genetic algorithms to their project. Tsagaris et al [8] compared the use of a genetic algorithm and the combination of a genetic algorithm and an ant colony algorithm.…”