Measurement Optimization from CAD Using Artificial Intelligence

Conducting measurements is a daily and time-consuming process that is critical to the manufacturing industry. The most widespread way to carry out the measuring process is using a Coordinate Measuring Machine (CMM). In this paper, a methodology is presented to accelerate the measuring procedure by optimally programming a CMM. The proposed methodology utilizes the information from a computer-aided design (CAD) file and the capabilities of CMMs in order to optimize the measurement process. An improved artificial fish swarm algorithm was modified to meet the requirements of the measurement process and the capabilities of the CMMs. In addition, the ant colony optimization method is applied to extract the optimal sequence of measurements throughout the multiple areas on the component. The resulting optimal path also utilizes the free areas between the different manufactured features of the component. Finally, the resulting path is collision-free, ensuring the integrity and the safety of the CMM. The proposed methodology is verified through real-world experiments.

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Publication Details

Journal
Metrology
Published
2025-10-29
DOI
https://doi.org/10.3390/metrology5040064
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
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article

Measurement Optimization from CAD Using Artificial Intelligence

Michel Theodor Mansour, Gabriel Mansour, Ápostolos Tsagaris, Ilias Chouridis et al.
Metrology
Industrial Vision Systems and Defect Detection
article

Measurement Optimization from CAD Using Artificial Intelligence

Michel Theodor Mansour, Gabriel Mansour, Ápostolos Tsagaris, Ilias Chouridis, Vasileios Papageorgiou
article en

Abstract

Conducting measurements is a daily and time-consuming process that is critical to the manufacturing industry. The most widespread way to carry out the measuring process is using a Coordinate Measuring Machine (CMM). In this paper, a methodology is presented to accelerate the measuring procedure by optimally programming a CMM. The proposed methodology utilizes the information from a computer-aided design (CAD) file and the capabilities of CMMs in order to optimize the measurement process. An improved artificial fish swarm algorithm was modified to meet the requirements of the measurement process and the capabilities of the CMMs. In addition, the ant colony optimization method is applied to extract the optimal sequence of measurements throughout the multiple areas on the component. The resulting optimal path also utilizes the free areas between the different manufactured features of the component. Finally, the resulting path is collision-free, ensuring the integrity and the safety of the CMM. The proposed methodology is verified through real-world experiments.

MetrologyVol. 5(4)
International Hellenic University (GR), Aristotle University of Thessaloniki (GR)
Openalex Percentile: Top 56%
Industrial Vision Systems and Defect Detection
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