Automatic Inspection Planning Method for Complex Surfaces on a Contact-Scanning Coordinate Measuring Machine
The growing use of complex surfaces in various industries has intensified the demand for accurate and efficient surface quality measurements. Contact-scanning inspection on the Coordinate Measurement Machine (CMM) has become one of the key technologies for measuring complex surfaces with high precision. However, developing an automatic inspection plan that balances adaptability, safety, and efficiency remains challenging. This paper introduces an automatic inspection planning method that uses two key strategies: the Sawtooth adaptive Sampling (SS) strategy and the probing Orientation & Path planning (OP) strategy. The SS strategy is used to distribute sawtooth scanning lines both globally and locally based on multiple surface information. The OP strategy is used to plan probing orientations for each scanning line, accounting for potential interferences. To optimize local and global inspection paths, we propose an improved ant colony optimization that incorporates interference costs. A series of experiments verifies that the proposed method reduces the total inspection time by 14.8% and unnecessary transition distances by more than 46.8%, while maintaining the highest sampling accuracy and avoiding potential interference. This contribution enhances the inspection efficiency of complex surfaces and further improves manufacturing capability.
Authors
- Sitong Wang (ORCID: https://orcid.org/0000-0003-2493-4038)
- Gaiyun He (ORCID: https://orcid.org/0000-0002-5799-0418)
- Yichen Yan (ORCID: https://orcid.org/0000-0001-9667-4867)
- Yue Yang (ORCID: https://orcid.org/0000-0002-5922-8697)
- Chenyu Wu (ORCID: https://orcid.org/0009-0006-4263-7167)
- Zhi Tan (ORCID: https://orcid.org/0000-0001-6310-5019)
Institutions
- Tianjin University (CN)
Publication Details
- Journal
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tim.2025.3602580
- Citations
- 1
- Primary Topic
- Manufacturing Process and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 1.24