LAPSE:2024.0861
Published Article
LAPSE:2024.0861
Adaptation of Symbolic Discrete Control Synthesis for Energy-Efficient Multi-Pocket Milling
June 7, 2024
In engineering, cost minimization, especially in Computer Numerical Control (CNC) machining like pocket milling, is crucial. Existing tool path definition software often lacks optimization, particularly at critical starting and ending points. This study optimizes CNC machine tool paths for energy-efficient multi-pocket milling, utilizing the Symbolic Discrete Control Synthesis (SDCS) method for formal correctness. In our work, the tool path generation is formulated as a traveling salesman problem. We introduce a modeling framework to adapt SDCS to multi-pocket-milling processes, aiming to enhance precision and efficiency for potential cost savings, including energy and time, in engineering applications. This study reports experimental and comparative results, where comparative evaluations were made using metaheuristic algorithms. Our proposed approach improves CNC machining processes for multi-pocket milling. We experimentally evaluate our control algorithms and demonstrate and validate our approach through case studies.
Keywords
computer numerical control machine, Energy Efficiency, multi-pocket milling, optimization algorithms, symbolic discrete controller synthesis
Suggested Citation
Çaşka S, Özbaltan M. Adaptation of Symbolic Discrete Control Synthesis for Energy-Efficient Multi-Pocket Milling. (2024). LAPSE:2024.0861
Author Affiliations
Çaşka S: Department of Mechanical Engineering, Hasan Ferdi Turgutlu Technology Faculty, Manisa Celal Bayar University, 45400 Manisa, Türkiye [ORCID]
Özbaltan M: Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, 25050 Erzurum, Türkiye [ORCID]
Journal Name
Processes
Volume
12
Issue
3
First Page
584
Year
2024
Publication Date
2024-03-14
ISSN
2227-9717
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Original Submission
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PII: pr12030584, Publication Type: Journal Article
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LAPSE:2024.0861
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https://doi.org/10.3390/pr12030584
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Jun 7, 2024
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