MSTMM‐Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm

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Autores principales: Adeel Shehzad, Yuanyuan Ding, Yu Chang, Yiheng Chen, Xiaoting Rui, Hanjing Lu
Formato: Artículo Open Access
Publicado: Wiley 2025
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author Adeel Shehzad
Yuanyuan Ding
Yu Chang
Yiheng Chen
Xiaoting Rui
Hanjing Lu
author_facet Adeel Shehzad
Yuanyuan Ding
Yu Chang
Yiheng Chen
Xiaoting Rui
Hanjing Lu
Adeel Shehzad
Yuanyuan Ding
Yu Chang
Yiheng Chen
Xiaoting Rui
Hanjing Lu
collection Wiley Open Access
contents MSTMM‐Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm Adeel Shehzad Yuanyuan Ding Yu Chang Yiheng Chen Xiaoting Rui Hanjing Lu International Journal of Mechanical System Dynamics ABSTRACTUltra‐precision machining (UPM) has been extensively employed for the production of high‐end precision components. The process is highly precise, and the associated cost of production is also high. Optimization of machining parameters in UPM can significantly improve machining efficiency and surface roughness. This study proposes an innovative approach that couples transfer matrix methods for multibody systems (MSTMM) and particle swarm optimization (PSO) to optimize the machining parameters, aiming to simultaneously improve the machining efficiency and surface roughness of UPM machined components. Initially, the dynamic model of an ultra‐precision fly‐cutting (UPFC) machine tool was developed using MSTMM and validated by machining tests. Subsequently, the PSO algorithm was employed to optimize the machining parameters. Based on the optimized parameters, a 40% reduction in machining time and an 18.6% improvement in surface roughness peak‐to‐valley (PV) value have been achieved. The proposed method and the optimized parameters were verified through simulations using the MSTMM model, resulting in a minimal error of only 0.9%. 10.1002/msd2.70013 http://creativecommons.org/licenses/by/4.0/
doi_str_mv 10.1002/msd2.70013
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id wiley_oa_10_1002_msd2_70013
institution Wiley Open Access
license_str_mv http://creativecommons.org/licenses/by/4.0/
publishDate 2025
publisher Wiley
record_format wiley_oa
spellingShingle MSTMM‐Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm
Adeel Shehzad
Yuanyuan Ding
Yu Chang
Yiheng Chen
Xiaoting Rui
Hanjing Lu
International Journal of Mechanical System Dynamics
MSTMM‐Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm Adeel Shehzad Yuanyuan Ding Yu Chang Yiheng Chen Xiaoting Rui Hanjing Lu International Journal of Mechanical System Dynamics ABSTRACTUltra‐precision machining (UPM) has been extensively employed for the production of high‐end precision components. The process is highly precise, and the associated cost of production is also high. Optimization of machining parameters in UPM can significantly improve machining efficiency and surface roughness. This study proposes an innovative approach that couples transfer matrix methods for multibody systems (MSTMM) and particle swarm optimization (PSO) to optimize the machining parameters, aiming to simultaneously improve the machining efficiency and surface roughness of UPM machined components. Initially, the dynamic model of an ultra‐precision fly‐cutting (UPFC) machine tool was developed using MSTMM and validated by machining tests. Subsequently, the PSO algorithm was employed to optimize the machining parameters. Based on the optimized parameters, a 40% reduction in machining time and an 18.6% improvement in surface roughness peak‐to‐valley (PV) value have been achieved. The proposed method and the optimized parameters were verified through simulations using the MSTMM model, resulting in a minimal error of only 0.9%. 10.1002/msd2.70013 http://creativecommons.org/licenses/by/4.0/
title MSTMM‐Validated Machining Efficiency and Surface Roughness Improvement Using Evolutionary Optimization Algorithm
topic International Journal of Mechanical System Dynamics
url https://onlinelibrary.wiley.com/doi/10.1002/msd2.70013