Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm.
Titanium alloy is known for its low thermal conductivity, small elastic modulus, and propensity for work hardening, posing challenges in predicting surface quality post high-speed milling. Since surface quality significantly influences wear resistance, fatigue strength, and corrosion resistance of p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310365 |
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| author | Nanqi Li ZuEn Shang Yang Zhao Hui Wang Qiyuan Min |
| author_facet | Nanqi Li ZuEn Shang Yang Zhao Hui Wang Qiyuan Min |
| author_sort | Nanqi Li |
| collection | DOAJ |
| description | Titanium alloy is known for its low thermal conductivity, small elastic modulus, and propensity for work hardening, posing challenges in predicting surface quality post high-speed milling. Since surface quality significantly influences wear resistance, fatigue strength, and corrosion resistance of parts, optimizing milling parameters becomes crucial for enhancing service performance. This paper proposes a milling parameter optimization method utilizing the snake algorithm with multi-strategy fusion to improve surface quality. The optimization objective is surface roughness. Initially, a prediction model for titanium alloy milling surface roughness is established using the response surface method to ensure continuous prediction. Subsequently, the snake algorithm with multi-strategy fusion is introduced. Population initialization employs an orthogonal matrix strategy, enhancing population diversity and distribution. A dynamic adaptive mechanism replaces the original static mechanism for optimizing food quantity and temperature, accelerating convergence. Joint reverse strategy aids in selecting and generating individuals with higher fitness, fortifying the algorithm against local optima. Experimental results across five benchmarks employing various optimization algorithms demonstrate the superiority of the MSSO algorithm in convergence speed and accuracy. Finally, the multi-strategy snake algorithm optimizes the objective equation, with milling parameter experiments revealing a 55.7 percent increase in surface roughness of Ti64 compared to pre-optimization levels. This highlights the effectiveness of the proposed method in enhancing surface quality. |
| format | Article |
| id | doaj-art-73f0afb6544042069af0bbb5c95e4e56 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-73f0afb6544042069af0bbb5c95e4e562025-08-20T02:13:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031036510.1371/journal.pone.0310365Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm.Nanqi LiZuEn ShangYang ZhaoHui WangQiyuan MinTitanium alloy is known for its low thermal conductivity, small elastic modulus, and propensity for work hardening, posing challenges in predicting surface quality post high-speed milling. Since surface quality significantly influences wear resistance, fatigue strength, and corrosion resistance of parts, optimizing milling parameters becomes crucial for enhancing service performance. This paper proposes a milling parameter optimization method utilizing the snake algorithm with multi-strategy fusion to improve surface quality. The optimization objective is surface roughness. Initially, a prediction model for titanium alloy milling surface roughness is established using the response surface method to ensure continuous prediction. Subsequently, the snake algorithm with multi-strategy fusion is introduced. Population initialization employs an orthogonal matrix strategy, enhancing population diversity and distribution. A dynamic adaptive mechanism replaces the original static mechanism for optimizing food quantity and temperature, accelerating convergence. Joint reverse strategy aids in selecting and generating individuals with higher fitness, fortifying the algorithm against local optima. Experimental results across five benchmarks employing various optimization algorithms demonstrate the superiority of the MSSO algorithm in convergence speed and accuracy. Finally, the multi-strategy snake algorithm optimizes the objective equation, with milling parameter experiments revealing a 55.7 percent increase in surface roughness of Ti64 compared to pre-optimization levels. This highlights the effectiveness of the proposed method in enhancing surface quality.https://doi.org/10.1371/journal.pone.0310365 |
| spellingShingle | Nanqi Li ZuEn Shang Yang Zhao Hui Wang Qiyuan Min Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. PLoS ONE |
| title | Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. |
| title_full | Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. |
| title_fullStr | Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. |
| title_full_unstemmed | Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. |
| title_short | Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm. |
| title_sort | optimization of surface roughness for titanium alloy based on multi strategy fusion snake algorithm |
| url | https://doi.org/10.1371/journal.pone.0310365 |
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