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使用混合遗传算法-响应曲面法和田口-灰色关联分析统计工具对AISI D2进行超精密立方氮化硼车削的优化

Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools.

作者信息

Tura Amanuel Diriba, Isaya Elly Ogutu, Adizue Ugonna Loveday, Farkas Balázs Zsolt, Takács Márton

机构信息

Budapest University of Technology and Economics, Faculty of Mechanical Engineering, Department of Manufacturing Science and Engineering, Budapest, Hungary.

Projects Development Institute (PRODA), Department of Engineering Research Development and Production, Enugu, Nigeria.

出版信息

Heliyon. 2024 May 23;10(11):e31849. doi: 10.1016/j.heliyon.2024.e31849. eCollection 2024 Jun 15.

Abstract

Ultra-precision turning is a crucial process in the manufacturing industry as it helps to produce parts with high dimensional accuracy, surface finish, and tolerance. The process is similar to traditional turning but is carried out under special circumstances to achieve greater precision and surface finish. The process can be applied to conventional structural materials, but the demand for machining hardened steels is increasing. The optimization of ultra-precision turning of AISI D2 using cubic boron nitride (CBN) tools is a crucial aspect in the field of high-quality machining. This study aims to evaluate the performance of the process and identify the optimal parameters that result in the best quality components while using a CBN tool's ultra-precision turning of AISI D2. Ultra-precision turning process factors such as cutting speed, feed, and depth of cut were experimentally investigated to enhance the response output, such as surface roughness and cutting force components. The full factorial experimental design was used for determining the process characteristics under different conditions, and experimental results were applied to search for the optimum response of machining performance. The optimization process was done by combining the hybrid genetic algorithm-response surface methodology (GA-RSM) and the Taguchi-grey relational analysis (GRA) statistical tools. These methods are useful in situations where the relationship between the input variables and the output responses is complex and non-linear. The results showed that a hybrid GA-RSM approach, combined with Taguchi-GRA statistical analysis, can effectively find optimal process parameters, leading to the best combination of surface roughness and cutting force. In hybrid Taguchi - GRA, the optimal cutting conditions were found to be a cutting speed of 175 m/min, a feed of 0.025 mm, and a depth of cut of 0.06 mm. The findings of this study provide valuable insights for the optimization of ultra-precision CBN turning operations, contribute to the development of precision manufacturing technology, and can be used as a reference for similar machining processes.

摘要

超精密车削是制造业中的关键工艺,因为它有助于生产具有高精度尺寸、表面光洁度和公差的零件。该工艺与传统车削类似,但在特殊条件下进行,以实现更高的精度和表面光洁度。该工艺可应用于传统结构材料,但对加工淬硬钢的需求正在增加。使用立方氮化硼(CBN)刀具对AISI D2进行超精密车削的优化是高质量加工领域的一个关键方面。本研究旨在评估该工艺的性能,并确定在使用CBN刀具对AISI D2进行超精密车削时能产生最佳质量零件的最佳参数。通过实验研究了切削速度、进给量和切削深度等超精密车削工艺因素,以提高诸如表面粗糙度和切削力分量等响应输出。采用全因子实验设计来确定不同条件下的工艺特性,并将实验结果应用于寻找加工性能的最佳响应。优化过程通过结合混合遗传算法-响应面法(GA-RSM)和田口-灰色关联分析(GRA)统计工具来完成。这些方法在输入变量和输出响应之间的关系复杂且非线性的情况下很有用。结果表明,结合田口-GRA统计分析的混合GA-RSM方法可以有效地找到最佳工艺参数,从而实现表面粗糙度和切削力的最佳组合。在混合田口-GRA中,最佳切削条件为切削速度175米/分钟、进给量0.025毫米和切削深度0.06毫米。本研究的结果为超精密CBN车削操作的优化提供了有价值的见解,有助于精密制造技术的发展,并可作为类似加工工艺的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77c8/11153245/5ca11bc1368e/ga1.jpg

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