对线切割机床性能指标的新型统计调查：优化、微观结构和机械性能

IF 1.8 4区工程技术 Q3 ENGINEERING, MECHANICAL

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science Pub Date : 2024-09-11 DOI:10.1177/09544062241272465

Soutrik Bose

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引用次数: 0

摘要

在加工混合钛基复合材料（TMC）时，通过改变功率（P）、峰值电流（IP）和关闭时间（Toff）等关键输入参数，对线材放电加工（WEDM）的响应进行了性能比较分析。开发了两种新型多目标优化算法，即理想多目标遗传算法（DMOGA）和理想多目标巨型太平洋章鱼优化器（DMOGPOO），用于解决曲柄和凸轮轴中的汽车气门销、航空螺旋桨和生物医学植入物等许多行业中的各种问题。与其他算法相比，DMOGA 的主要优势在于准确性和鲁棒性。该算法的新颖之处在于，它通过迭代的方式不断增加高效率的 "大样本集"，即根据适应度函数进行群体聚集。在评估 MOO 问题时，许多技术经常会遇到不合格的解决方案，而不是解决目标中帕累托最优解的适当近似函数。DMOGPOO 是一种令人着迷的统计方法，它模仿章鱼的捕食行为，表现优于其他多目标优化（MOO）算法，其中理想的目标函数可使用 MOGPOO 在 python 中获取。在多目标觅食环境中，档案被用来模仿章鱼的捕食行为和建立社会等级制度。DMOGPOO 方法采用多目标公式设计，以保持和保证在所有目标中增强最优解的覆盖范围。对材料去除率（MRR）、表面粗糙度（SR）、切口宽度（KW）和超切（OC）进行了实验研究。DMOGA 的综合可取性为 0.716，在提出 DMOGPOO 时提高到 0.813。与 DMOGA 相比，DMOGPOO 的 MOO 提高了 13.547%，MRR 为 3.81 mm3/min，SR 为 0.79 µm，KW 为 0.349 mm，OC 为 0.099 mm，综合可取性为 0.813。使用 DMOGPOO 时，优化集得到改善。MRR 提高了 5.54%，SR 提高了 75.95%，KW 提高了 0.29%，OC 提高了 4.21%。

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Novel statistical investigation on performance measures of WEDM: Optimization, microstructure and mechanical properties

A comparative performance analysis has been investigated on wire electrical discharge machining (WEDM) responses while machining a hybrid titanium matrix composite (TMC) varying the key input parameters like power (P), peak current (IP) and time-off (Toff). Two novel multi-objective optimization algorithms are developed namely desirable multi-objective genetic algorithm (DMOGA) and desirable multi-objective giant pacific octopus optimizer (DMOGPOO) for tackling various issues in many industries like automobile valve pins in crank and cam shafts, aerospace propeller and biomedical implants. The principal advantage of DMOGA to other algorithm is accuracy and robustness. The novelty fits in the iterative progression of growth of efficient grandee set, uttered as population congregating to a fitness function. Many techniques frequently encounter substandard solutions when evaluating MOO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. DMOGPOO is an enthralling statistical method which mimics the octopus’s predatory behavior, performs better than other multi-objective optimization (MOO) algorithms, where the desirable objective functions is fetched in python using MOGPOO. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. DMOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all goals. Experimental investigation is accepted on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Combined desirability in case of DMOGA is 0.716 which improved to 0.813 when DMOGPOO is proposed. MOO is improved with DMOGPOO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm3/min, SR of 0.79 µm, KW of 0.349 mm, OC of 0.099 mm, and combined desirability of 0.813. Improved optimality set is obtained when DMOGPOO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29%, and OC is 4.21%.

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来源期刊

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 工程技术-工程：机械

CiteScore

3.80

自引率

10.00%

发文量

625

审稿时长

4.3 months

期刊介绍： The Journal of Mechanical Engineering Science advances the understanding of both the fundamentals of engineering science and its application to the solution of challenges and problems in engineering.