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首页> 外文期刊>Composite Structures >Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-Ⅱ and fuzzy C-means
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Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-Ⅱ and fuzzy C-means

机译：结合ANN，NSGA-Ⅱ和模糊C均值的混合方法对CFRP钻井参数进行多目标优化

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摘要

A full factorial experiment is performed for the conventional dry drilling of CFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artificial neural network (ANN) is developed to express thrust force and delamination factor as a function of drilling parameters. Multi-objective optimization of drilling parameters is accomplished based on Non-dominated Sorting Genetic Algorithm (NSGA-II) with thrust force, delamination factor and material removal rate as optimization objectives, delamination factor also serves as a constraint. The Pareto front of drilling response variables determined by NSGA-II consists of a large number of non-dominated solutions. In order to facilitate the experimental verification of optimization results, fuzzy C-means clustering algorithm is used to narrow down the solutions on the front to several representative ones. Conformation tests are conducted and results show that the representative solutions can give satisfactory performance with achieving a trade-off among thrust force, exit-delamination and material removal rate.
机译：对于传统的CFRP干钻，以主轴速度，进给速度和尖角作为钻削参数进行了全因子实验，响应变量为推力和出口分层。开发了人工神经网络（ANN），以表示推力和分层因子随钻井参数的变化。基于非主导排序遗传算法（NSGA-II）实现了钻井参数的多目标优化，以推力，分层因子和材料去除率为优化目标，分层因子也作为约束条件。由NSGA-II确定的钻井响应变量的帕累托前沿由大量非支配性解决方案组成。为了便于对优化结果进行实验验证，使用模糊C均值聚类算法将前面的解缩小为几个有代表性的解。进行了构形试验，结果表明，代表性的解决方案在推力，出口分层和材料去除率之间进行权衡，可以提供令人满意的性能。

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