Applied Soft Computing ( IF 5.472 ) Pub Date : 2021-01-09 , DOI: 10.1016/j.asoc.2020.107075 K. Balaji; M. Siva Kumar; N. Yuvaraj
This novel study deals with the investigation of abrasive mixtures on abrasive water jet (AWJ) drilling of stainless steel 304 using multi objective soft computing techniques. In this study, the drilling parameters such as abrasive mixture, stand-off distance and feed rate were varied. The abrasive mixture was prepared with the composition of different types of abrasives such as silicon carbide & garnet, and aluminium oxide & garnet, and different mixture ratios. This mixing ratio was done based on the total mass of the abrasive mixture. The effect of abrasive water jet drilling parameters was examined on the hole features such as the hole diameter, circularity, cylindricity, and surface roughness. Multi objective optimization and algorithm techniques were employed in this study, namely Taguchi–Grey Relational Analysis (TGRA) and Krill Herd Algorithm (KHA). In this research work, the performance of KHA method was also compared with another recent metaheuristic technique i.e. grey wolf optimization (GWO) based on the quality measurement tools such as Spacing and Inverted generational distance. For this approach, the main parameters of metaheuristics algorithms were tuned using a robust design approach to acquire the best feasible solution. Besides, different multiple linear regression model equations were established to determine the best model for the KHA method based on the similarity between experimental and calculated attributes. With the assistance of these approaches, it is found that the abrasive mixtures have improved the performance of the AWJ drilling process in SS 304 rather than the use of a single type abrasive such as 100% Garnet. The results of this study proved that the KHA optimization technique is successfully utilized to find the best configuration parameter setting for AWJ drilling process, and that results are found to be efficient than the TGRA. To validate the predicted results of KHA, confirmation test was conducted. The results of the confirmation test showed that the predicted hole features of KHA were acceptable as that the error deviation was found as less than 2% with the experimental results. It is also noticed that the computational time and the selected quality metrics of KHA are found to be lower than the GWO method. Hence, it is confirmed that a new metaheuristic algorithm namely, KHA was found suitable for AWJ drilling process. The outcome of the present work explores a new paradigm to the AWJ machining to improve performance features in various operations.
这项新颖的研究涉及使用多目标软计算技术对不锈钢304的磨料水射流（AWJ）钻孔进行磨料混合物的研究。在这项研究中，钻削参数，例如磨料混合物，间隔距离和进给速度均发生了变化。制备的磨料混合物具有不同类型的磨料（例如碳化硅和石榴石，氧化铝和石榴石）以及不同的混合比。基于磨料混合物的总质量完成该混合比。考察了水射流打磨参数对孔特征的影响，例如孔直径，圆度，圆柱度和表面粗糙度。本研究采用了多目标优化和算法技术，即Taguchi-Grey关系分析（TGRA）和Krill牛群算法（KHA）。在这项研究工作中，还将KHA方法的性能与另一种新的启发式技术（即基于间隔和反向世代距离的质量测量工具）的灰太狼优化（GWO）进行了比较。对于此方法，使用健壮的设计方法调整元启发式算法的主要参数，以获得最佳可行的解决方案。此外，根据实验和计算属性之间的相似性，建立了不同的多元线性回归模型方程式，以确定KHA方法的最佳模型。在这些方法的帮助下，已经发现，磨料混合物改善了SS 304中的AWJ钻孔工艺的性能，而不是使用诸如100％石榴石的单一类型的磨料。这项研究的结果证明，KHA优化技术已成功用于AWJ钻井过程的最佳配置参数设置，并且发现结果比TGRA更为有效。为了验证KHA的预测结果，进行了确认测试。确认测试的结果表明，预测的KHA孔特征是可以接受的，因为与实验结果相比，误差偏差小于2％。还应注意，发现KHA的计算时间和所选质量指标低于GWO方法。因此，证实了一种新的元启发式算法，即 发现KHA适用于AWJ钻孔工艺。本工作的成果探索了AWJ加工的新范例，以改善各种操作中的性能特征。