Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm

Abstract

Purpose

The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.

Design/methodology/approach

In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.

Findings

The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.

Originality/value

The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

Citation

Tamilarasan, A., Renugambal, A. and Shunmugesh, K. (2023), "Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm", Multidiscipline Modeling in Materials and Structures, Vol. 19 No. 6, pp. 1199-1225. https://doi.org/10.1108/MMMS-11-2022-0254