Multi-objective parametric optimization for high surface quality and process efficiency in micro-grinding

Jun Wang,Tingwei Fei,Man Zhao,Xusheng Shi,Qiang Ye

doi:10.1177/0020294020944953

Abstract

In this study, for the selection of maximum material removal rate and minimum surface roughness [Formula: see text] in micro-grinding of aluminum alloy through multi-response optimization, two optimization approaches are proposed based on statistical analysis and genetic algorithm. The statistical analysis–based approach applies response surface methodology according to the analysis of variance to propose a mathematical model for [Formula: see text]. In addition, the individual desirability of material removal rate, [Formula: see text], and the global desirability function are calculated, and the inverse analysis is conducted to locate input setting giving maximum desirability function. The genetic algorithm–based approach uses the improved multi-objective particle swarm optimization with the experimental data trained by support vector machine. To demonstrate that the material microstructure is a significant parameter for material removal rate and [Formula: see text], the models with and without Taylor factor consideration are developed and compared. The optimized results achieved from both response surface methodology and improved multi-objective particle swarm optimization demonstrate that the consideration of Taylor factor can significantly improve the optimization process to achieve the maximum material removal rate and minimum [Formula: see text].

Highlights

Micro-grinding is a competitive finish machining method for better surface integrity including more compressive surface residual stress, decreased surface roughness, and smaller dimensional tolerance
To obtain the high quality of the machined surface and high efficiency in micro-grinding, the multi-response parametric optimizations are conducted in this investigation
To demonstrate the significance of Taylor factor to Ra and material removal rate (MRR), the optimization models with and without considering Taylor factor are developed through the response surface methodology (RSM) and improved multi-objective particle swarm optimization (IMOPSO) approaches, respectively

Summary

Introduction

Micro-grinding is a competitive finish machining method for better surface integrity including more compressive surface residual stress, decreased surface roughness, and smaller dimensional tolerance. Lu et al.[7] investigated the optimization of cutting parameters, including spindle speed, feed per tooth, and the depth of cut, for the maximum MRR and minimum surface roughness without cutter breakage in micro-milling. The desirability function (DF) was proposed to optimize the input parameters to lower surface roughness, increase MRR, and reduce cutting force as well as power. The optimization models with and without considering Taylor factor of material as input parameter are developed for MRR and surface roughness in micro-grinding alloy aluminum 7075 (AA7075). As the first optimization approach, RSM is applied to build the mathematical models, one of which is a function of surface speed, feed rate, the depth of cut, and Taylor factor for surface roughness; the other model uses the same parameters but without Taylor factor. The optimal results from both two approaches indicate that the models considering the Taylor factor improve the optimization process

Methodology

Experiments setup and procedure

Experimental setup and measurement condition

Conclusion