Multi-Objective Optimization of Material Removal Characteristics for Robot Polishing of Ti-6Al-4V

TL;DR

This paper uses a multi-objective optimization algorithm to improve robotic polishing of Ti-6Al-4V by balancing surface quality and material removal rate.

Contribution

A novel application of MOPSO for optimizing robotic polishing parameters in Ti-6Al-4V processing.

Findings

Optimal parameters achieved 0.2197 mm3/s MRR and 0.291 μm Ra with low prediction errors.

A quadratic polynomial model accurately predicted surface roughness based on process parameters.

MOPSO successfully generated Pareto optimal solutions for dual objectives.

Abstract

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston’s equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model’s reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm3/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method’s effectiveness.

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