Optimal robot spray painting process parameters are specified using a modified Taguchi approach in multi-objective optimization

Abstract

Improvements in quality, productivity, and environmental cleanliness can be achieved through the application of robot spray painting processes, while also minimizing labor and costs. This technique finds application in various sectors including automobiles and home appliances. The enhancement of paint coating quality hinges on the specification of optimal spray painting process parameters. Performance indicators such as thickness variation, surface roughness, and film adhesion are considered crucial in this pursuit. In this context, a modified Taguchi approach is introduced, serving as a straightforward alternative to the Taguchi orthogonal array and gray rational analysis. The objective is to determine optimal spray painting process parameters (e.g., distance, pressure, and speed) that result in minimal thickness variation, reduced surface roughness, and maximized film adhesion. Empirical relations are established to predict thickness variation, surface roughness, and film adhesion. The acquired test data closely align with or fall within the anticipated range.