Machining Process Planning Through Latent Variable Model Inversion

Abstract

Manufacturers are always exerting significant effort to improve the quality of machined parts by suitable choice of process parameters. Furthermore, there is a trend within industry to improve process performance and product quality through analyzing available historical data especially in chemical industry. This trend is driven by the need to reduce product development time and cost. The use of latent variable modeling using historical data has been proposed in the past for product design and quality improvement (C.M. Jaeckle and J.F. MacGregor) [23]. This paper outlines the application of such approach using Projection to Latent Structure (PLS) and its model inversion to facilitate the choice of cutting parameters for a desired surface roughness while maximizing the Metal Removal Rate (MRR). The approach is mainly based on using historical data readily available on most of factory platform and simulated through experiments conducted on three different milling machines under normal conditions (sharp tool and stable cut). The model inversion approach is formulated in an optimization problem using the latent space linear model with nonlinear constraint. The approach output solutions were validated with the results showing that the proposed technique can be used for process planning and quality improvement of machining data.