AI-enabled dynamic finish machining optimization for sustained surface integrity

Abstract

While machining processes are typically leveraged to establish geometric features, many functional characteristics of advanced materials are directly determined by their machining-induced quality, i.e. surface integrity. Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters. We present a novel integrated approach based on model-informed artificial intelligence (AI), which quickly and efficiently optimizes ‘dynamic’ process parameters. By maximizing the useful life of a cutting tool over which required quality parameters can be maintained, our paradigm will enable significantly more efficient processing of next-generation materials and components.