Data-centric real-time integration of diagnostics, prognostics, and process quality control : realizing intelligent manufacturing

Abstract

The systematic and easy to use tool condition monitoring systems and integrated methods developed in this thesis are radically essential for manufacturing industries. At its most fundamental level, it enables ‘data-centric real-time integration of diagnostics, prognostics, and process quality control, to realize a holistic view of intelligent manufacturing to machinists, thereby forming the basis for building an autonomous decision-support system and serves as a guide for joint consideration of critical strategic operational policies and several additional progressions in the contemporary state-of-the-art. More specifically, the thesis resulted in the following contributions:a) A methodology for dynamic optimisation of process quality control and maintenance planning, considering the real-time health state of the system is formulated and experimentally validated. b) Solved one of the standing and non-trivial problems of literature viz. prognostics (predicting remaining useful life) under dynamic operating profiles. The proposed generic prognostic approach encompasses all real-world industrial scenarios. c) A novel integrated diagnostics and prognostics system based on the relationship between product quality and tool degradation is proposed and validated. In essence, the outcomes of the research in this thesis advances the existing body of knowledge by developing an autonomous decision-support system and methods for systematic expansion of intelligent manufacturing in diverse real-world production environments.