A MACHINE LEARNING FRAMEWORK FOR DATA-DRIVEN DEFECT DETECTION IN MULTISTAGE MANUFACTURING SYSTEMS

Economic transformation and escalating market competitiveness have prompted manufacturers to adopt zero-defect manufacturing principles to lower production costs and maximise product quality. The key enabler of zero-defect manufacturing is the adoption of data-driven techniques that harness the wealth of information offered by digitalised manufacturing systems in order to predict errors. Multi-stage manufacturing systems, however, introduce additional complexity owing to the cascade effects associated with stage interactions. A generic modular framework is proposed for facilitating the tasks associated with preparing data emanating from multi-stage manufacturing systems, building predictive models, and interpreting these models' results. In particular, cascade quality prediction methods are employed to harness the benefit of invoking a stage-wise modelling approach. The working of the framework is demonstrated in a practical case study involving data from a multistage semiconductor production process.