Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA

In many manufacturing processes, some key process parameters (i.e., system inputs) have very strong relationship with the categories (e.g., normal or various faulty products) of finished products (i.e., system outputs). The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, a knowledge-based artificial neural network (KBANN) is developed for monitoring the manufacturing process and recognizing faulty quality categories of the products being produced. In addition, a genetic algorithm (GA)-based rule extraction approach named GARule is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for diagnosis of the manufacturing process, provide guidelines on improving the product quality, and are used to construct KBANN. Therefore, the seamless integration of GARule and KBANN provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to a japing-line, which improves the product quality and saves manufacturing cost.