Recognition of fail parts in hard disk drive manufacturing process: data mining approach

Abstract

In many manufacturing processes, some key process parameters have very strong relationship with the normal or various faulty products of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a data mining model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, an Apriori learning rules developed for monitoring the manufacturing process and recognizing faulty quality of the products being produced. In addition, this algorithm 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. Therefore, the data mining system provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to an assembly line in hard disk drive process, which improves the product quality and saves manufacturing cost.