事象因果を用いた製品の不良原因予測

抄録

In this paper, we propose a defect cause analysis method that can automatically derive possible causes without relying on the experience of engineers, and can extract causes difficult to predict. First, events and their causal relationships are modeled with Petri nets. In addition, an assembly model is introduced to represent the propagation of events between parts. Next, a method to predict causes of product defects using the causal relationship model and the assembly model is proposed. In the method, a product model including not only its functions but also its manufacturing process is created at first. Then, past event causal data related to events in the product model are combined to the model. After that, a defect cause prediction model could be generated by reversing the relationship between cause and effect. By using this model, a cause event of a product defect can be traced step by step. Thus, possible underlying causes of the product defect is automatically derived. Finally, we confirmed the usefulness of our proposed method by implemented a system and predicting causes of a defect of an electric drill.