Intelligent Classification of the Drop Hammer Forming Process Method

Forging is a cost-effective way to produce net-shape or nearnet-shape components. Forged components are used throughout the manufacturing sector in many different applications. Owing to the lack of an accurate process model, modern forging operations still largely rely on operator skills and experience. The expertise of skilled operators can help to develop a forging process model. However, these operators’ expert knowledge is accumulated through years of hands-on experience and is often biased towards their own heuristic. It is well known that accurate acquisition of this type of knowledge is challenging and time-consuming. In this paper, an innovative approach is developed to acquire forging process knowledge automatically by combining learning ability of the neural networks with the structured knowledge representation of rule-based systems. The approach is applied to the classification of process methods used in a type of impression-die forging, namely, drop hammer forming. Specifically, process data from an aerospace company’s production facility are collected. The data are processed and then used to train a back-propagation neural network. By analysing the connections and weights of the trained neural network, concise and intelligible rules are extracted. These rules can be used to allow a clearer specification of the drop hammer forming process plan and to shorten learning curves for novice operators.