An effective process design intent inference method of process data via integrating deep learning and grammar parsing

Abstract

With the constantly generation of process data in industries, process knowledge reuse and learning from process dataset has shown promising potential in machining process planning. However, existing methods cannot effectively extract different granularity process intents, and thus the process data cannot be effectively utilized. In this paper, an effective process design intent inference method of process data via integrating deep learning and grammar parsing is proposed. First, based on the projection of cutter location segments, an association approach between machining operations and machining features is proposed to extract the process intent of each machining operation. Then, a deep learning based working step process intent sequence decision-making model is proposed to estimate the probability distributions of alternative working step process intents at each time. Moreover, according to the working step process intent sequences of existing parts in an industry, the process knowledge And-Or graph (PK-AOG) is constructed by introducing the probabilistic grammar graph model. Finally, the optimal process intents of each working step are jointly obtained based on grammar parsing by taking the PK-AOG as a guide. In the experiments, the effectiveness of our approach is validated by developing a prototype system, and the results show that our approach is practical in process intents extraction.