An effective NC machining process planning method via integrating grammar knowledge with deep learning

As a vast number of process data composed of three-dimensional (3D) computer-aided design (CAD) model and its associated machining process are generated each year, data-driven machining process planning is becoming an effective strategy for engineers to generate the process scheme of a part with less time and lower cost. However, variety of knowledge in these data have not been analyzed, learned and used effectively, and thus the numerical control (NC) process scheme of the part still relies on the skilled engineers. In this work, we propose an effective NC machining process planning method via integrating grammar knowledge with deep learning. First, the process knowledge And-Or graph (PK-AOG) is defined through the use of a context-free grammar for its compositional properties, and it forms the solution space for the working step sequence of a part, which is a parsing graph of PK-AOG essentially. Then, the implicit mapping mode between each machining feature and its feature process label are learned through the use of deep learning method based on attention mechanism for the structured process data, and the probability distributions of different feature process labels are calculated. Finally, taking PK-AOG as guidance, the optimal process scheme for a part, which are composed of working step sequence, its compatible feature processes, and the machining process of each machining feature, is jointly searched via integrating ant colony optimization (ACO) and genetic algorithm (GA) effectively, and thus a process scheme of a part with correct logics and semantics is generated. The proposed approach is verified by the experiments on the developed prototype system based on computer aided three-dimensional interactive application (CATIA) software.