A deep learning-based approach for machining process route generation

As the core machining process element in the overall manufacturing process of a part, the machining process route plays an important role in improving final manufacturing quality. In the current CAPP system, the decision-making of the process route still depends on human-computer interaction and essentially depends on human intelligence. In the past decade, deep learning technology architecture has been gradually improved, which provides a new enabling technology for intelligent process planning. Recently, some researchers have applied deep learning to process route decision-making. However, due to the challenges of data representation and deep learning network construction, this promising solution is still at infancy. To address the two challenges, this paper presents a novel process route generation approach based on deep learning. First, we propose a fourth-order tensor model to represent the geometry and technological requirements of a part. And the relation matrix is constructed to represent the relationships among machining features. The process route is represented as a sequential set of one-hot vectors. Then, we construct an encoder-decoder neural architecture to automatically generate the machining process route for the part. The 3D convolution neuron network-based encoder converts the geometry, technological requirements, and the information of the relationships among machining features into a higher layer of vector representation, and the long short-term memory network-based decoder maps this representation to the process route. The whole neural architecture including the encoder and decoder is jointly trained to maximize the conditional probability of the target process route given the training part. Finally, the paper takes slot cavity parts as examples to verify the feasibility and effectiveness of the proposed approach.