Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 115, Issue 11-12, Pages 3493-3511Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-021-07412-9
Keywords
Machining process route; Deep learning; Data representation; 3D convolution neuron network; Long short-term memory network
Funding
- National Science Foundation of China [51875474, 52075148]
- Equipment Pre-Research Domain Foundation of China [61409230102]
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This paper discusses a novel approach for process route generation based on deep learning. By using a fourth-order tensor model and encoder-decoder neural architecture, the automatic generation of machining process route for parts is achieved. The feasibility and effectiveness of the proposed method are demonstrated through experiments on slot cavity parts.
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.
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