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.

作为零件整体制造过程中的核心加工工艺要素，加工工艺路线对提高最终制造质量起着重要作用。在目前的CAPP系统中，工艺路线的决策仍然依赖于人机交互，本质上依赖于人的智能。近十年来，深度学习技术架构逐步完善，为智能流程规划提供了新的使能技术。最近，一些研究人员将深度学习应用于处理路线决策。然而，由于数据表示和深度学习网络构建的挑战，这种有前途的解决方案仍处于起步阶段。为了解决这两个挑战，本文提出了一种基于深度学习的新型工艺路线生成方法。第一的，我们提出了一个四阶张量模型来表示零件的几何形状和技术要求。并构造关系矩阵来表示加工特征之间的关系。工艺路线表示为一组连续的单热向量。然后，我们构建了一个编码器-解码器神经架构来自动生成零件的加工工艺路线。基于3D卷积神经元网络的编码器将加工特征之间的几何关系、工艺要求和关系信息转换为更高层的向量表示，基于长短期记忆网络的解码器将此表示映射到工艺路线. 包括编码器和解码器在内的整个神经架构被联合训练，以最大化给定训练部分的目标过程路线的条件概率。最后，以槽腔零件为例，验证了所提方法的可行性和有效性。