Effective machining process planning method based on knowledge graph and deep learning

With the widespread application of digital manufacturing Systems, the amount of process data generated by manufacturing companies has been continuously increasing. To achieve effective reuse, learning and mining of exist-ing process data, a knowledge graph and deep learning-based approach for part machining process design was pro-posed. A process knowledge graph model based on features, parts, feature process plan and part processes was con-structed to achieve a structured multi-level representation of process data. On this basis, a BiLSTM +Attention deep learning model was developed to reveal the mapping patterns between parts and typical process plans, and a Seq2Seq+Attention deep learning model was developed to generate effective sequences of part process steps. A part process reasoning method based on the fusion probability of feature process plans and macro process sequences of parts was proposed, achieving effective generation of part process plans with complete process context. Finally, a prototype System was developed and validated using pin parts as an example to demonstrate the effectiveness of the proposed approach. © 2024 CIMS. All rights reserved.