ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicAI-Driven Architectures and Algorithms for Secure and Scalable Big Data SystemsView all 8 articles
Smart Manufacturing-Driven Probabilistic Process Planning for Components via AP-BiLSTM-ATT
Provisionally accepted
- Shenyang University of Technology, Shenyang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In the context of smart manufacturing, improving the quality and efficiency of process planning, especially in the processing of complex parts, has become a key factor influencing the level of intelligence in manufacturing systems. However, most current process planning methods still heavily rely on manual expertise, leading to problems such as difficulty in knowledge reuse, low planning efficiency, and slow response times, which are inadequate to meet the diverse and changing needs of engineering applications. To address these issues, this paper proposes an algorithm for Assembly Process Reasoning and Decision-making based on Bidirectional Long Short-Term Memory with Attention (AP-BiLSTM-ATT), which aims to deeply explore the hidden relationships between the multi-dimensional features of parts and process plans, thereby achieving probabilistic modeling of process decisions. Specifically, the attributes, geometric features, and historical process plans of parts are first labeled and vectorized, transforming traditional process knowledge into structured data representations suitable for deep learning models. A BiLSTM network model, integrated with a multi-head attention mechanism, is then constructed to capture contextual dependencies and semantic weight distributions between features, enhancing the model's ability to express complex process relationships. During training, the model learns the mapping distribution between features and processes from a large-scale historical process dataset, enabling intelligent reasoning and recommendation of process plans for new parts. The results show that this method outperforms traditional methods in terms of accuracy, response speed, and generalization ability in process planning, providing effective support for enhancing the intelligence of complex part process planning and laying a foundation for the structured expression and intelligent application of manufacturing process knowledge.
Keywords: AP-BiLSTM-ATT, Intelligent reasoning, Knowledgerepresentation, ProcessPlanning, SmartManufacturing
Received: 13 Nov 2025; Accepted: 18 Dec 2025.
Copyright: © 2025 Yang, Liang, Zhang and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiting Peng
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.