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Smart manufacturing-driven probabilistic process planning for components via AP-BiLSTM-ATT.

Wei Yang1, Jinyan Liang2, Xiaoyu Zhang3

  • 1School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China.

Frontiers in Artificial Intelligence
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Assembly Process Reasoning and Decision-making based on Bidirectional Long Short-Term Memory with Attention (AP-BiLSTM-ATT) algorithm to enhance smart manufacturing. The novel approach improves process planning accuracy and efficiency for complex parts.

Keywords:
AP-BiLSTM-ATTintelligent reasoningknowledge representationprocess planningsmart manufacturing

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Area of Science:

  • Smart Manufacturing
  • Artificial Intelligence in Engineering
  • Process Planning Optimization

Background:

  • Current process planning relies heavily on manual expertise, leading to inefficiencies and slow response times.
  • Existing methods struggle with knowledge reuse and adapting to diverse engineering application needs.
  • The intelligence of manufacturing systems is significantly influenced by process planning quality and efficiency, especially for complex parts.

Purpose of the Study:

  • To develop an intelligent algorithm for Assembly Process Reasoning and Decision-making (AP-BiLSTM-ATT) to address limitations in traditional process planning.
  • To deeply explore hidden relationships between multi-dimensional part features and process plans for probabilistic decision-making.
  • To enhance the intelligence of complex part process planning in smart manufacturing.

Main Methods:

  • Labeled and vectorized part attributes, geometric features, and historical process plans to create structured data for deep learning.
  • Constructed a Bidirectional Long Short-Term Memory (BiLSTM) network model integrated with a multi-head attention mechanism.
  • Trained the model on a large-scale historical process dataset to learn feature-process mapping for intelligent reasoning and recommendation.

Main Results:

  • The AP-BiLSTM-ATT method demonstrated superior accuracy, response speed, and generalization ability compared to traditional methods.
  • The algorithm effectively captures contextual dependencies and semantic weight distributions between features.
  • Successfully achieved probabilistic modeling of process decisions for complex parts.

Conclusions:

  • The proposed AP-BiLSTM-ATT algorithm provides effective support for intelligent complex part process planning.
  • This work lays a foundation for the structured expression and intelligent application of manufacturing process knowledge.
  • The method enhances the overall intelligence level of manufacturing systems.