通过AP-BiLSTM-ATT通过组件的智能制造驱动的概率过程规划
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
概括
本研究介绍了基于双向长期短期记忆与注意力 (AP-BiLSTM-ATT) 算法的装配过程推理和决策,以增强智能制造. 这种新的方法提高了复杂部件的工艺规划准确性和效率.
科学领域:
- 智能制造 智能制造是一种智能制造.
- 在工程领域的人工智能.
- 过程规划优化过程规划优化
背景情况:
- 当前的流程规划严重依赖于人工专业知识,导致效率低下和响应时间缓慢.
- 现有的方法在知识的再利用和适应各种工程应用需求方面扎.
- 制造系统的智能性受到工艺规划质量和效率的重大影响,特别是对于复杂的部件.
研究的目的:
- 为组装过程推理和决策 (AP-BiLSTM-ATT) 开发一个智能算法,以解决传统过程规划中的局限性.
- 深入探索多维部件特征和过程计划之间的隐藏关系,用于概率决策.
- 提高智能制造中复杂零件工艺规划的智能化.
主要方法:
- 标记和向量化部分属性,几何特征和历史过程计划,以创建用于深度学习的结构化数据.
- 构建了一个双向长期短期记忆 (BiLSTM) 网络模型,与多头注意力机制集成.
- 在大规模的历史过程数据集上训练模型,以学习用于智能推理和推的特征过程映射.
主要成果:
- 与传统方法相比,AP-BiLSTM-ATT方法显示出更高的准确性,响应速度和概括能力.
- 该算法有效地捕获了特征之间的上下文依赖关系和语义权重分布.
- 成功实现了复杂部件过程决策的概率模型.
结论:
- 拟议的AP-BiLSTM-ATT算法为智能复杂零件流程规划提供了有效的支持.
- 这项工作为制造工艺知识的结构化表达和智能应用奠定了基础.
- 该方法提高了制造系统的整体智能水平.
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