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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.

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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.

关键词:
在 AP-BiLSTM-ATT 里.智能推理 智能推理知识表示知识表示.过程规划 过程规划智能制造是智能制造的一种方式.

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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.