...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Quality Control, Transactions >An ELM-Embedded Deep Learning Based Intelligent Recognition System for Computer Numeric Control Machine Tools
【24h】

An ELM-Embedded Deep Learning Based Intelligent Recognition System for Computer Numeric Control Machine Tools

机译：基于ELM嵌入的计算机数字控制机床智能识别系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In modern manufacturing industry featured with automation and flexibility, the intelligent tool management for Computer Numeric Control (CNC) machine plays an essential role in manufacturing automation. The automatic tool recognition in terms of geometric shapes, materials and usage functions could facilitate the seamless integration with downstream process planning and scheduling processes. In this paper, a intelligent tool recognition system is proposed with a novel hybrid framework of multi-channel deep learning network with non-iterative and fast feedforward neural network to meet high efficiency and accuracy requirement in intelligent manufacturing. The combination of the fine-tuning Convolutional Neural Networks (CNNs) with the random parameter assignment mechanism of Extreme Learning Machines (ELMs) reach a balance in accurate feature extraction and fast recognition. In the proposed hybrid framework, features extracted from efficient CNNs are aggregated into robust ELM auto-encoders (ELM-AEs) to generate the compact but rich feature information, which are then feed to the subsequent single layer ELM network for tool recognition. The performance of proposed framework is verified on several standardized 3D shape retrieval and classification dataset, as well as on a self-constructed multi-view 3D data represented tool library database. Numerical experiments reveal a promising application perspective of proposed intelligent recognition system on manufacturing automation.
机译：在现代制造业以自动化和灵活性特色，计算机数字控制（CNC）机器的智能工具管理在制造自动化中起重要作用。在几何形状，材料和使用功能方面，自动工具识别可以促进与下游过程规划和调度过程的无缝集成。在本文中，提出了一种具有非迭代和快速前馈神经网络的多通道深度学习网络的新型混合框架，以满足智能制造的高效率和准确性要求的新型混合骨架识别系统。微调卷积神经网络（CNNS）的组合具有极端学习机（ELMS）的随机参数分配机制（ELMS）的准确特征提取和快速识别达到平衡。在所提出的混合框架中，从高效CNN中提取的特征被聚合成鲁棒ELM自动编码器（ELM-AES）以生成紧凑但丰富的特征信息，然后将其馈送到后续单层ELM网络以进行工具识别。在若干标准化的3D形式检索和分类数据集中验证了所提出的框架的性能，以及自建的多视图3D数据表示的工具库数据库。数值实验揭示了建议制造自动化智能识别系统的有前途的应用视角。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号