基于可视化破译聚类的机械零件单元形成 自组织映射

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摘要翻译：
在单元制造中，机器零件单元形成是为了加工更多的品种、质量、更低的工序水平，减少制造提前期和客户响应时间，同时保持新产品的灵活性。本文提出了一种获取机器单元和零件族的新方法。单元制造的基本问题是零件族和机器单元的形成。本文研究了人工智能中的一种无监督学习算法--自组织映射(SOM)方法，并将其作为一种可视化的机械零件细胞形成聚类工具。本文的目的是通过SOM颜色编码的可视化聚类和SOM映射节点的标记来聚类二进制机器零件矩阵，从而使零件族在该机器单元中进行处理。本文介绍了SOM的等值线、成分平面、主成分投影、散点图和直方图等方法，成功地实现了机械零件单元结构的可视化。文中还给出了该算法对一组成组技术问题的计算结果。提出的SOM方法产生的解决方案具有分组效率，至少与以前文献中报道的任何结果一样好，并提高了70%的问题的分组效率，发现对行业从业者和研究人员都非常有用。
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英文标题：
《Machine-Part cell formation through visual decipherable clustering of
  Self Organizing Map》
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作者：
Manojit Chattopadhyay (Pailan College of Management & Technology),
  Surajit Chattopadhyay (Pailan College of Management & Technology), Pranab K.
  Dan (West Bengal University of Technology)
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.
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PDF链接：
https://arxiv.org/pdf/1105.1247

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关键词：可视化 自组织 Intelligence Practitioner Presentation 分组 制造 machine 报道 formation