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首页> 外文期刊>International Journal of Advancements in Technology >Solving Component Family Identification Problems on Manufacturing Shop Floor
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Solving Component Family Identification Problems on Manufacturing Shop Floor

机译：解决生产车间中的组件族识别问题

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This article demonstrates effective techniques for component/part family formation problem in the vicinity of Cellular Manufacturing Systems (CMS). Past investigations reported that part family formation techniques are typically grounded on production flow analysis (PFA) which largely considers operational requirements, sequences and time. Part coding analysis (PCA) is merely counted in cellular manufacturing which is assumed to be the most competent method to identify the part families. In present study different clustering techniques are quantified to develop proficient part families by utilizing Opitz part coding scheme and the techniques are tested on 5 different datasets of size (5×9) to (27×9) and the obtained results are compared with each other. The experimental results reported that the C-Linkage method is more effective in terms of the quality of the solution obtained, has outperformed SLCA and K-means techniques.
机译：本文演示了在蜂窝制造系统（CMS）附近解决部件/零件族形成问题的有效技术。过去的调查报告说，零件族的形成技术通常基于生产流程分析（PFA），该流程主要考虑了操作要求，顺序和时间。零件编码分析（PCA）仅在蜂窝制造中进行计算，这被认为是识别零件家族的最有效方法。在本研究中，利用Opitz零件编码方案对不同的聚类技术进行量化，以开发出熟练的零件族，并在5个大小从（5×9）到（27×9）的不同数据集上对该技术进行了测试，并将获得的结果进行了比较。 。实验结果表明，C-Linkage方法在获得的溶液质量方面更有效，其性能优于SLCA和K-means技术。

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