纺织学报 ›› 2018, Vol. 39 ›› Issue (06): 149-154.doi: 10.13475/j.fzxb.20170601706

• 管理与信息化 • 上一篇    下一篇

决策树算法在针织产品质量管理中的应用

  

  • 收稿日期:2017-06-05 修回日期:2018-03-05 出版日期:2018-06-15 发布日期:2018-06-15

Application of decision tree algorithm in quality management of knitting products

  • Received:2017-06-05 Revised:2018-03-05 Online:2018-06-15 Published:2018-06-15

摘要:

为解决针织企业传统质量管理方法只注重事后处理,缺乏科学的预防控制措施,采用决策树C 5.0 算法,通过对影响针织产品质量的多种关键因素,如原料、原料质量等级、产品、设备型号、环境温度与湿度、挡车工、班次等进行了探讨,建立了针织产品质量数据挖掘模型。利用此模型对某公司经过预处理后的8157 条质量数据进行数据挖掘。结果表明,按对坯布质量影响程度大小排序,各因素依次是:原料、原料质量等级、设备型号、环境温度与湿度、班次、挡车工、产品。依据此结果给出了确切的生产要素的分配方案,可帮助企业优化资源配置,以提高产品质量。

关键词: 针织产品, 质量管理, 决策树算法, 生产要素

Abstract:

In order to solve the problem of the conventional product quality management methods of knitting enterprise of only focusing on post-processing, lack of scientific pre-management measures. Using the decision tree C5.0 algorithm, a variety of dey factors influencing quality,such as raw materials, raw material quality level, products, equipment types, environment temperature and humidity, blockers, shifts, were discussed. The knitting product quality data mining model was established. By using this model, the data mining of 8 157 quality data of a company after filtering processing was carried out, the results show that the order of influence on the quality of the grey fabric is (from high to low):raw materials, raw material quality level, equipment models, environment temperature and huimidity, shifts, blockers and products. Based on this result, the exact distribution of production factors is given to hilp knitting enterprises optimize the resources allocation to improve the product quality.

Key words: knitted product, quality management, decision tree algorithm, production factor

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