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针对企业在制造装备设计时，选型困难，已有信息利用不足等问题。本文提出了一种挖掘历史订单信息并联合二分K-Means聚类的方法，结合三维CAD软件二次开发出了制造装备智能优选系统。通过数据挖掘技术结合历史订单信息、实时物料信息等，对历史选型数据预处理方法开展了研究，实现了历史方案数据信息的K-Means聚类，采用多种相似度距离进行模型优选，最终实现制造装备的快速设计，以某制麦产线用输送设备为例，验证了方法的有效性。
Abstract：To address issues faced by enterprises during the design of manufacturing equipment, such as difficulties in model selection and insufficient utilization of existing information, this paper proposes a method that mines historical order information and combines it with binary K-Means clustering. Furthermore, an intelligent optimal selection system for manufacturing equipment is developed through the secondary development of 3D CAD software.By integrating data mining technology with historical order information, real-time material information, and other relevant data, research is conducted on preprocessing methods for historical model selection data. This enables the implementation of K-Means clustering on historical scheme data information. Multiple similarity distance metrics are employed for model optimization, ultimately achieving rapid design of manufacturing equipment.Taking the conveying equipment used in a certain wheat processing production line as an example, the effectiveness of the proposed method is verified.
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基本信息:
DOI：10.16640/j.cnki.37-1222/t.2026.01.004
中图分类号:TP278
引用信息:
[1]崔家源,方喜峰,施雨雨.面向生产线的制造装备智能优选方法研究[J].山东工业技术,2026,No.327(01):23-29.DOI:10.16640/j.cnki.37-1222/t.2026.01.004.
基金信息:
镇江市重点研发计划项目(GY2020007)
2025-10-20
2025
2025-11-06
2026-01-04
2026
1
2026-02-15
2026-02-15