基于模糊聚类K-means播种机焊接零件的编码分类

Encoding classification of welding parts of seeding machine based on fuzzy clustering K-means

焊接零件是精量播种装备的核心零部件,其生产计划直接影响播种机生产的进度和安排。播种机焊接零件的生产是典型的多品种、小批量生产模式,其生产过程中存在未考虑不同焊接零件间的相似度而导致作业转换频繁、生产作业时间过长等问题。为缩短生产作业时间,本文提出一种基于模糊聚类K-means的播种机焊接零件编码分类方法。首先对加工零件各属性进行分类描述,将不同属性再划分子属性;然后对各焊接零件所具属性进行编码,采用模糊聚类K-means算法进行分类;最后以典型的播种机焊接零件为例,验证了该方法的可行性和实用性。

Weldments are core components of precision sowing equipment, and the production plan directly affects planter production schedule and arrangements. Sowing machine welding parts production is a typical production mode of many varieties and small batch.The production process without considering the similarity between different welding results in frequent changeover, production operations and other issues too long. In order to shorten the production time, a new method based on fuzzy clustering K-means is proposed, which is based on the encoding classification method of the welding parts of the seeding machine. First on machining attributes for classification of the different attributes in are zoned the molecular properties, then each part of the attribute coding, are classified by fuzzy k-means clustering algorithm, and choose the typical welding parts of sowing machine as an example to verify the feasibility and practicability.