基于关联规则的粗纱工序断头影响因素分析OACSTPCD
Influence factor analysis of roving process broken ends based on association rule
为了降低粗纱在生产过程中的断头率,提高纱线生产质量和效率,通过生产实践收集纺制C 14.6 tex和JC 24.3 tex两个品种的粗纱工序断头影响因素.使用K-means聚类算法对影响因素指标分别进行聚类,然后使用Apriori算法将聚类后的断头影响因素指标数据集进行关联规则挖掘.结果表明:纺制C 14.6 tex品种的粗纱工序断头影响因素关联规则有粗纱回潮率和末并定量湿重,粗纱条干CV和末并定量湿重,末并定量湿重和粗纱条干CV;纺制JC 24…查看全部>>
In order to reduce the breakage rate of roving during the production process and improve the quality and efficiency of yarn production,the influence factors of roving process broken ends of C 14.6 tex and JC 24.3 tex yarn were collected in production.The influencing factor indicators were clustered by K-means clustering algorithm,Apriori algorithm was used to mine the association rules of the clustered breakage influencing factor indicator datasets.The resul…查看全部>>
郑通;薛风洋;张立杰
新疆大学,新疆乌鲁木齐,830046新疆大学,新疆乌鲁木齐,830046新疆大学,新疆乌鲁木齐,830046
轻工业
粗纱工序断头影响因素数据挖掘K-means聚类关联规则Apriori算法
roving processinfluence factor of broken enddata miningK-means clusteringassociation ruleApriori algorithm
《棉纺织技术》 2024 (6)
22-26,5
新疆维吾尔自治区科技重大专项(2022A01008-1)新疆维吾尔自治区自然科学基金(2021D01C053)
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