A novel approach for part family formation using K-means algorithm

• 作 者: Ashutosh Gupta P.K.Jain Dinesh Kumar
• 机 构: Department of Mechanical and Industrial Engineering,Indian Institute of Technology Roorkee,
• 出 处: Advances in Manufacturing
• 发表日期: 2013年03期
• 关键词: similarity hierarchical clustering grouping operations manufactured assumed near

The reconfigurable manufacturing system(RMS) is the next step in manufacturing,allowing the production of any quantity of highly customized and complex parts together with the benefits of mass production.In RMSs,parts are grouped into families,each of which requires a specific system configuration.Initially system is configured to produce the first family of parts.Once it is finished,the system will be reconfigured in order to produce the second family,and so forth.The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues.The aim of this work is to establish a methodology for grouping parts into families for effective working of RMS.The methodology carried out in two phases. In the first phase,the correlation matrix is used as similarity coefficient matrix.In the second phase,agglomerative hierarchical K-means algorithm is used for the parts family formation resulting in an optimum set of part families for reconfigurable manufacturing system.

The reconfigurable manufacturing system (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customized and complex parts together with the benefits of mass production.In RMSs, parts are grouped into families, each of which require a specific system configuration.Initially system is configured to produce the first family of parts. Once it is finished, the system will be reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part family addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of of. RMS. The methodology carried out in two phases. In the first phase, the correlation matrix is ​​used as similarity coefficient matrix .In the second phase, agglomerative hierarchical K-means algorithm is used for the parts family formation resulting in an optimum set of part families for reconf igurable manufacturing system.