Title: Cellular manufacturing performance improvement using data mining techniques

Authors: Mahammad Aloudat, Ali K. Kamrani, Emad Abouel Nasr

Addresses: Department of Industrial Engineering, University of Houston, Houston, TX 77204-4009, USA. ' Department of Industrial Engineering, University of Houston, Houston, TX 77204-4009, USA. ' Department of Mechanical Engineering, Helwan University, Cairo, Egypt

Abstract: This paper examines cell quality performance improvement through the integration of data mining using Artificial Neural Network (ANN) techniques and cellular manufacturing. The aim of this paper is to study and predict the factors that impact quality product in cellular manufacturing, such as used material, material complexity, operation type (lathe, mill, thread, groove, bore, etc.), machine, machinist and quantity to improve cell performance. The outcome suggests improvement in the part processing sequence, machining process capability, formation of family products, design, the machine operator performance and work schedule in order to improve the machine performance in cellular manufacturing.

Keywords: data mining; cellular manufacturing; CMS; artificial neural networks; ANN; manufacturing cells; performance improvement; quality improvement; machine performance.

DOI: 10.1504/IJKMS.2008.019748

International Journal of Knowledge Management Studies, 2008 Vol.2 No.4, pp.387 - 405

Published online: 29 Jul 2008 *

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