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Cell Formation using Artificial Neural Network (ANN) by Meta Heuristics learning Algorithm (GA) for Cellular Manufacturing Systems (CMSS) with Multiple Objectives


Affiliations
1 Dept. of Mechanical Engineering, Arulmigu Kalasalingam College of Engineering, Anand Nagar, Krishnankoil - 626 190, India
2 Department of Production Engineering, Regional Engineering College, Tiruchirappalli - 620 015, India
     

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Cellular manufacturing system (CMS) is the application of Group Technology (GT) in which similar parts and machines are grouped into part families and machining cells. The success of the CMS depends on the efficiency in forming part families and manufacturing cells. In this work, a back propagation Artificial Neural Network (ANN) is developed for the above task. Learning (i.e.) adjustment of weight age to each connection in the network is incorporated using Genetic algorithm (GA). Multiple objectives (Exceptional elements, Intracell move. Intracell machine load unbalances. Intercell machine load unbalances) are considered to evaluate the goodness of the cell formation. The network is tested for different problem sizes.
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  • Cell Formation using Artificial Neural Network (ANN) by Meta Heuristics learning Algorithm (GA) for Cellular Manufacturing Systems (CMSS) with Multiple Objectives

Abstract Views: 216  |  PDF Views: 0

Authors

P. Venkumar
Dept. of Mechanical Engineering, Arulmigu Kalasalingam College of Engineering, Anand Nagar, Krishnankoil - 626 190, India
K. Chandra Sekar
Dept. of Mechanical Engineering, Arulmigu Kalasalingam College of Engineering, Anand Nagar, Krishnankoil - 626 190, India
R. Sudhakarapandian
Dept. of Mechanical Engineering, Arulmigu Kalasalingam College of Engineering, Anand Nagar, Krishnankoil - 626 190, India
A. Noorul Haq
Department of Production Engineering, Regional Engineering College, Tiruchirappalli - 620 015, India

Abstract


Cellular manufacturing system (CMS) is the application of Group Technology (GT) in which similar parts and machines are grouped into part families and machining cells. The success of the CMS depends on the efficiency in forming part families and manufacturing cells. In this work, a back propagation Artificial Neural Network (ANN) is developed for the above task. Learning (i.e.) adjustment of weight age to each connection in the network is incorporated using Genetic algorithm (GA). Multiple objectives (Exceptional elements, Intracell move. Intracell machine load unbalances. Intercell machine load unbalances) are considered to evaluate the goodness of the cell formation. The network is tested for different problem sizes.