Yarn engineering using hybrid artificial neural network-genetic algorithm model |
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Authors: | Subhasis Das Anindya Ghosh Abhijit Majumdar Debamalya Banerjee |
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Institution: | 3. Government College of Engineering & Textile Technology, Berhampore, 742 101, India 1. Department of Textile Technology, Indian Instutute of Technology, Delhi, 110 016, India 2. Department of Production Engineering, Jadavpur University, Kolkata, 700 032, India
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Abstract: | This work aims to manufacture cotton yarns with requisite quality by choice of suitable raw materials for a given spinning system. To fulfill this aim, a hybrid model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been developed which captures both the high prediction power of ANN and global solution searching ability of GA. In an attempt to achieve a yarn having predefined tenacity and evenness, a constrained optimization problem is formulated with the ANN input-output relation between fibre and yarn properties. GA has been used to solve the optimization problem by searching the best combination of fibre properties that can translate into reality a yarn with the desired quality. The model is capable in identifying the set of fibre properties that gives requisite yarn quality with reasonable degree of accuracy. |
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