Improvement of process conditions in acrylic fiber dyeing using gray-based Taguchi-neural network approach

Author:

Zeydan Mithat,Yazıcı Deniz

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference24 articles.

1. Kuo CC, Pietras S (2010) Applying regression analysis to improve dyeing process quality: a case study. Int J Adv Manuf Technol 49:357–368

2. Ravikumar K, Krishnan S, Ramalingam S, Balu K (2006) Optimization of process variables by the application of response surface methodology to optimize the process variables for reactive red and acid brown dye removal using a novel adsorbent. Dyes Pigm 72:66–74

3. Kuo CFJ, Chang CD, Su TL, Fu CT (2008) Optimization of the dyeing process and prediction of quality characteristics on elastic fiber blending fabrics. Polym Plast Technol Eng 47(7):678–687

4. Hench KW, Al-Ghanim AM (1995) The application of a neural network methodology to the analysis of a dyeing operation. In: ANNIE ‘95: artificial neural networks in engineering, St. Louis, MO (United States), pp 873–878

5. Köksal G (1992) Robust design of batch dyeing process. Degree of Doctor of Philosophy, Department of Industrial Engineering, Graduate Faculty of North Carolina State University, Raleigh, NC

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