Validation of a method using Taguchi,response surface,neural network,and genetic algorithm |
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| Affiliation: | 1. AGH University of Science and Technology, Department of Electronics, Krakow, Poland;2. Silicon Creations, 49 Highway 23 NE, Suwanee, GA 30024, USA;3. Singulus Technologies AG, 63796 Kahl am Main, Germany;4. INESC-MN and IN, 1000-029 Lisbon, Portugal;5. Physics Department, Instituto Superior Tecnico, Universidade de Lisboa, Portugal;6. INL-International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, 4715-330 Braga, Portugal;1. I.K.G. Punjab Technical University, Kapurthala 144601, India;2. Production Engineering Department, GNDEC, Ludhiana 141006, India;3. Mechanical Engineering Dept., MRSPTU, Bathinda-151001, India;1. University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11120 Belgrade 35, Serbia;2. Sorbonne Universités, Université de Technologie de Compiègne, FRE UTC-CNRS 2012, Laboratoire Roberval, Centre de Recherche de Royallieu, CS 60319, 60203 Compiègne cedex, France;3. Université de la Cote d′Opale et du Littoral Sud, Unité de Dynamique et Structure des Matériaux Moléculaires MREI, 145, avenue M. Schumann, F-59140 Dunkerque, France;4. IMMM, UMR CNRS 6283, Avenue Olivier Messiaen, 72085 Le Mans Cedex 9, France |
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| Abstract: | Piston is one of the important parts for aircraft engine, and the quality of piston affects the efficiency and safety of the engine. This study applies Taguchi method, response surface methodology (RSM), and back-propagation neural networks (BPNN) combining with genetic algorithm (GA) on the quality improvement of piston manufacturing processes to enhance the process yield. The Taguchi parameter design concerns three nominal-the-best specifications, including ring groove diameter specification, inner groove diameter specification, and inner diameter of pistons. Together with five control factors consisting of (1) type of carbon steel, (2) type of cutting fluid, (3) cutting depth, (4) spindle speed, and (5) chuck pressure, the L27(313) orthogonal array was selected for this experiment. Three models: (1) Taguchi model, (2) Taguchi_RSM model, and (3) Taguchi_BPNN_GA model were constructed to find the parameter combinations of five control factors for each model. Confirmation experiments were done for each model and the performances of three models were also compared to indict the enhancement of manufacturing quality of piston. |
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| Keywords: | Taguchi method Piston Back-propagation neural network Response surface methodology Genetic algorithm |
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