Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN)
| Autor: | Mohamed Athmane Yallese, Mourad Nouioua, Mohamed Lamine Bouhalais, Riad Khettabi, François Girardin, Salim Belhadi |
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| Přispěvatelé: | Laboratoire de Mécanique des Structures (LMS), Guelma University, materiaux, surfaces et mise en forme, Laboratoire de Mecanique et Structures (LMS), Université du 8 Mai 1945 [Guelma, Algérie]-Université du 8 Mai 1945 [Guelma, Algérie], Laboratoire Vibrations Acoustique (LVA), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Optimization
0209 industrial biotechnology Engineering drawing Engineering 02 engineering and technology Industrial and Manufacturing Engineering Root mean square chemistry.chemical_compound 020901 industrial engineering & automation 0203 mechanical engineering Machining Tungsten carbide Cutting force Surface roughness Response surface methodology MQL Artificial neural network business.industry Mechanical Engineering RSM [SPI.MECA.VIBR]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Vibrations [physics.class-ph] Structural engineering Computer Science Applications 020303 mechanical engineering & transports chemistry Tangential force Control and Systems Engineering Green process business ANN Software |
| Zdroj: | International Journal of Advanced Manufacturing Technology International Journal of Advanced Manufacturing Technology, Springer Verlag, 2017, 93 (5-8), pp.2485-2504. ⟨10.1007/s00170-017-0589-2⟩ |
| ISSN: | 0268-3768 1433-3015 |
| DOI: | 10.1007/s00170-017-0589-2⟩ |
| Popis: | International audience; In this approach, response surface methodology (RSM) and artificial neural network (ANN) techniques were used in order to search for optimal prediction of uncontrollable machining factors that leads to better machining performance. The experiment has been established using 3 levels and 4 factors Box-Behnken design (BBD) for tangential force and surface roughness measurements according to combinations of cutting speed, feed rate, and cutting depth using multilayer-coated tungsten carbide insert with various nose radius in turning of X210Cr12 steel under dry, wet, and MQL machining. Consequently, it could be possible to investigate the efficiency of MQL technique for an environment-friendly ecological machining. Then, a comparative between ANN and RSM models has been established to determine the best approach according to model accuracy and capability for predicting surface roughness and cutting force. The ANN method provides more accurate results and proved its effectiveness as soon as its correlation coefficients, mean prediction errors (MPEs), and root mean square errors are rather small compared to those obtained by the RSM method. |
| Databáze: | OpenAIRE |
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