Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization
- 233 Downloads
This paper emphasizes on the development of a combined study of surface roughness for modeling and optimization of cutting parameters for keyway milling operation of C40 steel under wet condition. Spindle speed, feed, and depth of cut are considered as input parameters and surface roughness (Ra) is selected as output parameter. Surface roughness model is developed by both artificial neural networks (ANN) and response surface methodology (RSM). ANOVA analysis is performed to determine the effect of process parameters on the response. Back-propagation algorithm based on Levenberg-Marquardt (LM) and gradient descent (GDX) methods is used separately to train the neural network and results obtained from the two methods are compared. It is found that network trained by the LM algorithm gives better result. ANN model (trained by the LM algorithm) is coupled with genetic algorithm (GA) and RS model is further interfaced with the GA and particle swarm optimization (PSO) to optimize the cutting conditions that lead to minimum surface roughness. It is found that RSM coupled with PSO gives better result and the result is validated by confirmation test. Good agreement is observed between the predicted Ra value and experimental Ra value for RSM-PSO technique.
KeywordsSurface roughness Keyway milling Artificial neural network Genetic algorithm Particle swarm optimization
Unable to display preview. Download preview PDF.
The authors acknowledge the kind support and cooperation provided by the technical staffs of the workshop in IIEST, Shibpur. The author sincerely thanks Mr. Tapan Kumar Das, workshop inspector, IIEST, Shibpur, for his kind help during experimentation.
- 2.Bhandari VB (2007) Design of machine elements. Tata McGraw Hill Publishing Company Ltd., New DelhiGoogle Scholar
- 3.https://ec.kamandirect.com/content/resources/2010/downloads/falk_metric_key_keyway.pdf. Accessed 05 July 2017
- 16.Jeyakumar S, Marimuthu K, Ramachandran T (2015) Optimization of machining parameters of Al6061 composite to minimize the surface roughness–modelling using RSM and ANN. Indian J Eng Mater Sci 22:29–37Google Scholar
- 20.Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway NJ:1942–1948Google Scholar
- 27.Gupta MK, Sood PK, Sharma VS (2016) Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum quantity lubrication environment. Mater Manuf Process 31(13):1671–1682. https://doi.org/10.1080/10426914.2015.1117632 CrossRefGoogle Scholar
- 29.Malghan RL, Rao KMC, Shettigar AK, Rao SS, Souza RJD (2016) Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. J Braz Soc Mech Sci Eng 1–13Google Scholar
- 31.Selaimia AA, Yallese MA, Bensouilah H, Meddour I, Khattabi R, Mabrouki T (2017) Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach. Meas J Int Meas Confed 107:53–67. https://doi.org/10.1016/j.measurement.2017.05.012 CrossRefGoogle Scholar
- 36.Khalid HH, Ghulam Z, Raza MB, Khalil S (2016) Optimization of process parameters for high speed machining of Ti-6Al-4V using response surface methodology. Int J Adv Manuf Technol 85(5–8):1847–1856Google Scholar
- 37.Mousavi SM, Hajipour V, Niaki STA, Alikar N (2013) Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: two calibrated meta-heuristic algorithms. Appl Math Model 37(4):2241–2256. https://doi.org/10.1016/j.apm.2012.05.019 MathSciNetzbMATHCrossRefGoogle Scholar
- 38.David LC (1997) Genetic algorithms. University of Illinois, ChampaignGoogle Scholar
- 43.Mousavi SM, Hajipour V, Niaki ST, Aalikar N (2014) A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm. Int J Adv Manuf Technol 70(9–12):1739–1756. https://doi.org/10.1007/s00170-013-5378-y CrossRefGoogle Scholar
- 51.Pathak L, Singh V, Niwas R, Osama K, Khan S, Haque S, Tripathi CK, Mishra BN (2015) Artificial intelligence versus statistical modeling and optimization of cholesterol oxidase production by using Streptomyces Sp. PLoS One 10(9):e0137268. https://doi.org/10.1371/journal.pone.0137268 CrossRefGoogle Scholar