Abstract
Today’s dies and molds making industries demand for milling of hardened tool steel with less production time and requirement of excellent quality. AISI D2 tool steel has attained high hardness and excellent corrosion resistance at elevated temperature. The experiments were conducted on hardened D2 tool steel using AlCrN coated end mill tool based on response surface methodology (RSM). Central composite design (CCD) method was implemented to design 30 experiments. Analysis of variance (ANOVA) was used to verify adequacy of model. The derived model is utilized to analyze and interaction effect of the input milling parameters with surface roughness. Genetic algorithm was used to optimize of machining parameters to obtain best surface quality on AISI D2 tool steel during milling process. Lowest surface roughness was measured 0.08 after applying genetic algorithm. Optimized value from genetic algorithm was also validated experimentally.
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References
Durakbasa, M.N., Akdogan, A., Vanli, A.S., Bulutsuz, A.G.: Optimization of end milling parameters and determination of the effects of edge profile for high surface quality of AISI H13 steel by using precise and fast measurements. Measurement 68, 92–99 (2015)
Bergmann, B., Denkena, B., Kohler, J.: Development of cutting edge geometries for hard milling operations. CIRP J. Manuf. Sci. Technol. 8, 43–52 (2015)
Abrão, A.M., José, L.S.R.: Milling of annealed and hardened tool steel with coated carbide and cermet inserts. Int. Congr. Mech. Eng. (2007)
Liew, W.Y.H.: Low-speed milling of stainless steel with TiAlN single-layer and TiAlN/AlCrN nano-multilayer coated carbide tools under different lubrication conditions. Wear 269(7–8), 617–631 (2010)
Veldhuis, S., Kovalev, A.I., Wainstein, D.L., Rashkovskiy, A.Y., Fox-rabinovich, G.S., Yamamoto, K.: Impact of Al and Cr alloying in TiN-based PVD coatings on cutting performance during machining of hard to cut materials. Vaccum 84(1), 184–187 (2010)
Yueh-jaw Lin, A.A., Fang, Y.: Wear progressions and tool life enhancement with AlCrN coated inserts in high-speed dry and wet steel lathing. Wear 264, 226–234 (2008)
Le Bourhis, E., Goudeau, P., Staia, M.H., Carrasquero, E., Puchi-cabrera, E.S.: Mechanical properties of hard AlCrN-based coated substrates. Surf. Coat. Technol. 203(19), 2961–2968 (2009)
Parkinson, R., Fox-rabinovich, G.S., Beake, B.D., Endrino, J.L., Veldhuis, S.C.: Effect of mechanical properties measured at room and elevated temperatures on the wear resistance of cutting tools with TiAlN and AlCrN coatings. Surf. Coat. Technol. 200, 5738–5742 (2006)
Kalss, J.L.E.W., Reiter, A., Derflinger, V., Gey, C.: Modern coatings in high performance cutting applications. Int. J. Refract. Metals Hard Mater. 24, 399–404 (2006)
Aslan, E., Camuscu, N.: A comparative study on cutting tool performance in end milling of AISI D3 tool steel. J. Mater. Process. Technol. 170, 121–126 (2005)
Rao, M.S., Venkaiah, N.: Parametric optimization in machining of Nimonic-263 alloy using RSM and particle swarm optimization. Procedia Mater. Sci. 10, 70–79 (2015)
Saad, N.H., Saedon, J.B., Soo, S.L., Aspinwall, D.K., Barnacle, A.: Prediction and optimization of tool life in micromilling AISI D2 (~62 HRC) hardened steel. Procedia Eng. 41, 1674–1683 (2012)
Rodrigues, A.R., Brandao, L.C., Coelho, R.T.: Experimental and theoretical study of workpiece temperature when end milling hardened steels using (TiAl) N-coated and PcBN-tipped tools. J. Mater. Process. Technol. 199, 234–244 (2008)
Gaitonde, V.N., Karnik, S.R., Maciel, C.H.A., Rubio, J.C.C., Abrão, A.M.: Machinability evaluation in hard milling of AISI D2 steel. Mater. Res. 19(2), 360–369 (2016)
Palanisamy, P., Rajendran, I., Shanmugasundaram, S.: Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int. J. Adv. Manuf. Technol. 32, 644–655 (2007)
Kumar, J.S., Reddy, B.S., Reddy, K.V.K.: Optimization of surface roughness in CNC end milling using response surface methodology and genetic algorithm. Int. J. Eng. Sci. Technol. 3(8), 102–109 (2011)
Alrashdan, A., Bataineh, O., Shbool, M.: Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm. Int. J. Adv. Manuf. Technol. (2014)
Sharma, S.: Optimization of machining process parameters for surface roughness of Al-composites. J. Inst. Eng. India Ser. C 94, 327–333 (2013)
Loftus, M., Fan, X.: The influence of cutting force on surface machining quality. Int. J. Prod. Res. 45(4), 899–911 (2007)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization And Machine Learning. Addison-Wesley Publisher, New York (1989)
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Patel, R.D., Bhavsar, S.N. (2020). Optimization of Machining Parameters to Minimize Surface Roughness During End Milling of AISI D2 Tool Steel Using Genetic Algorithm. In: Li, L., Pratihar, D., Chakrabarty, S., Mishra, P. (eds) Advances in Materials and Manufacturing Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-1307-7_25
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DOI: https://doi.org/10.1007/978-981-15-1307-7_25
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