Abstract
The manufacturing sector in the modern era is striving hard to reduce the cost of production by employing innovative techniques. One such technique is hard turning where the workpiece is heat treated to the requisite hardness, and the final size and shape of the component are obtained directly through hard turning process. Hard turning is generally carried out with a huge amount of cutting fluid to enhance the output performance. Since petroleum-based emulsions are easily available in the market at reasonable price, they are widely used in industries. Petroleum-based cutting fluids create a number of environmental and health issues. In this perspective, pure dry turning is a logical substitute as it does not possess the harmful effects connected with the cutting fluids. The feasible tool life and surface quality are often disturbed while carrying out the machining operation under pure dry condition. Under such circumstances, the concept called minimal cutting fluid application (MCFA) performs itself as a possible solution. This paper investigates the effect of applying cutting fluid using MCFA technique at the critical contact zones while hard turning of H13 steel. An artificial neural network (ANN) model was developed for the prediction of the main cutting force, and its ability to predict cutting force (Fz) was analyzed. An effort is made to optimize the cutting parameters to accomplish minimum cutting force using genetic algorithm.
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References
Enabling manufacturing competitiveness and economic sustainability. In: Proceedings of the 4th international conference on changeable, agile, reconfigurable and virtual production (CARV2011), Montreal, Canada (2011)
Attanasio A, Gelfi M, Giardini C, Remino C (2006) Minimal quantity lubrication in turning: effect on tool wear. Wear 260(3):333–338
Aronson RB (1995) Why dry machining. Manuf Eng 33–36
NIOSH (2001) Metal working fluids—recommendation for chronic inhalation studies. National Institute for Occupational Safety and Health, Cincinnati, OH USA
Marano RS, Smolinnski JM, Esingulari CWM (1997) Polymer additives as mist suppressant in metal cutting fluids. J Soc Tribol Lubr Eng 25–32
Sam Paul P, Varadarajan AS (2013) Performance evaluation of hard turning of AISI 4340 steel with minimal fluid application in the presence of semi-solid lubricants. Proc Inst Mech Eng Part J J Eng Tribol 227(7):738–748
Itoigawa F, Childs THC, Nakamura T, Belluco W (2006) Effects and mechanisms in minimal quantity lubrication machining of aluminium alloy. Wear 260:339–344
Dhar NR, Kamruzzaman M, Ahmed M (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J Mater Process Technol 172:299–304
Varadarajan AS, Philip PK, Ramamoorthy B (2002) Investigations on hard turning with minimal cutting fluid application (HTMF) and its comparison with dry and wet turning. Int J Mach Tools Manuf 42:193–200
Robinson GR, Varadarajan AS (2012) Investigation on the effect of an auxiliary pulsing jet of cutting fluid on the top side of the chip during hard turning with minimal fluid application. Int J Mach Mach Mater 12:321–336
Chryssolouris G, Guillot M (1990) A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining. ASME J Eng Ind 112:122–131
Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479
Leo Dev Wins K, Varadarajan AS, Ramamoorthy B (2007) Artificial neural network assisted sensor fusion model for predicting tool wear online during hard turning. In: 9th international symposium on measurement and quality control, IIT Madras, pp 267–270
Bhoskar T, Kulkarni O, Kulkarni N (2015) Genetic algorithm and its applications to mechanical engineering: a review. Mater Today 2(4–5):2624–2630
Goldberg DE (2003) Genetic algorithm in search. Optimization and machine learning. Pearson Education Asia, New Delhi
Kaur M, Bhattacharya A, Singh M, Batish A (2014) Hard turning: parametric optimization using genetic algorithm for rough/finish machining and study of surface morphology. J Mech Sci Technol 28(5):1629–1640
Raj A, Leo Dev Wins K, Varadarajan AS (2016) Optimization of fluid application parameters during hard turning of AISI H13 tool steel using minimal cutting fluid application. Int J Res Mech Eng 4(3):190–196
Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural network; The state of the art. Int J Forecast 14:35–62
Mia M, Dhar NR (2017) Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Comput Appl 1–22
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Leo Dev Wins, K., Anuja Beatrice, B., Ebenezer Jacob Dhas, D.S., Anita Sofia, V.S. (2021). Artificial Neural Network and Genetic Algorithm-Based Models for Predicting Cutting Force in Turning of Hardened H13 Steel. In: Vijayan, S., Subramanian, N., Sankaranarayanasamy, K. (eds) Trends in Manufacturing and Engineering Management. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4745-4_56
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DOI: https://doi.org/10.1007/978-981-15-4745-4_56
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