A Review Paper on Assessing an Effective Approach for Adjusting Casting Process Parameters through Optimization
DOI:
https://doi.org/10.32628/IJSRMMEKeywords:
Casting process, Casting defects, Probabilistic Approach, Markov Chain ModelAbstract
Foundries in India are facing lower productivity issues due to low quality of castings produced as well as wrong practices followed, therein. Minimizing rejection as well as production costs is the key area of concern in these foundries. Casting process parameters are important in the optimization of casting process. Design optimization using reliability has also emerged as a promising tool for optimization, using the probabilistic approach. This approach works on probability of failures through various parameters. In this paper we focus on the development of a Markov chain model for investigating the casting failure. In the study, several casting defects and parameters was carried out. This study we proposed a hierarchical model that focuses on the casting failure in industries and accordingly the model was tested for various process parameters.
References
Gupta, A.K., Kumar, S., Chandna, P. and Bhushan, G., 2021. Optimization of process parameters during pressure die casting of A380: a silicon-based aluminum alloy using GA & fuzzy methodology. Silicon, 13, pp.2429-2443.
Hore, S., Das, S.K., Humane, M.M. and Peethala, A.K., 2019. Neural network modelling to characterize steel continuous casting process parameters and prediction of casting defects. Transactions of the Indian Institute of Metals, 72, pp.3015-3025.
Adithiyaa, T., Chandramohan, D. and Sathish, T., 2020. Optimal prediction of process parameters by GWO-KNN in stirring-squeeze casting of AA2219 reinforced metal matrix composites. Materials Today: Proceedings, 21, pp.1000-1007.
Karthik, A., Karunanithi, R., Srinivasan, S.A. and Prashanth, M., 2020. The optimization of squeeze casting process parameter for AA2219 alloy by using the Taguchi method. Materials Today: Proceedings, 27, pp.2556-2561.
Parekh, P.V. and Vadher, J.A., 2015. Optimization of Green Sand Casting Process parameters by Taguchi Method & Artificial Neural Network. International Journal of Modern Engineering Research, 5(5), pp.6-12.
Shahria, S., Tariquzzaman, M., Rahman, M.H., Al Amin, M. and Rahman, M.A., 2017. Optimization of molding sand composition for casting Al alloy. Int. J. of Mech. Eng. and Application, 5(3), pp.155-161.
Guharaja, S., Noorul Haq, A. and Karuppannan, K.M., 2006. Optimization of green sand casting process parameters by using Taguchi’s method. The International Journal of Advanced Manufacturing Technology, 30, pp.1040-1048.
Kumar, J.Y., Amirthagadeswaran, K.S. and Gowrishankar, S., 2015a. Casting process optimization for reducing the cold shut defect in castings using response surface methodology.
Upadhye, R.A. and Keswani, I.P., 2012. Optimization of sand casting process parameter using Taguchi method in foundry. International Journal of Engineering Research & Technology, 1(7), pp.2278-0181.
Joshua, J.J. and Andrews, A.A.E., 2020. Design of experiments to optimize casting process of aluminum alloy 7075 in addition of TiO2 using Taguchi method. Materials Today: Proceedings, 33, pp.3353-3358.
Kumaravadivel, A., Natarajan, U. and Ilamparithi, C., 2012. Determining the optimum green sand casting process parameters using Taguchi's method. Journal of the Chinese Institute of Industrial Engineers, 29(2), pp.148-162.
Manjunath Patel, G.C., Krishna, P. and Parappagoudar, M.B., 2016. Modelling and multiobjective optimization of squeeze casting process using regression analysis and genetic algorithm. Australian Journal of Mechanical Engineering, 14(3), pp.182-198.
Santhosh, A.J. and Lakshmanan, A.R., 2016. Investigation of ductile iron casting process parameters using Taguchi approach and response surface methodology. China Foundry, 13, pp.352-360.
Tiwari, S.K., Singh, R.K. and Srivastava, S.C., 2016. Optimization of green sand casting process parameters for enhancing quality of mild steel castings. International Journal of Productivity and Quality Management, 17(2), pp.127-141.
Zhang, L. and Wang, R., 2013. An intelligent system for low-pressure die-cast process parameters optimization. The International Journal of Advanced Manufacturing Technology, 65, pp.517-524.
Dabade, U.A. and Bhedasgaonkar, R.C., 2013. Casting defect analysis using design of experiments (DoE) and computer aided casting simulation technique. Procedia Cirp, 7, pp.616-621.
Rajkumar, I. and Rajini, N., 2021. Influence of parameters on the smart productivity of modern metal casting process: an overview. Materials Today: Proceedings, 47, pp.2694-2700.
Kumar, A., Ram, M. and Rawat, R.S., 2017. Optimization of casting process through reliability approach. International Journal of Quality & Reliability Management, 34(6), pp.833-848.
Golbabaei, F., Omidvar, M. and Nirumand, F., 2018. Risk assessment of heat stress using the AHP and TOPSIS methods in fuzzy environment-a case study in a foundry shop. Journal of Health and Safety at Work, 8(4), pp.397-408.
Arunachalam, R., Piya, S., Krishnan, P.K., Muraliraja, R., Christy, J.V., Mourad, A.H.I. and AlMaharbi, M., 2020. Optimization of stir–squeeze casting parameters for production of metal matrix composites using a hybrid analytical hierarchy process–Taguchi-Grey approach. Engineering Optimization, 52(7), pp.1166-1183.
Godoy, L.P., Dos Santos, A.V., Cardoso da Silva, D.J., Bohrz, S.R., Da Silva Wegner, R. and Casarin, V.A., 2018. Application of the Fuzzy-AHP method in the optimization of production of concrete blocks with addition of casting sand. Journal of Intelligent & Fuzzy Systems, 35(3), pp.3477-3491.
Chadha, U., Selvaraj, S.K., Pant, H., Arora, A., Shukla, D., Sancheti, I., Chadha, A., Srivastava, D., Khanna, M., Ram Kishore, S. and Paramasivam, V., 2022. Phase change materials in metal casting processes: a critical review and future possibilities. Advances in Materials Science and Engineering, 2022.
Li, J., Xu, X.W., Ren, N., Xia, M.X. and Li, J.G., 2022. A review on prediction of casting defects in steel ingots: from macrosegregation to multi-defect model. Journal of Iron and Steel Research International, 29(12), pp.1901-1914.
Olorunfemi, E.S. and David, A.O., 2022. Taguchi’s method to determine the casting parameters using optimization investment to maximize its mechanical properties. bw academic journal, pp.10-10.
Chelladurai, C., Mohan, N.S., Hariharashayee, D., Manikandan, S. and Sivaperumal, P., 2021. Analyzing the casting defects in small scale casting industry. Materials Today: Proceedings, 37, pp.386-394.
Kandpal, B.C., Johri, N., Kumar, B., Patel, A., Pachouri, P., Alam, M., Talwar, P., Sharma, M.K. and Sharma, S., 2021. Experimental study of foundry defects in aluminum castings for quality improvement of casting. Materials Today: Proceedings, 46, pp.10702-10706.
Sheikh, A.K. and Khan, M.A.A., 2020. Fatigue life prediction and reliability assessment of ductile iron castings using optimized mold design. The International Journal of Advanced Manufacturing Technology, 106, pp.1945-1966.
Solanki, M. and Desai, D., 2020. Competitive advantage through Six Sigma in sand casting industry to improve overall first-pass yield: a case study of SSE. International Journal of Lean Six Sigma, 12(3), pp.477-502.
Darko, A., Chan, A.P.C., Ameyaw, E.E., Owusu, E.K., Pärn, E. and Edwards, D.J., 2019. Review of application of analytic hierarchy process (AHP) in construction. International journal of construction management, 19(5), pp.436-452.
Khan, M.A.A., Sheikh, A.K., Al-Shaer, B.S., Khan, M.A.A., Sheikh, A.K. and Al-Shaer, B.S., 2017. Evolution of metal casting technologies—a historical perspective (pp. 1-43). Springer international publishing.
