Quality Improvement in Investment Castings Using Genetic Algorithm

Year 2021, Volume: 7 Issue: 3, 75 - 82, 30.09.2021

Hussam Abbas Amit Sata

https://doi.org/10.19072/ijet.938251

Abstract

Investment casting is well-known for its distinguished characteristics such as manufacturing small industrial components of ferrous as well as nonferrous alloys used in aerospace, automobile, bio-medical, chemical, defense, etc. with closed tolerances at relatively low cost. These industrial components need to be defect free as well as must possess desired mechanical properties. This quality metrics (defect free castings with desired mechanical properties) is mainly driven by process parameters associated with different sub-processes of investment casting including wax pattern making, shell making, dewaxing, melting & pouring, and chemical composition of alloys. It is always challenging to identify such parameters affecting quality of investment castings. In this work, an application of Genetic Algorithm has been extended to identify critical parameters and their specific set of values affecting quality of investment castings. This technique is found be very useful in performing data analytics.

Keywords

Investment Casting, Data Analytics, linear Regression, Genetic Algorithm

References

• Sata, A., & Ravi, B. (2019). Foundry data analytics to identify critical parameters affecting quality of investment castings. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering. 5(1): 011010.
• Sata, A., & Ravi, B. (2017). Bayesian inference-based investment-casting defect analysis system for industrial application. The International Journal of Advanced Manufacturing Technology, 90(9), 3301-3315.
• Kumar, M., Husain, M., Upreti, N., & Gupta, D. (2010). Genetic algorithm: Review and application. International Journal of Information Technology and Knowledge Management. Volume 2, No. 2, pp. 451-454.
• Tranmer, M., & Elliot, M. (2008). Multiple linear regression. The Cathie Marsh Centre for Census and Survey Research (CCSR), 5(5), 1-5.
• Yen, Y. S., Chan, Y. K., Chao, H. C., & Park, J. H. (2008). A genetic algorithm for energy-efficient based multicast routing on MANETs. Computer Communications, 31(10), 2632-2641.
• Manjunath Patel, G. C., Krishna, P., & Parappagoudar, M. B. (2016). Modelling and multi-objective optimization of squeeze casting process using regression analysis and genetic algorithm. Australian Journal of Mechanical Engineering, 14(3), 182-198.
• Santos, C. A., Spim, J. A., & Garcia, A. (2003). Mathematical modeling and optimization strategies (genetic algorithm and knowledge base) applied to the continuous casting of steel. Engineering applications of artificial intelligence, 16(5-6), 511-527.
• Anijdan, S. M., Bahrami, A., Hosseini, H. M., & Shafyei, A. (2006). Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity. Materials & design, 27(7), 605-609.
• Dučić, N., Ćojbašić, Ž., Manasijević, S., Radiša, R., Slavković, R., & Milićević, I. (2017). Optimization of the gating system for sand casting using genetic algorithm. International Journal of Metalcasting, 11(2), 255-265.
• Vijian, P., & Arunachalam, V. P. (2007). Modelling and multi objective optimization of LM24 aluminium alloy squeeze cast process parameters using genetic algorithm. Journal of materials processing technology, 186(1-3), 82-86.
• Patel, G. C. M., Krishna, P., Vundavilli, P. R., & Parappagoudar, M. B. (2016). Multi-objective optimization of squeeze casting process using genetic algorithm and particle swarm optimization. Archives of Foundry Engineering, 16. International Journal of Swarm Intelligence Research (IJSIR), 7.1: 55-74.
• Tsoukalas, V. D. (2008). Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis. Materials & Design, 29(10), 2027-2033.
• Lagdive, P. B., & Inamdar, K. H. (2013). Optimization of riser in casting using genetic algorithm. International Archive of Applied Sciences and Technology, 4(2), 21-26.
• Santos, C. A., Cheung, N., Garcia, A., & Spim, J. A. (2005). Application of a solidification mathematical model and a genetic algorithm in the optimization of strand thermal profile along the continuous casting of steel. Materials and Manufacturing Processes, 20(3), 421-434.
• GC, M. P., Krishna, P., Parappagoudar, M. B., & Vundavilli, P. R. (2016). Multi-objective optimization of squeeze casting process using evolutionary algorithms. International Journal of Swarm Intelligence Research (IJSIR), 7(1), 55-74.
• Pattnaik, S., Karunakar, D. B., & Jha, P. K. (2012). Developments in investment casting process-a review. Journal of Materials Processing Technology, 212(11), 2332-2348.