Parameter Optimisation of Squeeze Casting Process using LM 20 Alloy: Numeral Analysis by Neural Network and Modified Coefficient-based Deer Hunting Optimization

References

• Ali, M. A., K. Ishfaq, and M. Jawad. August 2019. “Evaluation of Surface Quality and Mechanical Properties of Squeeze Casted AA2026 Aluminum Alloy Using Response Surface Methodology.” The International Journal of Advanced Manufacturing Technology 103 (9–12): 4041–4054. doi:10.1007/s00170-019-03836-6.
   Web of Science ®Google Scholar
• Arulraj, M., and P. K. Palani. January 2018. “Parametric Optimization for Improving Impact Strength of Squeeze Cast of Hybrid Metal Matrix (Lm24–sicp–coconut Shell Ash) Composite.” Journal of the Brazilian Society of Mechanical Sciences and Engineering 40, (2). doi:10.1007/s40430-017-0925-3.
   Google Scholar
• Ashiri, R., F. Karimzadeh, and B. Niroumand. 2014. “On Effect of Squeezing Pressure on Microstructural Characteristics, Heat Treatment Response and Electrical Conductivity of an Al-Si-Mg-Ni-Cu Alloy.” Materials Science and Technology 30 (10): 1162–1169. doi:10.1179/1743284713Y.0000000405.
   Web of Science ®Google Scholar
• Brammya, G., S. Praveena, N. S. Ninu Preetha, R. Ramya, B. R. Rajakumar, and D. Binu. 24 May 2019. “Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm.” The Computer Journal doi:10.1093/comjnl/bxy133
   Google Scholar
• Chattopadhyay, H. 7 May 2007. “Simulation of Transport Processes in Squeeze Casting.” Journal of Materials Processing Technology 186 (1–3): 174–178. doi:10.1016/j.jmatprotec.2006.12.038.
   Web of Science ®Google Scholar
• Fernández-Navarro, F., M. Carbonero-Ruz, D. Becerra Alonso, and M. Torres-Jiménez. November 2017. “Global Sensitivity Estimates for Neural Network Classifiers.” IEEE Transactions on Neural Networks and Learning Systems 28 (11): 2592–2604. doi:10.1109/TNNLS.2016.2598657.
   PubMed Web of Science ®Google Scholar
• George, A., and B. R. Rajakumar. July 2013. “On Hybridizing Fuzzy Min Max Neural Network and Firefly Algorithm for Automated Heart Disease Diagnosis”, Fourth International Conference on Computing, Communications and Networking Technologies, Tiruchengode, India.
   Google Scholar
• Ghassemali, E., A. Jarfors, and A. Dioszegi. July 2018. “On the Formation of Micro-Shrinkage Porosities in Ductile Iron Cast Components.” Metals - Open Access Metallurgy Journal 8, (7): 551.
   Google Scholar
• Ghomashchi, M. R., and A. Vikhrov. 14 April 2000. “Squeeze Casting: An Overview.” Journal of Materials Processing Technology 101 (1–3): 1–9. doi:10.1016/S0924-0136(99)00291-5.
   Web of Science ®Google Scholar
• Goh, C. S., K. S. Soh, P. H. Oon, and B. W. Chua. June 2010. ““Effect of Squeeze Casting Parameters on the Mechanical Properties of AZ91–Ca Mg Alloys”.” Materials & Design 31, (1): S50–S53. doi:10.1016/j.matdes.2009.11.039.
   Google Scholar
• Lakshmikanthan, A., S. Bontha, M. Krishna, P. G. Koppad, and T. Ramprabhu 2019. “Microstructure, Mechanical and Wear Properties of the A357 Composites Reinforced with Dual Sized SiC Particles.” Journal of Alloys and Compounds 786: 570–580. doi:10.1016/j.jallcom.2019.01.382.
   Web of Science ®Google Scholar
• Maleki, A., A. Shafyei, and B. Niroumand. 21 April 2009. “Effects of Squeeze Casting Parameters on the Microstructure of LM13 Alloy.” Journal of Materials Processing Technology 209 (8): 3790–3797. doi:10.1016/j.jmatprotec.2008.08.035.
   Web of Science ®Google Scholar
• Manjunath, P. G. C., A. K. Shettigar, and M. B. Parappagoudar. April 2018. “A Systematic Approach to Model and Optimize Wear Behaviour of Castings Produced by Squeeze Casting Process.” Journal of Manufacturing Processes 32: 199–212. doi:10.1016/j.jmapro.2018.02.004.
   Web of Science ®Google Scholar
• Manjunath, P. G. C., P. Krishna, and M. B. Parappagoudar. August 2016. “Squeeze Casting Process Modeling by a Conventional Statistical Regression Analysis Approach.” Applied Mathematical Modelling 40 (15–16): 6869–6888. doi:10.1016/j.apm.2016.02.029.
   Web of Science ®Google Scholar
• Manjunath Patel, G. C., P. Krishna, and M. Parappagoudar. October 2014. “Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches.” Applied Computational Intelligence and Soft Computing. 1–12.
   Google Scholar
• Manjunath Patel, G. C., P. Krishna, and M. B. Parappagoudar. October 2016a. “An Intelligent System for Squeeze Casting Process—soft Computing Based Approach.” The International Journal of Advanced Manufacturing Technology 86 (9–12): 3051–3065. doi:10.1007/s00170-016-8416-8.
   Web of Science ®Google Scholar
• Manjunath Patel, G. C., P. Krishna, and M. B. Parappagoudar. 2016b. “Modelling and Multi-objective Optimization of Squeeze Casting Process Using Regression Analysis and Genetic Algorithm.” Australian Journal of Mechanical Engineering 14, (3): 182–198. doi:10.1080/14484846.2015.1093231.
   Web of Science ®Google Scholar
• ManjunathPatel, G. C., P. Krishna, and M. B. Parappagoudar. 2014. “Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis.” Procedia Technology 14: 157–164. doi:10.1016/j.protcy.2014.08.021.
   Google Scholar
• Mirjalili, S., and A. Lewis. May 2016. “The Whale Optimization Algorithm.” Advances in Engineering Software 95: 51–67. doi:10.1016/j.advengsoft.2016.01.008.
   Web of Science ®Google Scholar
• Ninu Preetha, N. S., G. Brammya, R. Ramya, S. Praveena, D. Binu, and R. B R. 2018. “Grey Wolf Optimisation-based Feature Selection and Classification for Facial Emotion Recognition.” IET Biometrics 7 (5): 490–499. doi:10.1049/iet-bmt.2017.0160.
   Web of Science ®Google Scholar
• Parappagoudar, M., D. K. Pratihar, and G. L. Datta. October 2007. “Modelling of Input-output Relationships in Cement Bonded Moulding Sand System Using Neural Networks.” International Journal of Cast Metals Research 20, (5): 265–274. doi:10.1179/136404607X249446.
   Web of Science ®Google Scholar
• Parappagoudar, M., D. K. Pratihar, and G. L. Datta. January 2008a. “Forward and Reverse Mappings in Green Sand Mould System Using Neural Networks.” Applied Soft Computing 8, (1): 239–260. doi:10.1016/j.asoc.2007.01.005.
   Web of Science ®Google Scholar
• Parappagoudar, M. B., D. K. Pratihar, and G. L. Datta. 2008b. “Neural Network-Based Approaches for Forward and Reverse Mappings of Sodium Silicate-Bonded, Carbon Dioxide Gas Hardened Moulding Sand System.” Materials and Manufacturing Processes 24 (1): 59–67. doi:10.1080/10426910802543681.
   Web of Science ®Google Scholar
• Rajagopal, S. January 1981. “Squeeze Casting: A Review and Update.” Journal of Applied Metalworking 1, (4): 3–14. doi:10.1007/BF02834341.
   Google Scholar
• Sarfraz, M. H., M. Jahanzaib, W. Ahmed, and S. Hussain. May 2019. “Multi-response Parametric Optimization of Squeeze Casting Process for Fabricating Al 6061-SiC Composite.” The International Journal of Advanced Manufacturing Technology 102 (1–4): 759–773. doi:10.1007/s00170-018-03278-6.
   Web of Science ®Google Scholar
• Wang, D., D. Tan, and L. Liu. 2018. “Particle Swarm Optimization Algorithm: An Overview.” Soft Computing 22,: 387–408. doi:10.1007/s00500-016-2474-6.
   Web of Science ®Google Scholar
• Wang, R. J., J. Zeng, and D.-W. Zhou. December 2012. “Determination of Temperature Difference in Squeeze Casting Hot Work Tool Steel.” International Journal of Material Forming 5, (4): 317–324. doi:10.1007/s12289-011-1061-8.
   Web of Science ®Google Scholar
• Yang, L. J. 2007. “The Effect of Solidification Time in Squeeze Casting of Aluminium and Zinc Alloys.” Journal of Materials Processing Technology 192–193: 114–120. doi:10.1016/j.jmatprotec.2007.04.025.
   Web of Science ®Google Scholar
• Yize, L., H. Yang, and Z. Xing. January 2017. “Numerical Simulation and Process Optimization of Squeeze Casting Process of an Automobile Control Arm.” The International Journal of Advanced Manufacturing Technology 88 (1–4): 941–947. doi:10.1007/s00170-016-8845-4.
   Web of Science ®Google Scholar
• Yuan, D., X. Yang, S. Wu, S. Lü, and K. Hu. July 2019. “Development of High Strength and Toughness nano-SiCp/A356 Composites with Ultrasonic Vibration and Squeeze Casting.” Journal of Materials Processing Technology 269: 1–9. doi:10.1016/j.jmatprotec.2019.01.021.
   Web of Science ®Google Scholar