Reference Hub

This research has been cited in:

Article
Comprehensive modelling, analysis and optimization of furan resin-based moulding sand system with sawdust as an additiveJournal of the Brazilian Society of Mechanical Sciences and Engineering10.1007/s40430-019-1684-0
Chapter
Abrasive Water Jet Machining of Ceramic CompositesAbrasive Water Jet Machining of Engineering Materials10.1007/978-3-030-36001-6_4
Article
Multi-objective optimization and prediction of surface roughness and printing time in FFF printed ABS polymerScientific Reports10.1038/s41598-022-20782-8
Article
Multi-objective optimization of alloying elements to enhance the wear resistance and impact strength of HCCI produced by furan sand moldProceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering10.1177/09544089211069212
Article
Teaching learning based optimization of squeeze casting process for quality castingsIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/376/1/012112
Article
Study on the relationship between interfacial heat transfer coefficient and interface pressure in squeeze casting by using microscopic contact modelInternational Journal of Thermal Sciences10.1016/j.ijthermalsci.2020.106300
Article
Modeling and Optimization of Wear Rate of AISI 2507 Super Duplex Stainless SteelSilicon10.1007/s12633-018-9908-y
Article
Experimental investigation of tensile fractography and wear properties of Al-12.5Si alloy reinforced with ZrO2 using spray deposition methodMaterials Today Communications10.1016/j.mtcomm.2022.103217
Article
Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm OptimizationArchives of Foundry Engineering10.1515/afe-2016-0073
Chapter
Modelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary AlgorithmsOptimization of Manufacturing Processes10.1007/978-3-030-19638-7_1
Article
Analysis and optimization of surface quality while machining high strength aluminium alloyMeasurement10.1016/j.measurement.2019.107337
Article
Parametric optimization for improving impact strength of squeeze cast of hybrid metal matrix (LM24–SiCp–coconut shell ash) compositeJournal of the Brazilian Society of Mechanical Sciences and Engineering10.1007/s40430-017-0925-3
Article
Quality Improvement in Investment Castings Using Genetic AlgorithmInternational Journal of Engineering Technologies IJET10.19072/ijet.938251
Article
Decision support systems in the metal casting industry: An academic review of research articlesMaterials Today: Proceedings10.1016/j.matpr.2017.11.215
Article
Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙRenewable Energy10.1016/j.renene.2020.10.125
Article
Hybrid multi-objective control allocation strategy for compound high-speed rotorcraftISA Transactions10.1016/j.isatra.2019.08.039
Chapter
Studies on Machining of Hard MaterialsMachining of Hard Materials10.1007/978-3-030-40102-3_2
Article
MgO Nano-Catalyzed Biodiesel Production from Waste Coconut Oil and Fish Oil Using Response Surface Methodology and Grasshopper OptimizationSustainability10.3390/su141811132
Article
Prediction of tensile strength in squeeze casted hybrid aluminium matrix composites using conventional statistical approachAdvances in Materials and Processing Technologies10.1080/2374068X.2021.1896865
Article
Optimization for waste coconut and fish oil derived biodiesel with MgO nanoparticle blend: Grey relational analysis, grey wolf optimization, driving training based optimization and election based optimization algorithmFuel10.1016/j.fuel.2022.127249
Chapter
Application of Statistical Modelling and Evolutionary Optimization Tools in Resin-Bonded Molding Sand SystemHandbook of Research on Investigations in Artificial Life Research and Development10.4018/978-1-5225-5396-0.ch007
Chapter
Squeeze Casting Parameter Optimization Using Swarm Intelligence and Evolutionary AlgorithmsCritical Developments and Applications of Swarm Intelligence10.4018/978-1-5225-5134-8.ch010

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

Manjunath Patel G C, Prasad Krishna, Mahesh B. Parappagoudar, Pandu Ranga Vundavilli

Source Title: International Journal of Swarm Intelligence Research (IJSIR)7(1)

ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466691568|DOI: 10.4018/IJSIR.2016010103

Cite Article Cite Article

MLA

Patel G C, Manjunath, et al. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms." IJSIR vol.7, no.1 2016: pp.55-74. http://doi.org/10.4018/IJSIR.2016010103

APA

Patel G C, M., 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. http://doi.org/10.4018/IJSIR.2016010103

Chicago

Patel G C, Manjunath, et al. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms," International Journal of Swarm Intelligence Research (IJSIR) 7, no.1: 55-74. http://doi.org/10.4018/IJSIR.2016010103

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

MLA

APA

Chicago

Export Reference

Abstract

Request Access