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Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques

Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques

Pandu R. Vundavilli, J. Phani Kumar, M.B. Parappagoudar
Copyright: © 2013 |Volume: 4 |Issue: 3 |Pages: 16
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466633889|DOI: 10.4018/ijsir.2013070103
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MLA

Vundavilli, Pandu R., et al. "Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques." IJSIR vol.4, no.3 2013: pp.42-57. http://doi.org/10.4018/ijsir.2013070103

APA

Vundavilli, P. R., Kumar, J. P., & Parappagoudar, M. (2013). Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques. International Journal of Swarm Intelligence Research (IJSIR), 4(3), 42-57. http://doi.org/10.4018/ijsir.2013070103

Chicago

Vundavilli, Pandu R., J. Phani Kumar, and M.B. Parappagoudar. "Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques," International Journal of Swarm Intelligence Research (IJSIR) 4, no.3: 42-57. http://doi.org/10.4018/ijsir.2013070103

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Abstract

Tube spinning is an effective process for producing long and thin walled tubes. It is important to note that the quality of parts produced in tube spinning process, namely internal surface roughness, external surface roughness, change in diameter and change in thickness depends on the right combination of input process parameters, such as mandrel rotational speed, feed rate of rollers, percentage of thickness reduction, initial thickness, solution treatment time and ageing treatment time. As the 2024 aluminum tube spinning process contains four objectives, it is very difficult to achieve a set of optimal combination of input process parameters that produce best quality product. This paper presents a weighted average-based multi-objective optimization of tube spinning process using non-traditional optimization techniques, namely genetic algorithm, particle swarm optimization and differential evolution. Multiple regression equations developed between the control factors and responses have been considered for optimization.

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