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Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling: A Study Case on AISI 1045 Steel Turning

Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling: A Study Case on AISI 1045 Steel Turning

Ramón Quiza, Iván La Fé-Perdomo, Marcelino Rivas, Veena Ramtahalsing
Copyright: © 2021 |Pages: 27
ISBN13: 9781799849391|ISBN10: 1799849392|ISBN13 Softcover: 9781799854418|EISBN13: 9781799849407
DOI: 10.4018/978-1-7998-4939-1.ch010
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MLA

Quiza, Ramón, et al. "Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling: A Study Case on AISI 1045 Steel Turning." Handbook of Research on Advancements in Manufacturing, Materials, and Mechanical Engineering, edited by Leonid Burstein, IGI Global, 2021, pp. 215-241. https://doi.org/10.4018/978-1-7998-4939-1.ch010

APA

Quiza, R., La Fé-Perdomo, I., Rivas, M., & Ramtahalsing, V. (2021). Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling: A Study Case on AISI 1045 Steel Turning. In L. Burstein (Ed.), Handbook of Research on Advancements in Manufacturing, Materials, and Mechanical Engineering (pp. 215-241). IGI Global. https://doi.org/10.4018/978-1-7998-4939-1.ch010

Chicago

Quiza, Ramón, et al. "Triple Bottom Line-Focused Optimization of Oblique Turning Processes Based on Hybrid Modeling: A Study Case on AISI 1045 Steel Turning." In Handbook of Research on Advancements in Manufacturing, Materials, and Mechanical Engineering, edited by Leonid Burstein, 215-241. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4939-1.ch010

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Abstract

This chapter proposes a hybrid approach for modelling and optimizing the oblique turning processes. Analytical modelling and statistical regressions are combined for predicting the values of the most important parameters involved in the oblique cutting process. The predictions of the model were validated by using experimental data, showing coincidence for a 95% confidence level. Then, an a posteriori multi-objective optimization is carried out by using a genetic algorithm. Three conflicting objectives, which represent the three pillars of the sustainability as defined in the triple bottom line, are simultaneously considered: the carbon dioxide emissions, the cost, and the cutting time. The outcome of the optimization process is a set of non-nominate solutions, which are optimal in the wide sense that no other solution in the search space can improve one objective without worsening the other one. Finally, the decision-maker chooses the most convenient solution depending on the actual workshop conditions.

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