The International Dimension of Electrical Energy Derived Emissions for Machine Tools

Alswat, Haitham M.; Mativenga, Paul

doi:10.1016/j.procir.2021.01.177

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The main contribution is to obtain optimized solutions, similar to those obtained in [7], for the one-dimensional cutting stock problem, with relevant gains in computational complexity (or runtime) and conversion of loss to leftovers whenever possible. The heuristics can be used in commercial software solutions and provide a reduction in the raw materials used (or manufacturing costs) and, consequently, environment preservation, as was the case for the results obtained by [12,13], which address the green manufacturing study topic.…”

Section: Introductionmentioning

confidence: 99%

A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers

Bressan,

Pimenta-Zanon,

Sakuray

2023

Materials

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Cutting problems consist of cutting a set of objects available in stock in order to produce the desired items in specified quantities and sizes. The cutting process can generate leftovers (which can be reused in the case of new demand) or losses (which are discarded). This paper presents a tree-based heuristic method for minimizing the number of cut bars in the one-dimensional cutting process, satisfying the item demand in an unlimited bar quantity of just one type. The results of simulations are compared with the RGRL1 algorithm and with the limiting values for this considered type of problem. The results show that the proposed heuristic reduces processing time and the number of bars needed in the cutting process, while it provides a larger leftover (by grouping losses) for the one-dimensional cutting stock problem. The heuristic contributes to reduction in raw materials or manufacturing costs in industrial processes.

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Section: Introductionmentioning

confidence: 99%

A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers

Bressan,

Pimenta-Zanon,

Sakuray

2023

Materials

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Sustainable assessment of a milling manufacturing process based on economic tool life and energy modeling

Minquiz,

Meraz-Melo,

Flores Méndez

et al. 2023

J Braz. Soc. Mech. Sci. Eng.

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Greenhouse gas emissions are caused by human activities, one of which is the manufacturing process, which is the main driver of global warming. The machining process is a common practice in sectors such as aerospace and automotive. Based on the latter, this research focuses on understanding and testing an alternative way to make an economically sustainable machining process. This study reports on the performance of tool life analysis under dry settings, and the cutting speed calculation considers machining and tooling costs. Developing a design of the experiment established the power demand equation, which is part of the energy model presented in this research, and it also helps to understand the carbon dioxide emissions to the environment before starting the milling process. Based on the results, the tool life evaluation shows the longest working time under good tool conditions with a measured surface roughness of less than 0.6 μm. The energy model shows the alternative to improve energy consumption and CO2 emissions by 0.11 kWh and 0.055 kg CO2, respectively, which is essential to understand the challenge of reducing the manufacturing footprint.

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Integrated assessment and optimization of dual environment and production drivers in grinding

Wang

Tian

et al. 2023

Energy

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The International Dimension of Electrical Energy Derived Emissions for Machine Tools

Cited by 4 publications

References 10 publications

A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers

A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers

Sustainable assessment of a milling manufacturing process based on economic tool life and energy modeling

Integrated assessment and optimization of dual environment and production drivers in grinding

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