Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

Jiang, Zhipeng; Gao, Dong; Lü, Yong; Liu, Xianli

doi:10.1186/s10033-019-0408-9

Cited by 26 publications

(12 citation statements)

References 24 publications

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“…Arriaza et al [17] established a multi-objective response optimization method for processing time and processing energy consumption, and finally found that spindle speed is the main factor of energy consumption, and cutting width is the most important factor affecting processing efficiency. Jiang et al [18] proposes a cutting parameter optimization method based on an evaluation model of machining tasks, which also takes into account carbon emissions, surface roughness and machining time. Some researchers proposed a multi-objective optimization model to reduce energy consumption based on machining process planning, which takes into account processing efficiency and machine tool energy consumption [19][20][21][22], but they did not consider processing quality in the optimization modeling process.…”

Section: Cutting Parameters Optimization Methodsmentioning

confidence: 99%

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

Tian

Zhang

2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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The natural energy crisis and the increasingly serious environmental problems have imposed all industries to reduce energy consumption. During milling process, selecting a correct cutting parameters can not only greatly improve production quality and processing efficiency, but also can reduce energy consumption, in addition, tool wear also has a great impact on them. Therefore, a milling power consumption model of CNC machine tools is established based on modern machining theory is established in this article, unlike traditional energy consumption models, our model takes full account of cutting conditions and tool wear. The surface roughness of parts is one of the important indicators to measure the machining quality of machine tools. Therefore, taking milling process as research object, a multi-objective cutting parameters optimization model that takes the machining surface roughness, material removal rate (MRR) and machining energy consumption as the optimization goals was established. Furthermore, an intelligent optimization algorithm was proposed based on improved Teaching-Learning-Based Optimization (TLBO) to solve the model under various limited milling conditions. Finally, comparing experimental results of optimized parameter and empirical parameters, it shows that goals of reducing energy consumption, improving productivity and machining quality can be achieved by optimizing cutting parameters.

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Section: Cutting Parameters Optimization Methodsmentioning

confidence: 99%

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

Tian

Zhang

2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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“…The signal-to-noise (S/N) ratio is particularly utilized in the Taguchi method to study the influence of each factor [37]. Because the uniformity, the typical of interest in this investigation, should be as close as possible to zero, a "smaller the better" formula is employed for the S/N ratio calculation as follows [39], [40]:…”

Section: 2mentioning

confidence: 99%

Development and Characterization of a Thermoforming Apparatus Using Axiomatic Design Theory and Taguchi Method

Nguyen

Pham

Chau

et al. 2020

Preprint

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In this study, an experimental investigation of the development of a thermoforming apparatus and the thickness uniformity of its samples was performed based on the axiomatic design theory in conjunction with the Taguchi method. Thermoforming is a powerful tool for both consumer product needs and packaging industry. Such traditional technology has been investigated in many aspects for a long time ago. However, there are still needs for the development and characterization of thermoforming devices aiming at shorter construction time and less cost involved. Therefore, an experimental analysis was performed to systematically realize all the functional structures of the device using axiomatic design theory. The combination of orthogonal array (OA) and analysis of variance revealed the influences of the relatival processing factors, showing that the thickness of the plastic sheet and areal draw ratio had crucial roles to play. Furthermore, an optimization process using the Taguchi method was utilized to determine all accurate and optimum processing parameters. The outcomes obviously verified that the present combination method can overcome the current development and optimization method’s limitation and also conclusively give accurate optimal outcomes without using complicated algorithms and software solutions. Therefore, it promises a simple and powerful tool for engineers on-site in medium or small scale manufactories.

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“…Previous research such as [6,7,8,9] stated that machining parameters such as cutting speed, feed rate, and depth of cut can affect the energy and costs incurred during machining. Arif, et al [10] developed an energy optimization model consisting of machining, idle, tool replacement, and cutting tool energy.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Machining Parameters for Multi-Pass CNC Turning to Minimize Carbon Emissions, Energy, Noise and Cost

Fittamami

Pujiyanto²,

Priyandari³

2021

jti

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Global warming is a huge environmental issue today. This is due to the high level of world carbon emissions. The manufacturing process accounts for 30% of the world's carbon emissions production. Sustainable manufacturing is necessary to implement to reduce carbon emission levels caused by the manufacturing process. There are three aspects of sustainable manufacturing, namely environmental aspects, economic aspects, and social aspects. These three aspects can be implemented in the machining process by optimizing machining parameters in multi-pass CNC turning. This research aims to optimize CNC turning machining parameters by considering energy consumption, carbon emissions, noise, and production cost. The model is solved using a Multi-objective Genetic Algorithm in Matlab 2016b then the transformation and weighting functions are carried out from the feasible value. Based on the optimization results, the total energy consumption value obtained is 2.50 MJ; total production cost is $ 2.19; total carbon emissions are 5.97 kgCO2, and noise is 236, 19 dB. The sensitivity analysis exhibits the machining parameters that affect the objective function: The cutting speed parameter and the feed rate parameter. This model can be used to improve the manufacturing process and support sustainable manufacturing.

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Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

Cited by 26 publications

References 24 publications

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

Development and Characterization of a Thermoforming Apparatus Using Axiomatic Design Theory and Taguchi Method

Multi-Objective Optimization of Machining Parameters for Multi-Pass CNC Turning to Minimize Carbon Emissions, Energy, Noise and Cost

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