Modelling and optimization of energy consumption for feature based milling

Altıntaş, Resul Sercan; Kahya, Müge; Ünver, Hakkı Özgür

doi:10.1007/s00170-016-8441-7

Cited by 77 publications

(23 citation statements)

References 35 publications

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“…A multivariable energy optimization was carried out with the variables of cutting speed, feed rate and cutting depth. In line with the work in Velchev et al (2014) and Altıntaş et al 2016, our prior work established singlepass and multi-pass parameter optimization models for electrical energy efficiency improvement (Li et al, 2016b;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 97%

“…An equation for the optimal cutting speed is devised by applying the minimum energy criterion. The study conducted by Altıntaş et al (2016) presented a new model for evaluating the electrical energy consumed by a machine tool for processing a prismatic workpiece. A multivariable energy optimization was carried out with the variables of cutting speed, feed rate and cutting depth.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials

Chen

Jin

et al. 2018

Int J Adv Manuf Technol

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In a CNC milling process, proper selection of cutting parameters can significantly reduce the electrical energy consumption. Many scholars have conducted cutting parameter optimization of the CNC milling operation for electrical energy saving during the past several years. However, in the CNC milling process, a large amount of auxiliary materials such as cutting tools and cutting fluid are consumed. The production process of these materials is energy-intensive and a lot of energy are consumed. Optimizing cutting parameters considering both the electrical energy consumption and embodied energy consumption of auxiliary materials can further reduce the environmental impact of the milling process. In this paper, an approach of cutting parameter optimization is proposed to maximize energy efficiency and machining efficiency for CNC milling operation. Firstly, the electrical energy consumption and embodied energy consumption of auxiliary materials during the CNC milling process are analyzed. The energy model of CNC milling is then proposed. Then a multi-objective optimization model for maximizing energy efficiency and machining efficiency is established. To verify the proposed multiobjective model, a case study is conducted and the results show that: i) the optimum cutting parameters of CNC milling process varies with the energy boundary whether including the embodied energy of the auxiliary materials or not; ii) cutting parameter optimization for maximum machining efficiency does not necessarily satisfy the maximum energy efficiency criterion; iii) the proposed multi-objective optimization model strikes a balance between maximum energy efficiency and maximum machining efficiency.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials

Chen

Jin

et al. 2018

Int J Adv Manuf Technol

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“…The study of energy modeling and energy efficiency improvement of the machining process has aroused extensive interest in recent years [20][21][22][23][24][25]. To achieve better energy management of the machining process, energy consumption allowance, as a significant management tool for improving energy efficiency, was introduced into the mechanical manufacturing industry [26].…”

Section: Introductionmentioning

confidence: 99%

“…To achieve better energy management of the machining process, energy consumption allowance, as a significant management tool for improving energy efficiency, was introduced into the mechanical manufacturing industry [26]. Moreover, many studies have focused on optimizing the cutting parameters in the machining process [27][28][29][30][31][32][33][34][35][36][37][38]. It is necessary to point out that apart from cutting activities, non-cutting activities (NCA) occur frequently during machining [39] and the energy demand of NCA is significant, sometimes using more than 70% of the total energy consumption in machining [40].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, there is great potential for improving the energy efficiency of the machining process through energy-saving in NVAAs. The energy optimization of material cutting activities has drawn much attention from researchers [27][28][29][30][31][32][33][34][35][36][37][38]. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes in NVAA.…”

Section: Introductionmentioning

confidence: 99%

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Therblig-embedded value stream mapping method for lean energy machining

Jia

Yuan

et al. 2017

Energy

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To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning.

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State of Research

Madanchi¹

2022

Sustainable Production, Life Cycle Engineering and Management

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Modelling and optimization of energy consumption for feature based milling

Cited by 77 publications

References 35 publications

Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials

Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials

Therblig-embedded value stream mapping method for lean energy machining

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