Energy-Efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies

Moreira, Lorena Caires; Li, Weidong; Lu, Xin; Fitzpatrick, Michael E.

doi:10.1016/j.rcim.2019.01.011

Cited by 33 publications

(10 citation statements)

References 39 publications

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“…From the current literature about cutting parameter optimization for energy saving, the focused energy boundary of these studies is different. Some studies [14,15] only focus on a part of the electrical energy consumption of machining process, and other studies [16,17] explore the total electrical energy consumption and the embodied energy of cutting tool and cutting fluid. Hence, to gain a better understanding of the existing works about energy efficient cutting parameter optimization, the energy boundary and energy characteristics of machining process should be analyzed.…”

Section: Energy Consumption Characteristics Of Machining Processmentioning

confidence: 99%

“…The methods for modeling the relationship between energy consumption and cutting parameters can be mainly classified into two categories. The first category models energy consumption with respect to cutting parameters by using experimental design and mathematical models, such as artificial neural network (ANN) [48], response surface methodology (RSM) [14,35], and Kriging model [15]. The accuracy of these energy models can be extremely high because they are close to the specific machining conditions.…”

Section: Modeling Of Energy Consumption With Respect To Cutting Parametersmentioning

confidence: 99%

“…Other models established using standard mathematical models, such as ANN, RSM, and Kriging model, can be found in Refs. [14,15,35,48]. The methods in Table 2 are classified into two categories, namely, energy modeling method based on machine tool component (EMMBMTC) and energy modeling method based on machining state (EMMBMS).…”

Section: Modeling Of Energy Consumption With Respect To Cutting Parametersmentioning

confidence: 99%

“…For example, Yan and Li [44] found that the energy consumption of milling process can be reduced with a large MRR. Li et al [49] and Moreira et al [14] found that machining with a large feed rate can reduce the energy consumption of their milling process. This is because the main machining conditions of these studies are relatively similar, and the main conclusions are consistent.…”

Section: Recommendationsmentioning

confidence: 99%

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Energy efficient cutting parameter optimization

Chen

Tang

et al. 2021

Front. Mech. Eng.

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Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

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Section: Energy Consumption Characteristics Of Machining Processmentioning

confidence: 99%

Section: Modeling Of Energy Consumption With Respect To Cutting Parametersmentioning

confidence: 99%

Section: Modeling Of Energy Consumption With Respect To Cutting Parametersmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

See 2 more Smart Citations

Energy efficient cutting parameter optimization

Chen

Tang

et al. 2021

Front. Mech. Eng.

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“…In the process of CNC machining, the reasonable selection of process parameters not only affects the indexes of machining cost [7], quality [8] and efficiency [9], but also is closely related to the energy consumption of machine tools [10]. How to optimize the process parameters in the machining process of CNC machine tools is an urgent basic scientific problem to be solved under the background of green manufacturing [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption

Jia

Wang

et al. 2021

Sustainability

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Energy-saving and emission reduction are recognized as the primary measure to tackle the problems associated with climate change, which is one of the major challenges for humanity for the forthcoming decades. Energy modeling and process parameters optimization of machining are effective and powerful ways to realize energy saving in the manufacturing industry. In order to realize high quality and low energy consumption machining of computer numerical control (CNC) lathe, a multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption is proposed. By analyzing the energy consumption characteristics in the process of machining and introducing practical constraints, such as machine tool equipment performance and tool life, a multi-objective optimization model with turning process parameters as optimization variables and high quality and low energy consumption as optimization objectives is established. The model is solved by non-dominated sorting genetic algorithm-II (NSGA-II), and the pareto optimal solution set of the model is obtained. Finally, the machining process of shaft parts is studied by CK6153i CNC lathe. The results show that 38.3% energy consumption is saved, and the surface roughness of workpiece is reduced by 47.0%, which verifies the effectiveness of the optimization method.

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