Therblig-based energy supply modeling of computer numerical control machine tools

Lv, Jingxiang; Tang, Renzhong; Jia, Shi

doi:10.1016/j.jclepro.2013.09.055

Cited by 90 publications

(37 citation statements)

References 13 publications

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“…The automatic tool change is divided into the tool pick, tool place, and rotation of the tool magazine [33]. The energy consumption of the automatic tool change can be calculated as follows:…”

Section: Model Of Tool Changing Energy Consumptionmentioning

confidence: 99%

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

et al. 2018

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Abstract:Concerns have been increasing regarding the environmental sustainability of disassembly activities that take place in various recovery operations at end of life stage of products, and subsequently, disassembly activities have been gaining increased exposure. The disassembly line is a good choice for automated disassembly of end-of-life products. Although interest in addressing energy saving in manufacturing is rising, the study of incorporating energy consumption reduction and disassembly efficiency improvement into disassembly line balancing is still limited. The analysis, design, and balanced decision-making for disassembly lines are urgently needed in order to make the disassembly line as energy-saving as possible. In this paper, an energy-saving optimization method, which was used in scheduling workstation selection and the disassembly sequence for the disassembly line, is proposed. Energy consumption of each stage of the disassembling process was analyzed and modeled. Then, a mathematical model of the disassembly line balancing problem with energy saving considerations was formulated and the objective of energy consumption was integrated into the objectives of cost and working load in order to balance a disassembly line. Optimal solutions for the disassembly line balancing problem with an energy saving consideration were obtained by using an artificial bee colony algorithm, which was a feasible way of minimizing workstations, and ensuring similar working load, as well as minimizing energy consumption. Finally, a case study of disassembly line balancing for a typical driving system is presented to illustrate the proposed method.

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Section: Model Of Tool Changing Energy Consumptionmentioning

confidence: 99%

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

et al. 2018

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“…Comparing several 10 available models [32] to calculate spindle rotation power, the linear model proposed by Lv et al [33] 11 provides the highest accuracy which is 95%. Thus, this model is employed to calculate spindle rota-12 tion power of a machine tool in the -th feeding activity ( ) as: 13 (6) 14 where is spindle rotation speed [rpm] in the -th feeding activity; coefficient and constant 15 can be obtained by linear regression based on experiment data [33]. Finally, is axial rapid 16 feeding time for the -th feeding activity which is the maximum among the rapid feeding time of 17 three axes that max .…”

Section: Energy Consumption Of a Rapid Feeding Activity 16mentioning

confidence: 99%

“…The models for , 1 and are similar. For instance, following Lv et al [33], can be expressed as: 2 (8) 3 where coefficients and can be obtained by quadratic regression based on experiment data. 4…”

Section: Energy Consumption Of a Normal Feeding Activity 19mentioning

confidence: 99%

Minimising the energy consumption of tool change and tool path of machining by sequencing the features

Liu

Peng

et al. 2018

Energy

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and tool change plan will vary based on the different PSFP. This paper firstly aims to un-6 derstand the relationship between the PSFP and the energy consumption of tool change 7 and tool path during the feature transitions. Then, a model is introduced for the single ob-8 jective optimisation problem that minimises the energy consumption of machine tools 9 during the feature transitions which include all the tool path and tool change operations. 10Finally, optimisation approaches including depth-first search and genetic algorithm are 11 modified and applied to find the optimal PSFP which results in the minimisation of the 12 energy consumption of feature transitions (EFT). In the case study, the optimal and near-13 optimal sequences of features, in terms of the minimum EFT, of a 15 features part which 14 is processed by a machining centre have been found. The optimal PSFP achieves a 15

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“…Triggered by the necessity to improve the energy efficiency and environmental performance of the manufacturing industry, energy modeling [11][12][13][14][15][16], energy-efficiency improvement [17][18][19][20][21][22] and carbon-emission reduction [23,24] of the manufacturing industry have been studied. Experiments show that power peaks will be caused by state transitions during the machining process [25,26], as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…When the initial spindle speed is not zero (accelerating from low speed to high speed), the above model will not be applicable. Moreover, as shown in the energy supply model of the spindle startup established in our previous work [33], the model can only calculate the energy consumption of the spindle system during spindle speedup. However, during the spindle speedup process, standby operation, X,Y,Z-axis feeding, chip conveying, cutting flood spraying and other motions can also be executed.…”

Section: Introductionmentioning

confidence: 99%

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

et al. 2017

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Abstract:Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

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Therblig-based energy supply modeling of computer numerical control machine tools

Cited by 90 publications

References 13 publications

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

Minimising the energy consumption of tool change and tool path of machining by sequencing the features

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

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