Energy efficient cutting parameter optimization

Chen, Xingzheng; Li, Congbo; Tang, Ying; Li, Li; Li, Hongcheng

doi:10.1007/s11465-020-0627-x

Cited by 30 publications

(8 citation statements)

References 89 publications

(113 reference statements)

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“…The cutting tools used in this study are sharp-edged, so the friction power of the flank face is calculated using Equation (22). Combining Equations ( 11)-( 23), the cutting power prediction model can be obtained as Equation (24).…”

Section: Friction Power Of Flank Facementioning

confidence: 99%

“…Girish et al [23] developed a multi-objective prediction model for minimizing energy consumption and surface roughness to obtain the optimum machining parameters. Chen et al [24] proposed that energy consumption can be effectively reduced by correctly selecting the cutting parameters. The energy utilized at the cutting edge can be modeled as the specific cutting energy (SCE) [25].…”

Section: Introductionmentioning

confidence: 99%

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Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Meng,

Dong,

Sun

et al. 2024

Coatings

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The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

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Section: Friction Power Of Flank Facementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Meng,

Dong,

Sun

et al. 2024

Coatings

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“…W oparciu o podejścia statystyczne, związane z projektowaniem eksperymentów, opracowano różne metody optymalizacji warunków obróbki, opierając się na wyrywkowych badaniach doświadczalnych. Analizę charakterystyki energetycznej procesu obróbki oraz energooszczędną optymalizację parametrów skrawania za pomocą modeli zużycia energii przedstawiono w pracy [7]. Jednak metody te nie mogą być łatwo wykorzystane, gdy cel optymalizacji lub konstrukcja obrabiarki jest modyfikowana.…”

Section: Pomiar Zużycia Energii W Trakcie Cyklu Wiercenia Termicznegounclassified

Measurement of Energy Consumption During a Thermal Drilling Cycle

Błaszczak¹,

Stryczek²

2023

PAR

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The work presents the issue of measuring and analyzing energy consumption during a thermal drilling cycle. A research stand based on a CNC lathe was presented, the equipment of which was an experimental energy consumption system based on an electricity meter and a force gauge. The measuring devices used allowed for recording the load on the power grid, the load on the machine tool drives, the axial force, the drill braking torque, the current rotation of the machine tool spindle, the position of the drill in relation to the workpiece and the values of auxiliary parameter registers in the strategy of adaptive feed control. Two cases of a drilling cycle were considered: making a single hole and making a series of holes in one cycle. Five feed control strategies were tested during the drilling cycle, including adaptive feed control and a recursive feed optimization method. For comparison, holes were also made with traditional HSS twist drills. Based on the obtained test results, it can be concluded that the friction drilling method can be included in the energy-saving methods of making holes in thin-walled elements.

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“…Machining is one of the primary activities in the mechanical manufacturing process, [1], of cylindrical Ti6Al4V component parts used in the aerospace industry. The cutting of metallic components by machine tools is considered a significant electrical energy consuming activity of the manufacturing process [2].…”

Section: Introductionmentioning

confidence: 99%

Integrated Energy Use Optimisation and Cutting Parameter Prediction Model - Aiding Process Planning of Ti6Al4V Machining on the CNC Lathe

Tayisepi,

Mugwagwa,

Munyau

et al. 2023

JERR

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This paper reports on the IEUOCPPTM (Integrated Energy Use Optimisation and Cutting Parameters Prediction Tool Model) designed to optimise the machining parameters planning process of titanium alloy machining on the CNC lathe. It aimed to create a novel systematic methodology for determination of optimised cutting parameters. MATLAB genetic algorithm and Visual Basic Application softwares were integrated to generate the IEUOCPPTM optimised machining process planning tool for titanium alloys. The empirical 18 full factorial experiment runs design was carried out using Minitab. Determination of appropriate cutting parameters is vital for conserving energy and achieving sustainability for the titanium alloy machining businesses confronted with immense pressure to produce cost-effectively in record delivery times. Machining is a fundamental, and electrical energy intensive, activity in the profiling process of cylindrical T-alloy, Ti6Al4V, components used in the aerospace, automotive and general metal working industries. Varied performance outcomes were achieved, on the machined components after predicting the input parameters using the tool as opposed to the good-guess approach currently being applied in industry. Validation experiments confirmed functionality of IEUOCPPTM in forecasting the cutting parameter settings required, to achieve desired responses during machining of Ti6Al4V within an average error range of 8%.

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Energy efficient cutting parameter optimization

Cited by 30 publications

References 89 publications

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

Measurement of Energy Consumption During a Thermal Drilling Cycle

Integrated Energy Use Optimisation and Cutting Parameter Prediction Model - Aiding Process Planning of Ti6Al4V Machining on the CNC Lathe

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