Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time

Chen, Xingzheng; Li, Congbo; Tang, Ying; Li, Li; Du, Yanbin

doi:10.1016/j.energy.2019.02.157

Cited by 70 publications

(23 citation statements)

References 41 publications

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“…The embodied energy consumption of cutting tool is related to the tool life, which is highly dependent on the cutting parameters and machining performance of cutting tool. In a machining process, the embodied energy consumption of cutting tool can be reduced through proper selection of cutting parameters and cutting tool material [32].…”

Section: Embodied Energy Consumption Of Cutting Tool and Cutting Fluidmentioning

confidence: 99%

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: Embodied Energy Consumption Of Cutting Tool and Cutting Fluidmentioning

confidence: 99%

Energy efficient cutting parameter optimization

Chen

Tang

et al. 2021

Front. Mech. Eng.

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“…Several mathematical and numerical models have been proposed on these themes: minimization of the production costs [5], minimization of carbon footprint and production time [6], minimization of the total energy consumption [7] and the reduction of peak power periods with the introduction of buffers in a flow line [8]. Renna and Materi [9] focused on the design problem of the production lines with the aim of energy saving using switch-off policies.…”

Section: Introductionmentioning

confidence: 99%

Reducing energy costs and CO2 emissions by production system energy flexibility through the integration of renewable energy

Materi

D’Angola

Enescu

et al. 2021

Prod. Eng. Res. Devel.

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In recent years, the production of renewable energy has increased continuously to reduce fossil fuel consumption and CO2 emissions and to increase energy efficiency. The challenge of industries is to integrate renewable energy systems into the existing power system of manufacturing industries. In the energy flexibility approach, the manufacturing energy demand is aligned with renewable energy availability, to improve the use of the renewable energy source. This paper aims to investigate a manufacturing system supplied by a photovoltaic plant coupled with a battery storage system. A basic storage model has been developed and implemented to pursue this issue. The model is applied in the simplified case of a manufacturing system composed of a cutting numerical control machine, which can adapt the cutting speed to align the power needed to the power supplied by the photovoltaic plant. However, the model can be extended to realistic production cases characterized by complex systems as long as the time evolution of energy consumptions are known in detail. The introduction of battery storage allows reducing the cutting speed fluctuations, improving the cutting life derived from the fatigue effect. This solution reduces the costs of the machine and improves the forecasting of the means needed for the manufacturing system. Finally, a detailed analysis of greenhouse gas reduction is discussed.

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“…Chen et al [4] were able to optimize machining parameters with a reduction in energy consumption and production time for the face milling process. They presented an integrated approach for minimizing the energy footprint and production time by optimizing cutting tools and cutting parameters.…”

Section: Introductionmentioning

confidence: 99%

The Optimization of Machining Parameters for Milling Operations by Using the Nelder–Mead Simplex Method

Lee

Resiga

et al. 2020

JMMP

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The purpose of machining operations is to make specific shapes or surface characteristics for a product. Conditions for machining operations were traditionally selected based on geometry and surface finish requirements. However, nowadays, many researchers are optimizing machining parameters since high-quality products can be produced using more expensive and advanced machines and tools. There are a few methods to optimize the machining process, such as minimizing unit production time or cost or maximizing profit. This research focused on maximizing the profit of computer numerical control (CNC) milling operations by optimizing machining parameters. Cutting speeds and feed are considered as the main process variables to maximize the profit of CNC milling operations as they have the greatest effect on machining operation. In this research, the Nelder–Mead simplex method was used to maximize the profit of CNC milling processes by optimizing machining parameters. The Nelder–Mead simplex method was used to calculate best, worst, and second-worst value based on an initial guess. The possible range of machining parameters was limited by several constraints. The Nelder–Mead simplex method yielded a profit of 3.45 ($/min) when applied to a commonly used case study model.

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Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time

Cited by 70 publications

References 41 publications

Energy efficient cutting parameter optimization

Energy efficient cutting parameter optimization

Reducing energy costs and CO2 emissions by production system energy flexibility through the integration of renewable energy

The Optimization of Machining Parameters for Milling Operations by Using the Nelder–Mead Simplex Method

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