Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design

Camposeco-Negrete, Carmita; Nájera, Juan de Dios Calderón; Miranda-Valenzuela, J.C.

doi:10.1007/s00170-015-7679-9

Cited by 53 publications

(6 citation statements)

References 18 publications

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“…The feed rate and cutting depth are the significant factors on energy consumption. Camposeco-Negrete et al [40] optimized the turning parameters to minimize the energy consumption during turning of AISI 1018 steel under wet, MQL, and dry turning conditions. They found that feed rate and cutting depth have significant effect on energy consumption, which is similar to the findings reported by Zhang et al [39].…”

Section: Related Literature On Cutting Parameter Optimization For Energy Savingmentioning

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: Related Literature On Cutting Parameter Optimization For Energy Savingmentioning

confidence: 99%

Energy efficient cutting parameter optimization

Chen

Tang

et al. 2021

Front. Mech. Eng.

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“…They used single-layered (TiAlN) and multi-layered coated with Ti (C, N, B) cutting tools to study the phenomenon of surface finish and tool wear. Camposeco-Negrete et al (2016) performed experiments by applying Taguchi's L9 array through turning of AISI 1018 steel. They optimized the electrical energy required during machining against speed, feed and depth of cut.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Independent Optimization while Turning of Inconel-600 alloy using Grey Interactive Exploration

Sonawane¹,

Wangikar²,

Pukale³

2021

Production Engineering Archives

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This investigation effort offers multi-quality attributes optimization while turning of Inconel-600 superalloy. Taguchi's L9 orthogonal planning is implemented to review the upshot of governing aspects such as machining speed, feed rate, and depth of cut on vibrations and surface roughness (SR). To heighten all the three leading variables, the grey interactive exploration (GIE) is implemented. The grey interactive rating (GIR) is practiced as a multi-quality exclusive key (MQEK). The finest formation of central variables acquired from the investigational grades is cutting speed 500 m/min, feed rate 0.22 mm/rev and depth of cut 0.5 mm. ANOVA scrutiny signposts that feed rate is a crucial variable relating to the superiority yields. Products of endorsement pilots display that the ideal foremost variables developed the grey interactive rating from 0.6932 to 0.8138 for the numerous retorts. Scanning Electron Microscopic (SEM) scrutiny of cutting tool spectacles that fracture, chipping, abrasion and adhesion are the primary wear phenomena.

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“…The output data was gathered from the power consumption and the surface roughness of each sample. Typically, there are two techniques used to measure energy consumption: direct measurement technique by wattmeter or digital data logger [20,[29][30][31] and indirect measurement technique by measuring to cutting force with dynamometer [32,33]. The average energy consumption (kW) of each sample combination was measured through direct measurement technique by using a wattmeter (Peakmeter MS2205 3-Phase digital clamp meter) placed in the main electrical control panel of the machine.…”

Section: Materials and Experimental Setupmentioning

confidence: 99%

An artificial neural network approach to predict energy consumption and surface roughness of a natural material

2020

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Constructing a prediction model of machining performance is useful to improve its process efficiency. Artificial neural network (ANN) has been widely used in prediction works, capable of solving complex problems with numerous parameters. The present study aims to describe the application of the ANN technique in predicting the machining performance of a natural material. Bovine horns were the selected natural materials. Bovine horns are sustainable, recyclable, and abundant source for industrial applications. The outputs of the predictive model were surface roughness and energy consumption, whereas the input data were spindle speed, depth of cut and feed rate of a face milling. It was found that the ANN-based prediction model of bovine horns produced a high accuracy prediction (95.4%). The outcome of this study may be referred by similar studies on other natural materials, supporting the global efforts in improving the industrialization of natural materials.

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Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design

Cited by 53 publications

References 18 publications

Energy efficient cutting parameter optimization

Energy efficient cutting parameter optimization

Multi-Independent Optimization while Turning of Inconel-600 alloy using Grey Interactive Exploration

An artificial neural network approach to predict energy consumption and surface roughness of a natural material

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