Methodology for adapting metal cutting to a green economy

Rao, Balkrishna C.

doi:10.1243/09544054jem1814

Cited by 11 publications

(4 citation statements)

References 48 publications

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“…The lower specific energies and discrete chip formation enabled by MAM at small R conditions can result in improved quality of machined surfaces, lower machining temperatures, and enhancements in material removal rates, with positive implications for sustainability of machining processes [31]. The image analysis of deformation can be applied also to the broader class of vibrationassisted machining processes to assess changes in strain and friction.…”

Section: Chip Separation Frommentioning

confidence: 99%

Effect of Low-Frequency Modulation on Deformation and Material Flow in Cutting of Metals

Yeung

Guo

Mann

et al. 2015

Journal of Tribology

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The deformation field, material flow, and mechanics of chip separation in cutting of metals with superimposed low-frequency modulation (<1000 Hz) are characterized at the mesoscale using high-speed imaging and particle image velocimetry (PIV). The two-dimensional (2D) system studied involves a sharp-wedge sliding against the workpiece to remove material, also reminiscent of asperity contacts in sliding. A unique feature of the study is in situ mapping of material flow at high resolution using strain fields and streaklines and simultaneous measurements of tool motions and forces, such that instantaneous forces and kinematics can be overlaid onto the chip formation process. The significant reductions in specific energy obtained when cutting with modulation are shown to be a consequence of discrete chip formation with reduced strain levels. This strain reduction is established by direct measurements of deformation fields. The results have implications for enhancing sustainability of machining processes and understanding surface deformation and material removal in wear processes.

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Section: Chip Separation Frommentioning

confidence: 99%

Effect of Low-Frequency Modulation on Deformation and Material Flow in Cutting of Metals

Yeung

Guo

Mann

et al. 2015

Journal of Tribology

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“…For consumers, product quality is very essential and one of the most prominent indicators for quality is the surface quality which also exerts a massive influence on the service life and reliability of manufactured objects. Other factors of a machined part, like fatigue strength, corrosion resistance, wear behavior, and the overall appearance of the product, depend on the surface quality [29][30][31]. A rough surface usually wears more quickly and has an elevated friction coefficient than a smoother surface does.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Ochengo

Zhao

et al. 2022

Advances in Materials Science and Engineering

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Hard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. The Taguchi method, based on the L18 orthogonal array, the variance analysis, the signal-to-noise ratios, and the response surface methodology have been used to optimize surface roughness (Ra) and cutting power (Cp). Optimum cutting parameters and levels were determined, and the relationship between cutting parameters and output variables was analyzed with the aid of two-dimensional and three-dimensional graphics. The results show that the most effective parameter on the surface roughness was the tool type (78%), while the most effective parameter on energy consumption was the cutting speed (90%). The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Besides, the impact of two-factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appears to be substantial. The linear regression models were validated using confirmation tests. Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model yielded results that were very similar to those achieved via real experiment (correlation values: 97.64% for surface roughness and 98.72% for energy consumption).

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“…Reducing the energy consumption in machining can significantly improve the environmental performance of manufacturing industry. 6 In this regard, Rao 7 presented some schemes for reducing the energy consumed in grinding, which paves the way for improved energy-efficient manufacturing processes. The basic energy-saving approach is to monitor and evaluate energy consumption of machine tools.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

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Methodology for adapting metal cutting to a green economy

Cited by 11 publications

References 48 publications

Effect of Low-Frequency Modulation on Deformation and Material Flow in Cutting of Metals

Effect of Low-Frequency Modulation on Deformation and Material Flow in Cutting of Metals

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

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