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

Su, Yu; Li, Congbo; Zhao, Guoyong; Li, Chunxiao

doi:10.1177/0954405420971064

Cited by 24 publications

(14 citation statements)

References 32 publications

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“…Figure 14 shows that the length of the cutting trajectory of a single abrasive in the grinding arc becomes longer with increasing amplitude exerted on the workpiece in TUAG, which accelerates the grinding wheel wear process and increases the ground surface roughness Ra. 41 The surface roughness Ra has a minimum value with an appropriate ultrasonic vibration amplitude.…”

Section: Resultsmentioning

confidence: 99%

Generation mechanism modeling of surface topography in tangential ultrasonic vibration-assisted grinding with green silicon carbide abrasive wheel

Qiu

Yin

Cao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Tangential ultrasonic vibration-assisted grinding (TUAG) has a wide prospect in machining difficult-to-machine materials. However, the surface generation mechanism in TUAG is not fully recovered. This study proposes an analytical model of the surface topography produced by TUAG. Based on the model, the surface topography and roughness are predicted and experimentally verified. In addition, the influence of the grinding parameters on the surface topography is analyzed. The predicted surface topography well coincides with experimental measurements, and the prediction error in surface roughness Ra by the proposed model is less than 5%. Compared with conventional grinding, TUAG produces a surface with more uniform scratches and surface roughness Ra was reduced by up to 27% with the proper parameters. However, the improvement of surface roughness in TUAG is weakened when grinding speed or depth of cut increases. Moreover, the influence of the ultrasonic vibration amplitude on the surface roughness is not monotonous. With the grinding parameters selected in this study, TUAG with an ultrasonic amplitude of 7.5 μm produces the minimum surface roughness.

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Section: Resultsmentioning

confidence: 99%

Generation mechanism modeling of surface topography in tangential ultrasonic vibration-assisted grinding with green silicon carbide abrasive wheel

Qiu

Yin

Cao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…G point coordinates in the cutting zone trajectory by cutting edge jth are determined by:. See equation (12) below.…”

Section: See Equation (3) Belowmentioning

confidence: 99%

“…Ki et al [11] commented that singularities analysis is applied early in image processing, in which a sudden change in the pixel is usually expressed through a singularity change so singularity point is used to define boundaries in the images. The study by Su et al [12] report that during machining under the supervision of sensors, tool wear is easily recognized through singularity analysis. Wavelet analyzes together with the Fourier transforms are the commonly used tools in singularity point analysis from the received signals, these tools can provide better local frequency characteristics over the time domain.…”

Section: Introductionmentioning

confidence: 99%

Application of vibration singularity analysis, stochastic tool wear, and GPR-MOPSO hybrid algorithm to monitor and optimise power consumption in high-speed milling

Hoang Tien,

Duc Quy,

Pham Thi Thieu

et al. 2022

Manufacturing Rev.

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Power consumption in manufacturing direct affects production costs and the environment. Therefore, the process of evaluating and researching power consumption in the machining process is very important. During high-speed milling, the power consumption varie`s due to tool wear and radial deviation. Therefore, a new model power consumption optimization is proposed based on cutting mode factors taking into account tool wear and radial deviation. In the existing power consumption models, studies on the effects of radial deviation and tool wear have not been thoroughly investigated. Stochastic tool wears established during high-speed milling is established in combination with the cutting force analysis model and wavelet singularity vibration point analysis. The nonlinear processes due to stochastic tool wear and cutting edge geometry were considered in the model. To optimize power consumption and establish a model for the real-time prediction of power consumption, a new GPR–MOPSO hybrid algorithm was developed based on Gaussian process regression (GPR) and multi-objective particle swarm optimizations (MOPSO). In order to verify the feasibility proposed monitoring and optimization model, experimental processes high-speed milling have been established. Results showed that the presented improvement model will reduce power consumption by 20.38% compared with manufacturer manuals chosen process parameters.

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“…3,4 Many influencing factors, such as cutting parameters, cutting forces, workpiece materials, tool parameters, and the vibration between the workpiece and tool, should be considered in the 3D surface topography prediction model. 5,6 According to the research status of surface topography prediction, there are three methods: artificial intelligence, 1,4,[7][8][9] experimental methods, [10][11][12][13][14][15][16] and theoretical analysis methods. 17,18 The advantages and disadvantages of the three methods are apparent.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the surface roughness modeling and analysis for ultra-precision diamond turning processes constrained by the complex multisource factors

Yang

Sun

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Ultra-precision machining is widely used in the manufacturing of national defense and sophisticated civil products because of its high precision, nearly no surface damage and other advantages. The surface roughness of a workpiece is the key indicator of ultraprecision machining technology, which is affected by many factors in the process of machining. However, the existing surface roughness prediction model only considers a single error factor between the tool and workpiece, lacking comprehensive consideration of multisource error factors. Therefore, based on the research of the single error factor, multisource errors that affect the surface roughness of a workpiece in machining was investigated in this paper. A prediction model of workpiece surface roughness under multisource error affecting was established to improve the accuracy of prediction models. According to the vibration model, the sensitive direction of roughness can be determined. In the axial direction, the error of each point on the surface is superimposed with the theoretical surface shape to obtain the final surface profile. The three-dimensional morphology was established and the two-dimensional topography can be obtained by intercepting the three-dimensional topography. The roughness simulation value is obtained by extracting the peak value of each point of the two-dimensional topography. A single-point diamond turning experiment is performed to verify the simulation models. The result concluded from the experiment is compared with the simulation result, showing that the error between the simulation and the experiment is less than 4%, which is of great significance to optimizing machining process parameters and improving machining precision of workpiece surfaces for ultraprecision single point diamond turning.

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

Cited by 24 publications

References 32 publications

Generation mechanism modeling of surface topography in tangential ultrasonic vibration-assisted grinding with green silicon carbide abrasive wheel

Generation mechanism modeling of surface topography in tangential ultrasonic vibration-assisted grinding with green silicon carbide abrasive wheel

Application of vibration singularity analysis, stochastic tool wear, and GPR-MOPSO hybrid algorithm to monitor and optimise power consumption in high-speed milling

Investigation on the surface roughness modeling and analysis for ultra-precision diamond turning processes constrained by the complex multisource factors

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