Prediction of cutting forces including tool wear in high-feed turning of Nimonic® C-263 superalloy: A geometric distortion-based model

Amigo, Francisco Javier; Urbikaín, Gorka; Lacalle, Luis Norberto López de; Pereira, Octavio; Fernández-Lucio, P.; Fernández-Valdivielso, A.

doi:10.1016/j.measurement.2023.112580

Cited by 14 publications

(5 citation statements)

References 39 publications

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“…If the vector system u 0 , u 1 , Á Á Á , u n is linearly independent, then the coefficient matrix of the regular equations ( 1) is a symmetric positive definite matrix, and the unique solution a Ã 0 , a Ã 1 , Á Á Á , a Ã n can be obtained by the square root method. Therefore, the fitting function is obtained, as shown in equation (2).…”

Section: Test Scheme Designmentioning

confidence: 99%

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Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

Zhang,

Zhao

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper presents a steady state cutting force prediction model for CNC lathe considering feed displacement. The frequency spectrum characteristics of the cutting force in principal direction are analyzed. The turning test for 12Cr18Ni9 stainless steel workpiece is carried out by CNC machine tool ETC1625P firstly, and the real-time displacement data of worktable in the turning process are obtained. The cutting force data varying with the displacement of the worktable under different cutting parameters are obtained by cubic spline interpolation. The mechanical force model including metal shearing force and plowing force is established, so as to predict the steady state cutting force accurately corresponding to different displacement of the worktable. The influence of different cutting parameters on the amplitude frequency characteristics for the low frequency part of the cutting force in principal direction is studied by using wavelet packet decomposition and fast Fourier transform. The results show that the method can predict the steady cutting force accurately and reflect the amplitude frequency characteristics of the low frequency part for the cutting force in principal direction under different cutting parameters.

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Section: Test Scheme Designmentioning

confidence: 99%

“…CNC lathe is an advanced machining equipment with relatively complex structure, high degree of automation 1 and a large proportion, 2 the research on its dynamic machining performance has always been a hot issue. Cutting force 3 is an important parameter in the cutting process.…”

Section: Introductionmentioning

confidence: 99%

Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

Zhang,

Zhao

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In their research, Li et al [ 6 ] introduced a force-based system, with experimental results reaching 96.76% accuracy. Research by leading authors, such as the work carried out by Amigo et al [ 7 ] and Urbikain et al [ 8 ], focused on cutting force signals in relation to tool wear, has confirmed that in terms of pure accuracy, this is one of, if not the best, indirect measure signal sources. Despite their excellent qualities, the use of force-based systems has some drawbacks, as obtaining these values requires sensitive dynamometers and often other sensors.…”

Section: Introductionmentioning

confidence: 99%

Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis

Turšič,

Klančnik

2024

Sensors

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In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools.

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“…It has an impact, among others, on the components of the cutting force, the stability of the process, or the topography of the surface [ 4 , 8 ]. The effect of the entering angle on the cutting process was tested by Amigo et al [ 9 ], but only in the turning process. It was shown that the use of low entering angles has a positive effect on tool life.…”

Section: Introductionmentioning

confidence: 99%

Modelling of the Face-Milling Process by Toroidal Cutter

2023

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When face milling using a toroidal cutter, with a change in the depth of the cut, the entering angle varies as well. An experimental test of the influence of cutting parameters, such as the depth of the cut and the feed per tooth on the cutting force components and surface roughness parameters, was conducted. The experimental test was carried out using a DMU 100 monoBLOCK CNC machine with registration of cutting force components and surface roughness parameters Ra, Rz, and RSm. FEM analysis of the face-milling process was also carried out and compared with the experimental results. The average deviation of the FEM values for cutting force components does not exceed 12%. Experimental models were established for each force component. It was shown that the depth of the cut has the strongest influence on each force component. The feed per tooth has a little impact on the cutting force. The obtained model of the feed force component is the most complex, and the model of the Fa component is only linear. The influence of the ap parameter on the surface roughness parameters is nonlinear and nonmonotonic. In the range of approx. ap = 2 mm, there is a maximum surface roughness.

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Prediction of cutting forces including tool wear in high-feed turning of Nimonic® C-263 superalloy: A geometric distortion-based model

Cited by 14 publications

References 39 publications

Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

Steady state prediction model and spectrum characteristics analysis of cutting force for CNC lathe considering variable machining parameters

Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis

Modelling of the Face-Milling Process by Toroidal Cutter

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