Flank wear assessment on discrete machining process behavior for Inconel 718

Kuppuswamy, Ramesh; Zunega, Jonee; Naidoo, Samiksha

doi:10.1007/s00170-017-0623-4

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…The flank wear value and the wear degree classification were obtained accordingly. 20 To investigate the complex wear mechanisms and hardness difference between different parts of the drill bit in the high-speed process, 21,22 the flank wear was measured after drilling 10 consecutive holes at 1/12 and 4/12 of the diameter on both the left and the right cutting edge, as shown in Figure 6, and the calculation method was as follows…”

Section: Experimental Scheme and Platformmentioning

confidence: 99%

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Guo-qiang

Tang

2021

Composites and Advanced Materials

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Carbon fiber-reinforced polymer (CFRP) drilling is a typical process in the aircraft industry. Because the components of CFRP are different and uneven, it is difficult to extract tool wear characteristics from the machining signals, which are composed of the processing characteristics of various materials and the tool state characteristics. The aim of this work is to present a new comprehensive approach based on multicharacteristics and multisignal sources to predict the tool wear state during CFRP drilling through a combination of a backpropagation (BP) artificial neural network (ANN) model and an efficient automatic system depending on the sliding window algorithm. It was verified that the peak factor and Kurtosis coefficient of different signals and the energy value of the d5 layer of the thrust force signal and the d3 layer of the vibration signal after wavelet decomposition were related to tool wear. Among them, the energy value of the d3 layer of the vibration signal was selected as the wear indicator and was able to describe the state of the tool during the CFRP drilling process regardless of the drilling conditions and individual tool differences. A confirmatory drilling experiment using 6-mm-diameter polycrystalline diamond twist drilling under different processing parameters was conducted to verify the ANN model based on multicharacteristics and multisignal sources. A lower feed speed and a higher cutting speed were both highly correlated with the VB value of flank wear. Drill wear accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90. The accuracy of the neural network model is 80–87% when using the value of only one characteristic but clearly increases based on multicharacteristics and multisignal sources in real time, indicating that the BP ANN model has higher accuracy in predicting the tool state in CFRP drilling through the sensor signal fusion method.

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Section: Experimental Scheme and Platformmentioning

confidence: 99%

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Guo-qiang

Tang

2021

Composites and Advanced Materials

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“…Additionally, in order to clearly observe the wear differences of the blades, a proper milling distance had to be selected. Kuppuswamy et al 18,19 reported that the growth of the flank wear was erratic and rapid at the first 10−25 m of cutting distance. The cutting tool used in this work was a high-feed milling cutter, the diameter of which was 32 mm.…”

Section: Factors Affecting the Differential Wear On Tool Insert Bladementioning

confidence: 99%

Development of the innovative differential tool wear modeling for high-feed milling and its experimental verification

Zhao

Cheng

Jiang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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During the high-feed milling process, the vibrations generated by interrupted cutting cause changes in the instantaneous tool posture, as well as in the working angle and the distribution of the thermal stress coupling fields of each tool blade. These changes result in significant differences in the wear distribution of each tool blade. In this research, well-designed experiments for the high-feed milling of titanium alloys were carried out to identify the key factors affecting the differential wear on the milling tool insert blades. A differential tool wear model for the tool blades was developed in order to comprehensively describe the effects of the location error of the blades, the vibrations in the tool posture, and the working angle of each tool blade. The wear status of the milling tool was simulated based on the dynamic tool trajectories and postures derived by the model, and the entire simulated wear distribution was investigated with an innovative wear boundary recognition method. The differential tool wear model was evaluated and validated by the milling experiments and further supported by simulations.

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Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques

Singh

Gupta

Mia

et al. 2018

Int J Adv Manuf Technol

141

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Flank wear assessment on discrete machining process behavior for Inconel 718

Cited by 10 publications

References 16 publications

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Development of the innovative differential tool wear modeling for high-feed milling and its experimental verification

Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques

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