Monitoring tool wear and surface roughness in the face milling process of high-strength compacted graphite cast irons

Silva, L. R. R. da; França, Pedro Henrique Pires; Andrade, Clf; Silva, R. B. Da; Guesser, Wilson Luiz; Machado, Álisson Rocha

doi:10.1007/s40430-021-02897-7

“…As can be seen, the inset graph shows a pe ulated signal, where the cutting force signal variations can be identified. Th current profiles are commonly reported [14] and can be found when measu force with dynamometers [6]. Signal filtering is necessary to remove the en isolate and analyze the signal variations corresponding to the cutting force, a in Figure 6.…”

Section: Time and Frequency Analysismentioning

confidence: 99%

“…The electric current signal can be processed to obtain different machinability parameters to characterize tool life and workpiece surface roughness. Skewness and kurtosis effectively identify the workpiece surface conditions [14].…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency Approach for Cutting Tool Power Signal Separation in Face Milling Operations

Rubio,

Jáuregui-Correa

2024

Applied Mechanics

0

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Face milling is among the processes that can produce a high-precision surface finish. Tool condition monitoring and signal processing algorithms are under extensive research to improve production quality and productivity in machining processes. In the present research, the time–frequency analysis technique was applied to the signal obtained from a sensor integrated into the primary AC power circuitry during the milling of steel bars to evaluate its applicability in detecting the current variations associated with the cutting force. The signal acquired from the sensor was processed in the time–frequency domain using wavelet analysis, and the results were compared with the traditional time and frequency analyses. The results showed that the signal variations produced by the cutting force were well localized in the frequency spectra with both approaches. However, the wavelet processing method yielded a poorly defined cutting force signal shape due to the limited resolution inherent in the sub-bands containing the frequencies of interest.

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“…The indirect method makes up for the shortage of direct measurement, which is realized by sensor signals related to tool wear. The tool condition is estimated according to measurable signals, such as forces [6][7][8][9], acoustic emission (AE) [10][11][12][13], vibration [14][15][16], and motor current [17][18][19]. Li [7] extracted 14 time-domain features from forces and established the relationship between time-domain features and tool wear state with the v-SVR model, which was used to monitor the tool wear in turning.…”

Section: Introductionmentioning

confidence: 99%

Milling force coefficients-based tool wear monitoring for variable parameter milling

Pan

¹

,

Zhang

²

,

Zhang

³

et al. 2022

Int J Adv Manuf Technol

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Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

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“…The indirect method makes up for the shortage of direct measurement, which is realized by sensor signals related to tool wear. The tool condition is estimated according to measurable signals, such as forces [6][7][8][9], acoustic emission (AE) [10][11][12][13], vibration [14][15][16], and motor current [17][18][19]. Li [7] extracted 14 time-domain features from forces and established the relationship between time-domain features and tool wear state with the v-SVR model, which was used to monitor the tool wear in turning.…”

Section: Introductionmentioning

confidence: 99%

Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

Pan

¹

,

Jun

²

,

Zhang

³

et al. 2021

Preprint

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Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

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Monitoring tool wear and surface roughness in the face milling process of high-strength compacted graphite cast irons

Cited by 9 publications

References 20 publications

Time–Frequency Approach for Cutting Tool Power Signal Separation in Face Milling Operations

Time–Frequency Approach for Cutting Tool Power Signal Separation in Face Milling Operations

Milling force coefficients-based tool wear monitoring for variable parameter milling

Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

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