Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Patra, Karali; Jha, A. K.; Szalay, Tibor; Ranjan, Jitesh; Monostori, László

doi:10.1016/j.precisioneng.2016.12.011

Cited by 86 publications

(39 citation statements)

References 32 publications

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“…7. Time domain diagram of signal after WPD [3,1] 1250 to 2500 2 9 =512 3 [3,2] 2500 to 3750 2 9 =512 4 [3,3] 3750 to 5000 2 8 =256 5 [3,4] 5000 to 6250 2 8 =256 6 [3,5] 6250 to 7500 2 7 =128 7 [3,6] 7500 to 8750 2 7 =128 8 [3,7] 8750 to 10000 2 7 =128…”

Section: Comparison Of Classical Spectrum Subtraction Wavelet Packetmentioning

confidence: 99%

“…As it is an advanced sensor-and-signal processing technology, a growing number of scholars have been extensively adopting various kinds of sensors to ascertain the drilling process and drilling quality. The monitoring and prediction of tool wear and breakage in drilling are mainly done indirectly through thrust force [3] to [5]. Ferreiro et al [6] and [7] and Peña et al [8] completed the burr monitoring by extracting the features from the spindle torque signal in the drilling process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles. Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition. Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed. The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal. Highlights• A new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. • We have addressed the difficulty in extracting the weak features of vibration signals under intense background noise. • Compared with the traditional threshold de-noising method, the problem of threshold selection can be avoided.

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Section: Comparison Of Classical Spectrum Subtraction Wavelet Packetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

View full text Add to dashboard Cite

show abstract

“…Since the sensor does not have direct contact with the tool and does not affect the machining process, this class of methods is well-suited for practical working conditions in production workshops. At present, sensor-based tool data acquisition mainly includes the following signals; cutting force, vibration, acoustic emission, current, and torque [4][5][6]. In traditional tool wear detection methods, the acquired data is preprocessed with normalizing and denoising methods.…”

Section: Introductionmentioning

confidence: 99%

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Xie

Huang

et al. 2019

Symmetry

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To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.

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“…ANN is utilized for modelling and prediction purposes due to its advantages in nonlinear response and when time variability occurs. It covers the difficulty in inferring input/output mapping [4] and [5] and the search algorithms for optimization, based on genetic and evolution principles [6] to [8]. RSM is a reliable statistical tool for the mathematical modelling of engineering systems and for the optimization of different manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

2018

SV-JME

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Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM-and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.

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Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Cited by 86 publications

References 32 publications

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

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