Tool condition monitoring in micro-drilling using vibration signals and artificial neural network: Subtitle: TCM in micro-drilling using vibration signals

Patra, Karali; Jha, Amit Kumar; Szalay, Tibor

doi:10.1109/icieam.2017.8076196

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…This leads to tool failure after the 79th hole. The observation is in the similar line to that of trends reported earlier [45]. Figure 3c,d present all the 8 wavelet packets of 1st hole and the 79th hole (before the tool breakage), respectively.…”

Section: Wavelet Packet Features Of Vibration Signalssupporting

confidence: 89%

“…It can be seen that amplitudes of these signals increase non-linearly with the number of holes drilled. With increase in hole number, tool wear increases that contributes to increase RMS and mean values of these signals [23,45] shown in the figures. It can also be seen that RMS values of vibration signals increase suddenly just before the breakage of the tool [45].…”

Section: Time Domain Features Of Process Signalsmentioning

confidence: 97%

“…With increase in hole number, tool wear increases that contributes to increase RMS and mean values of these signals [23,45] shown in the figures. It can also be seen that RMS values of vibration signals increase suddenly just before the breakage of the tool [45]. On the other hand, the magnitudes of torque and thrust signals do not show any sudden change prior to tool breakage [23].…”

Section: Time Domain Features Of Process Signalsmentioning

confidence: 97%

“…Analytical models divulge better results of wear progression mechanism, but sometimes they are less accurate because of simplifications due to several assumptions. Artificial intelligence-based systems such as the artificial neural network (ANN) [43][44][45], the convolutional neural network (CNN) [46,47] and the adaptive neural fuzzy inference system (ANFIS) [48] are considered feasible, consistent and smart approaches for predicting the condition of cutting tools. These techniques can realize non-linear relations among machining parameters, process signals and tool states for their high fault tolerance and adaptability features.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Ranjan

Patra

Szalay

et al. 2020

Sensors

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The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

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Section: Wavelet Packet Features Of Vibration Signalssupporting

confidence: 89%

Section: Time Domain Features Of Process Signalsmentioning

confidence: 97%

Section: Time Domain Features Of Process Signalsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Ranjan

Patra

Szalay

et al. 2020

Sensors

Self Cite

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“…Several studies have been conducted to diagnose this issue in advance, since tool wear is directly related to the production cost of the product. A diagnostic model was developed to predict tool wear by measuring the acceleration of the main shaft, acoustic signal, load of the main shaft, and cutting load [9][10][11][12]. Totis predicted tool wear by installing a dynamometer on the table of machine tools and monitoring the cutting force during machining [13].…”

Section: Introductionmentioning

confidence: 99%

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Park

Lee

Yeo

et al. 2022

Sensors

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Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.

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Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications

Muthuswamy¹,

Shunmugesh²

2022

Int J Interact Des Manuf

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Tool condition monitoring in micro-drilling using vibration signals and artificial neural network: Subtitle: TCM in micro-drilling using vibration signals

Cited by 9 publications

References 21 publications

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications

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