A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks

Küçükyıldız, Gürkan; Demir, Habibe

doi:10.1007/s13369-021-05709-1

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The study showing that constructing prediction model with multi-type signals as input has a stronger anti-interference capacity [13]. For instance, the deep learning model with multiple input parameters such as cutting force, vibration, acoustic emission, etc., performs well in tool wear prediction [14][15]. However, in the practical equipment operation, it is common for the sensors' installation to be limited because of insufficient space.…”

Section: Introductionmentioning

confidence: 99%

Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion

Wang,

Zeng

2023

IEEE Access

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In order to solve the problem of low accuracy of tool wear detection due to the poor quality of generated data under small sample problems, a deep learning model based on data enhancement and feature fusion is proposed. Firstly, in order to solve the problem that there is no quality evaluation standard in the training process of the traditional generative adversarial network (GAN), the K nearest neighbor algorithm is proposed to test the data generated by the GAN model for the second time. The improved GAN model can be automatically trained to get the optimal model according to the second test results. Secondly, in order to enhance the antiinterference effect of the model, a double-path parallel convolutional neural network (DPCNN) which combines with the characteristics of time domain and time-frequency domain is constructed to analyze the wear data.Furthermore, the hyperparameters of the model are optimized by Bayesian optimization algorithm (BOA). Finally, the effectiveness of this method is verified in the saw blade wear detection experiment. The results show that the performance of this model is better than other models, and the accuracy rate in the experimental detection reaches 100%.INDEX TERMS Bayesian optimization algorithm, double-path parallel convolution neural network, feature fusion, generative adversarial networks, saw blade wear prediction I.

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Section: Introductionmentioning

confidence: 99%

Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion

Wang,

Zeng

2023

IEEE Access

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“…Although the direct monitoring method is convenient and accurate, it requires the measurement of the cutting tool flank wear values offline, which results in the reduction of the production efficiency. The indirect monitoring method determines the wear condition of a tool by acquiring sensor signals generated during mechanical processing, such as cutting force signals, 2–4 vibration signals, 5 acoustic emission signals, 6 current signals, and spindle power signals. 7 In comparison, the indirect monitoring method does not affect the machining process.…”

Section: Introductionmentioning

confidence: 99%

Cutting tool wear monitoring in milling processes by integrating deep residual convolution network and gated recurrent unit with an attention mechanism

Zhou

Pan

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The conditions of cutting tool wear in the machining process have a great influence on the dimension accuracy and surface integrity of a workpiece. Real-time tool condition monitoring can alert a worker to changes in a tool in time before tool failure occurs to reduce downtime and improve product quality. However, traditional monitoring methods are no longer able to meet the requirements of real-time monitoring in current intelligent manufacturing. Therefore, an online monitoring method based on deep learning to monitor the cutting tool conditions is proposed In this research. In this monitoring model, one-dimensional wide-kernel convolution is used to filter the high-frequency noise in the multi-dimensional raw signals, and then a deep residual convolution layer is adopted to adaptively extract the multi-dimensional spatial features of the signals, and a gated recurrent unit (GRU) layer with an attention mechanism is employed to adaptively extract the hidden temporal features of the sequence data and adaptively assign the corresponding weights, and finally, a regression layer is used to predict the wear value of the tool according to the extracted features. The PHM 2010 challenge dataset is used to illustrate and evaluate this proposed method. The experiment results show that the proposed method can accurately predict the future flank wear values in real time based on the acquired multi-dimensional signals and avoid the complexity of the manual feature extraction.

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“…Another study used the cutting tool force of a lathe machine to monitor tool wear through statistical processing of its wavelet transform to differentiate among initial wear, normal wear, and severe wear [23]. Similar to the previous study, the health wear status of a cutting tool was monitored via cutting forces and vibration signal analyses to determine when faults developed on the cutting tool through an algorithm developed in MATLAB [24]. Since force sensors are robust and reliable, another study extracted harmonic features from force signals of tools with different wear in order to select the most prominent wear indicator features to input into a tool condition monitoring system [25].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring to Enable Predictive Maintenance on a Six-Die Nut Manufacturing Machine through Force Data Analysis

Novelo

Chu

2022

Applied Sciences

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Nut fasteners are produced by machines working around the clock. Companies generally operate with a run-to-failure or planned maintenance approach. Even with a planned maintenance schedule, however, undetected damage to the dies and non-die parts occurring between maintenance periods can cause considerable downtime and pervasive damage to the machine. To address this shortcoming, force data from the fourth and sixth dies of a six-die nut manufacturing machine were analysed using correlation to the best health condition on the force profile and on the force shock response spectrum profile. Fault features such as quality adjustments and damage to both die and non-die parts were detectable prior to required maintenance or machine failure. This detection was facilitated by the determination of health thresholds, whereby the force SRS profile generated a longer warning period prior to failure. The analytical approach could benefit the industry by identifying damage that would normally go undetected by operators, thereby reducing downtime, extending die life, enabling “as needed” maintenance, and optimising machine operation.

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A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks

Cited by 6 publications

References 24 publications

Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion

Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion

Cutting tool wear monitoring in milling processes by integrating deep residual convolution network and gated recurrent unit with an attention mechanism

Condition Monitoring to Enable Predictive Maintenance on a Six-Die Nut Manufacturing Machine through Force Data Analysis

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