Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Wang, Qun; Wang, Hengsheng; Hou, Liwei; Yi, Shengjie

doi:10.3390/app112412041

Cited by 13 publications

(6 citation statements)

References 100 publications

(132 reference statements)

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“…Hidden Markov Model (HMM) [36], and Adaptive Network Based Fuzzy Inference Systems (ANFIS) [56] Neural Networks (CNNs) [48]. In recent years, the amount of studies on the application of CNN in tool wear estimation has increased [55]. Liu et al [39] conducted a new CNNtransformer neural network model to obtain a more suitable tool wear estimation.…”

Section: Eksploatacja I Niezawodnosc -Maintenance and Reliabilitymentioning

confidence: 99%

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak¹

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

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Section: Eksploatacja I Niezawodnosc -Maintenance and Reliabilitymentioning

confidence: 99%

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak¹

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…In the contemporary world, all industrialized developed countries carry out strategic layouts. Germany has proposed the national development plan of "Industry 4.0", and China has put forward the national strategic plan of "Made in China 2025" [4], [5]. CNC machine tools, serving as the "industrial mothership" of manufacturing, not only represent the degree of industrial development of the country but also represent the core competitiveness of the country [6].…”

Section: Introductionmentioning

confidence: 99%

An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing

Dong,

Zhao

2024

IEEE Access

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Computer numerical control (CNC) machine tools play a crucial role in the manufacturing industry, and cutting tools, as key functional components, directly impact the quality of the machining process. An improved autoEncoder with multi-head attention for tool wear prediction is proposed. MultiCNN-Attention-GRU (MCAG) consists of an encoder and decoder. The encoder contains multiple sets of Convolutional Neural Networks (CNNs) and CNNs adaptively extract signal features. The decoder includes Multi-Head Attention (MHA) and Gated Recurrent Unit (GRU), which can adaptively enhance the relevant feature weights and extract long-term, deep different features. For the model training, a monotonicity loss function is defined. The proposed method is validated on the 2010 PHM Data Challenge (PHM2010) public dataset. The original dataset is dimensionally reduced and then resampled. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an Mean Absolute Error (MAE) of 6.15 and Mean Square Error (MSE) of 79.6, which is approximately 1.6 and 13 lower than the second-place algorithm. The result validates the superior performance of the proposed model compared to other deep learning algorithms in predicting tool wear.

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“…In the existing TCM system based on a data-driven model, according to whether the data processing model needs training data, it is mainly divided into a supervised TCM system and an unsupervised TCM system. A supervised TCM system requires more or less teacher data to train model parameters, such as Hidden Markov Model [9,10], Neural Network [11][12][13], SVM [14,15], Deep Learning [16,17], etc. The Hidden Markov Model (HMM) is a Markov chain and a production model.…”

Section: Introductionmentioning

confidence: 99%

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Zhong

Liao

et al. 2023

Applied Sciences

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Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.

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Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Cited by 13 publications

References 100 publications

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

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