An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

Xiao, Cao; Chen, Binqiang; Yao, Bin; Zhuang, Shiqiang

doi:10.3390/app9183912

Cited by 31 publications

(12 citation statements)

References 55 publications

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“…The obtained results can be used to build a new generation of more effective tool wear diagnostics systems [72]. Such systems allow direct and indirect energy savings [73].…”

Section: Discussionmentioning

confidence: 98%

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

2021

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There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.

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“…The obtained results can be used to build a new generation of more effective tool wear diagnostics systems [72]. Such systems allow direct and indirect energy savings [73].…”

Section: Discussionmentioning

confidence: 98%

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

2021

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“…In another work [22], RMS and linear regression of wavelet transformed signals have been used to estimate tool wear in ramp cuts in end milling. In [23], high SNR frequency band has been extracted using derived wavelet frames, the spectrum of the chosen band has been then injected in a 2D Convolutional Neural Network that recognizes wear signs of milling tool during cutting operation. In [24], the authors predicted flank wear of a drilling tool with acceptable accuracy using wavelet packet transform.…”

Section: Introductionmentioning

confidence: 99%

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Bouhalais¹,

Nouioua²

2021

Int J Adv Manuf Technol

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Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition, a worn tool generally produces low quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 minutes of machining, which was a necessary amount of time to exceed (300 µm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level and vibration comportment. First, Spectral kurtosis has been calculated for the twenty-one performed runs, the signals has then been decomposed with ICEEMDAN, the energy of the high frequency modes has been finally calculated. The Spectral Kurtosis has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it couldn't have been used for wear monitoring. On the other hand, the energy of optimal frequency ICEEMDAN modes has increased in direct proportion to the increase of surface roughness degradation and thus, to tool wear.

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“…Ou et al [30] proposed an online sequential extreme learning machine for tool wear state recognition with a stacked denoising autoencoder (SDAE) put forward to extract abstract features. Cao et al [31] proposed a 2-D CNN for milling tool wear monitoring, with the spectrum of the high signal-to-noise ratio vibration signals obtained from the derived wavelet frames as input features. Aghazadeh et al [32] employed a CNN with a hybrid feature extraction method using wavelet time-frequency transformation and spectral subtraction algorithms for tool wear estimation.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

et al. 2020

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Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

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An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

Cited by 31 publications

References 55 publications

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

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