Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks

Lim, Meng Lip; Derani, Mohd Naqib; Ratnam, Mani Maran; Yusoff, Ahmad Razlan

doi:10.1007/s00170-022-09257-2

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…With this method, they obtained frequency and time domain features from shear force and vibration signals and combined feature tensors. Lim et al [38] enhanced the deep learning regression approach to estimate tool wear through features obtained from 2D visual data of the workpiece's surface. They compared the models developed based on CNN and deep neural networks for predictive accuracy.…”

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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“…This deviation can be analyzed and processed by the control system as a feedback quantity, and then the closed-loop manufacturing system can be adjusted again to improve the workpiece and meet the requirements of the turning process. Through the feedback mechanism, the deviation of the manufacturing system is automatically adjusted to achieve the improvement of workpiece machining precision [20].…”

Section: Error Compensation Correction Strategy Designmentioning

confidence: 99%

Genetic algorithm-based error correction algorithm for CNC turning machining of mechanical parts

Xue,

Miao,

Xue

2023

J. meas. eng.

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This paper discusses how to improve the machining precision in the turning of slender shaft. The main cause of dimensional error in slender shaft machining is analyzed by establishing dimensional error model and using genetic algorithm to optimize cutting parameter selection. Based on this, the proportional-integral-differential control error compensation is proposed to reduce the error in the turning process of slender shaft. Through the simulation experiment, the machining size error of slender shaft under different cutting parameters is obtained. It is found that the increase of back blowing and feed rate will aggravate the dimensional error, while the increase of CS will reduce the dimensional error. The error after the proportional-integral-differential control error compensation is much smaller than that without the error compensation. The experimental results show that the method is reliable in reducing the errors in the turning of slender shaft, and can realize the machining mode with higher precision and efficiency. This is of great significance to the development of machinery manufacturing industry.

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“…The smaller the mean square error RMSE and the closer the coefficient of determination R2 is to 1, the higher the accuracy and precision of the prediction model. ∑ (𝑦 𝑡 − 𝑦 ̅) 2 𝑚 𝑡=1 (11) where, m is the number of samples output from the fully connected layer, the number of samples in this paper is 315, and y ̂t is the predicted value of tool wear, and y t is the actual value of tool wear.…”

Section: Setting Of Prediction Model Parametersmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) have strong feature extraction capability and low computational complexity compared with long and short-term memory networks (LSTMs), and can tap deep features hidden in samples. Lim Meng Lip [11] et al cropped the surface profile images of machined parts and input them into CNN networks for tool wear prediction, and the results showed that the CNN model can meet the tool wear prediction requirements with an accuracy of 98.9 % accuracy. Although these methods have been successful in predicting tool wear, it is still challenging to fully reveal the effective features present in the monitored signals due to the defects in the network structure [12] .…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

WANG,

YU,

GUO

et al. 2022

Mech. Eng. Sci.

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In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold, a CNN-LSTM tool wear prediction model based on particle swarm algorithm (PSO) optimization with multi-channel feature fusion is proposed. Firstly, the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction; secondly, the time-domain, frequency-domain and time-frequency-domain features of each channel signal are extracted, and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features; finally, the data set is input to the CNN-LSTM-PSO model for training and testing. The results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848, a root mean square error RMSE value of 0.7281, and a coefficient of determination R2 value of 0.9964; and compared with the BP model, CNN model, LSTM model and CNN-LSTM model, its tool wear prediction accuracy improved by 7.56%, 2.60%, 2.98%, and 1.63%, respectively.

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Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks

Cited by 13 publications

References 24 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

Genetic algorithm-based error correction algorithm for CNC turning machining of mechanical parts

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

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