Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

Marani, Mohsen; Zeinali, Mohammadjavad; Songmené, Victor; Mechefske, Chris K.

doi:10.1016/j.measurement.2021.109329

Cited by 66 publications

(23 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the proposed methodology, for data normalization purposes, the min–max method is used. The min–max normalization method applies a linear adjustment to the original data by using equation 3.1 (Marani et al , 2021). …”

Section: Methodsmentioning

confidence: 99%

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

View full text Add to dashboard Cite

Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…The predictors of the tool wear mainly include the theoretically mechanistic models (Ren et al, 2015;Zhu and Zhang, 2019;Goodall et al, 2020), the neural network of machine learning (Salgado and Alonso, 2007;Drouillet et al, 2016;Marani et al, 2021), and the vision-based monitoring (Dutta et al, 2016;You et al, 2020), etc. The predictive source data of the tool wears for different RUL predictors are normally from the 3-phase AC power (Salgado and Alonso, 2007;Li et al, 2008;Drouillet et al, 2016;Marani et al, 2021), the tool cutting sounds (Salgado and Alonso, 2007;Yen et al, 2013;Ren et al, 2015;Ubhanyaratne et al, 2017;Gomes et al, 2021;Marani et al, 2021), the workpiece vibrations (Gomes et al, 2021), the tool cutting forces (Yang et al, 2019;Zhu and Zhang, 2019;Gao et al, 2021), and the images of tool edges and workpieces (Castejon et al, 2007;Dutta et al, 2016;You et al, 2020). The predictive sensing devices of the tool wears are generally used the power cells (Salgado and Alonso, 2007;Drouillet et al, 2016;Goodall et al, 2020;Marani et al, 2021), the microphones (Salgado and Alonso, 2007;Gomes et al, 2021), the accelerometers (Gomes et al, 2021), the dynamometers (Yang et al, 2019;Zhu and Zhang, 2019), the acoustic emissions (Yen et al, 2013;Ren et al, 2015), and the camera sensors…”

Section: Introductionmentioning

confidence: 99%

Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing

Chang

Hsu

2023

AIEDAM

View full text Add to dashboard Cite

The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and predict different tool RULs, this study combines the analytical force modeling, the back-propagation neural network (BPNN) machine learning, and the current sensor which all are integrated in smart machine box (SMB) to realize the practical RUL prediction for on-line and real-time applications. The analytical model of the cutting force coefficients of shear and ploughing was established from average cutting forces, it could reduce the experimental number and predict the different cutting conditions. In general, the loading current of the cutting tool from a spindle motor is relatively easier acquired than the resultant forces. Thus, the loading currents of the spindle are used to train and predict the cutting forces using the BPNN model during intelligent manufacturing. The SMB architecture mainly performed the autonomous actions based on the edge layer, the fog layer, and the cloud layer via the TCP/IP, the MQTT protocol, and the unified communication library. Results showed that a predictive error for the ends of the tool life is about 3–10% that are based on the calculating of the cumulative current ratio.

show abstract

“…Therefore, simultaneously employing different sensors and fusion techniques can effectively improve the accuracy and reliability of the systems owing to complementary information [25][26][27][28][29][30][31][32][33][34]. Several approaches have been proposed using neural networks [35][36][37][38], the support vector machine [39][40][41], hidden Markov model [42][43][44], fuzzy inference system [45,46], relevance vector machine [47,48], and long short-term memory networks [49][50][51]. Most of them are based on data-driven approaches [52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

View full text Add to dashboard Cite

This study introduces a tool wear monitoring system that uses multiple sensors and a feature fusion technique. To improve the robustness of the system, different tightening torque and spindle speed conditions were considered in the experimental design stage. Vibration signals in three coordinates and sound signals were collected and transformed by fast Fourier transform for feature extraction. Two types of features in the frequency domain were used to establish the proposed system, including the mean value features selected by the class mean scatter feature selection criterion and statistical features by Gradient Class Activation Mapping++ (Grad-CAM++). The proposed monitoring system was established using a hierarchical neural network structure and feature fusion of the sensors. Cross-validation was introduced to demonstrate the performance and effectiveness of our approach under various tightening torque values and spindle speeds.

show abstract

Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

Cited by 66 publications

References 41 publications

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing

Robust tool wear monitoring system development by sensors and feature fusion

Contact Info

Product

Resources

About