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

Abstract

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.

An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series

“…Previous works in the manufacturing domain for tool wear prediction [62][63][64][65][66] utilized different data sets, which either include one or a few time series features, making it difficult for direct comparison with ACLAE-DT as it is tested against a richer data set with 44 time series features. In the literature, no other work has utilized the entire data set in [59] to simultaneously detect anomalies and identify the anomalous root causes while employing ML or DL methods.…”

“…In the literature, no other work has utilized the entire data set in [59] to simultaneously detect anomalies and identify the anomalous root causes while employing ML or DL methods. Moreover, the accuracy metric was utilized in [62][63][64][65][66], potentially being misleading since the metric can be heavily skewed towards finding non-anomalous points, which usually dominate real-life data sets, and the accuracy metric was not used in this paper for direct score comparisons.…”

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

“…Whereas the forward and backward LSTMs are combined to form the Bi-directional LSTM. The architecture of different LSTM versions is also discussed by Kolekar et al, Chandra et al, and Zhao et al [ 45 , 46 , 47 ].…”

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

“…It is used to examine how closely the modeling data matches the actual ones. According to Kumar et al (2022), a small value generated from the error size represents a better model. The mean absolute percentage error (MAPE) was used to measure the accuracy of each modeling method using the following formula:…”