Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining

Bagga, Prashant J.; Chavda, Bharat; Modi, Vrutang; Makhesana, Mayur A.; Patel, Kaushik M.

doi:10.1016/j.matpr.2021.12.131

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…The use of particle swarm and backpropagation techniques were demonstrated by Sun et al [21] for predicting the geometric deviation, and the authors claimed that machine learning is an effective approach as it predicts the output based on the available data and calculates better geometric deviation. SVR (support vector regression) is a common ML model that is used for predicting the life of the tool, specifically in a milling machine operation, and Bagga et al [22] demonstrated that a support vector machine (SVM) outperforms the MVR (multivariable regression) model, resulting in a decrease in downtime with proper predictive maintenance. The combination of different techniques, e.g., using a relatively smaller dataset of tool wear and applying time series methods to predict the previous data, was demonstrated by Kun et al [23].…”

Section: Background and Motivational Statementmentioning

confidence: 99%

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.

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Section: Background and Motivational Statementmentioning

confidence: 99%

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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“…These examples highlight the widespread application of information fusion technology in different fields to improve the accuracy and performance of tool monitoring and diagnosis and optimize the results of the decisions required to maintain tools. Bagga et al [ 11 ] proposed a multi-sensor data fusion method to measure and predict rear tool wear using various parameters, such as vibration, power, temperature, force, and surface roughness, and constructed an artificial neural network model for tool wear measurement and prediction. Wang et al [ 12 ] proposed a novel virtual tool wear sensing technology based on multi-sensor data fusion and artificial intelligence models, fusing multi-sensor data (such as force and vibration signals) with dimensionality reduction techniques and support vector regression models to infer tool wear parameters that are difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion

Wang,

et al. 2024

Sensors

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The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

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“…For example, Zhou et al [29] proposed an enhanced genetic algorithm-optimized Extreme Learning Machine for multi-sensor feature fusion. Bagga et al [30] proposed a new method based on multi-sensor data fusion for surface wear measurement and prediction, which achieved good results in the machining of AISI 4140 and provided an accurate tool wear prediction model for improving workpiece productivity and quality. He et al [31] fusion of temporal, frequency, and time-domain features from multiple sensor signals using a stacked network of sparse autoencoder networks.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Prediction of Milling Cutter Wear Based on an Empirical Formula for Cutting Force and Wear

Yu,

Zhan,

et al. 2024

Preprint

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Accurately predicting tool wear is essential for maintaining high machining quality. Currently, deep learning models are extensively utilized in predicting tool wear. However, deep learning models are prone to local optima, and limited tool wear sample data and multi-sensor feature fusion limit the model's ability to generalize and extract valid information. To address the above problems, this paper proposes a semi-supervised milling cutter wear prediction method based on an empirical formula for cutting force wear, which is modelled (IRM-CFAM) by the Inception-ResNet Module (IRM) and the Channel Feature Adaptive Module (CFAM). By introducing an empirical formula for cutting force wear, the cutting forces in the unlabeled samples are input to estimate wear values and construct wear curves. Based on the constructed wear curves, a monotonic loss function guided by an empirical curve of milling cutter wear is proposed. Meanwhile, the estimated wear values are combined with unlabeled samples to form a second data source, which is then merged with the labeled samples to expand the dataset. The constructed CFAM consists of channel attention and cross-attention mechanisms, which achieves adaptive fusion of weights. Following validation on the PHM2010 milling cutter dataset, the proposed method attained average RMSE and MAE values of 6.693 and 5.136, respectively, across three test sets, showing a significant advantage over other methods. This research facilitates accurate prediction of milling cutter wear and introduces a novel approach for expanding sample data.

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Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining

Cited by 9 publications

References 20 publications

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion

Semi-supervised Prediction of Milling Cutter Wear Based on an Empirical Formula for Cutting Force and Wear

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