An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model

Mahmood, Jawad; Luo, Ming; Rehman, Mudassar

doi:10.1007/s00170-021-08200-1

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…The outcomes of several experiments have demonstrated that RFs are capable of producing more accurate predictions than FFBP ANNs equipped with a single hidden layer and SVR. Mahmood et al [39] proposed a method that extracted the optimum conditions for the ball mill to achieve the desired surface finish for the process. Prediction of the tool life and the effect of hot machining on the tool life can be made using backpropagation ANN in hot machining.…”

Section: Ai In Manufacturingmentioning

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: Ai In Manufacturingmentioning

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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“…The validation of experimental results is usually performed in parametric optimization. [60][61][62] For this sake, the signal-to-noise (S/N) ratio is first calculated to gain the optimized levels of control variables for each output response. This S/N ratio formula is mentioned in Equations (7) and ( 8) and classified into larger the better value for MRR and smaller the better value for ROC and EWR.…”

Section: Parametric Optimization 41 | Signal-to-noise Ratio Analysismentioning

confidence: 99%

Machinability investigation in electric discharge machining of carbon fiber reinforced composites for aerospace applications

Hassan

Rehman

et al. 2022

Polymer Composites

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“…Li et al [17] used radar charts to integrate multi-source signal features and combined them with AdaBoost and Stacked BiLSTM for accurate tool wear prediction. Mahmood et al [18] employed the singular spectrum analysis algorithm to denoise and extract features from original force signal data. Utilizing principal component analysis techniques to reduce data dimensionality and one-hot encoding to transform the model's target variables from text to binary numerical format, they inputted these data into a BLSTM model, which successfully recognized the state of the tools.…”

Section: Introductionmentioning

confidence: 99%

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

Guo,

Wang

2024

Machines

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In the manufacturing sector, tool wear substantially affects product quality and production efficiency. While traditional sequential deep learning models can handle time-series tasks, their neglect of complex temporal relationships in time-series data often leads to errors accumulating in continuous predictions, which reduces their forecasting accuracy for tool wear. For addressing these limitations, the parallel convolutional and recurrent neural networks with attention-modulated residual learning (ParaCRN-AMResNet) model is introduced. Compared with conventional deep learning models, ParaCRN-AMResNet markedly enhances the efficiency and precision of feature extraction from time-series data through its innovative parallel architecture. The model adeptly combines dilated convolution neural network and bidirectional gated recurrent units, effectively addressing distance dependencies and enriching the quantity and dimensions of extracted features. The strength of ParaCRN-AMResNet lies in its refined ability to capture the complex dynamics of time-series data, significantly boosting the model’s accuracy and generalization capability. The model’s efficacy was validated through comprehensive milling experiments and vibration signal analyses, showcasing ParaCRN-AMResNet’s superior performance. In evaluation metrics, the model achieved a MAE of 2.6015, MSE of 15.1921, R2 of 0.9897, and MAPE of 2.7997%, conclusively proving its efficiency and accuracy in the precise prediction of tool wear.

show abstract

An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model

Cited by 13 publications

References 30 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

Machinability investigation in electric discharge machining of carbon fiber reinforced composites for aerospace applications

Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach

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