Tool Wear In Milling Research Articles

Comprehensive analysis of cutting temperature, tool wear, surface integrity and tribological properties in sustainable milling of Ti6Al4V alloy: LN2, nanofluid and hybrid machining

Despite being expensive and difficult to process, the Ti6Al4V alloy is a vital component for crucial industries. To improve its machinability and accomplish sustainable production, environmentally friendly cooling and lubricating agencies are used. Studies on the machinability of the alloy are still necessary because of its unique features and significance in vital industries like aerospace, defense, and medicine. Therefore, this investigation focuses on tool wear, temperature, and surface integrity for sustainable milling Ti6Al4V under various machining environments, i.e., dry, pure-MQL, LN2, hBN, CuO-doped nanofluids, and hybrid methods. The produced nanofluids' thermophysical and rheological characteristics were examined in the study's initial phase. Because of the results from the first stage, machining performance indicators were assessed in the subsequent milling experiments. As a result, CuO-doped nanofluids gave improved results in terms of viscosity and pH. The best results obtained in the LN2 + CuO hybrid cooling lubrication environment in important machinability outcomes such as tool wear and surface integrity were attributed to the rheological properties of CuO-doped nanofluid and its harmonious cooperation with LN2-cryogenic cooling.

A data-driven method for prediction of surface roughness with consideration of milling tool wear

A data-driven method for prediction of surface roughness with consideration of milling tool wear

Enhancement of tool life using magneto-rheological fluid damping and tool wear prediction through deep learning model in milling

Undesirable vibrations generated during milling significantly intensify milling tool wear and reduces tool life. Hence it is imperative to employ the effective vibration control strategy during milling to optimize the overall effectiveness of the process. Thus, the present study incorporates the magneto-rheological fluid damping for vibration mitigation during the milling to enhance the life of a cutting tool under different operating conditions. Simultaneously, the data-driven modelling are employed to predict the tool wear using real-time multi-sensor machining data under different operating conditions. Accordingly, the real-time multi-sensor data was acquired at different operating conditions during normal and vibration-damped milling. Later, the extracted tine-domain features were selected using the sequential feature selection with Random Forest Regressor to obtain the most relevant time-domain features from multi-sensor data for improving the model performance. These selected features are then fed to the different deep-learning algorithms for tool wear prediction. Thus, Magneto-rheological fluid damping significantly reduces the vibrations and cutting forces in vibration-damped milling between 65 and 80% and improves the tool life by 18–21 % compared to normal milling under different operating conditions. Moreover, among the different deep learning algorithms, hybrid Encoder-Decoder Long short-term Memory is considered the optimal choice for tool wear prediction due to highest accuracy with coefficient of determinations (R2) ranging from 0.980 to 0.995 with the lowest mean squared error between 22 and 50 μm under varying operating conditions during normal and vibration-damped milling. Thus, it facilitates the significant enhancement in the tool life under different operating conditions, and robust data-driven model for the effective implementation of predictive maintenance approach.

Research on tool wear condition monitoring based on deep transfer learning and residual network

To address the issues of sample scarcity and insufficient recognition accuracy in existing deep learning models for tool wear monitoring, this study developed a milling tool wear monitoring model that combines transfer learning (TL) and deep residual networks (ResNet). The model uses continuous wavelet transform to convert vibration signals into time-frequency maps, which are then fed into the network model for analysis. ResNet50 was selected as the base feature extraction model, and transfer learning techniques were employed to update the classification layer’s weights, enabling tool wear detection. The model based on ResNet-TL achieved a detection accuracy of 94%, significantly exceeding the threshold for intelligent tool wear recognition. This accomplishment markedly improves the precision and stability of tool wear state monitoring, providing more reliable technical support for tool management in manufacturing processes. Additionally, the method demonstrated superiority in addressing the small sample problem, paving the way for future research in tool wear monitoring. By integrating advanced deep learning techniques with transfer learning, the model not only enhances detection capabilities but also improves adaptability and robustness in practical industrial applications.

Milling tool wear prediction using an integrated wireless multi-sensor tool holder and convolutional neural networks

Milling tool wear prediction using an integrated wireless multi-sensor tool holder and convolutional neural networks

Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model

Milling tool wear, a ubiquitous challenge in industrial automation and manufacturing, leads to diminished equipment utilization, escalating costs, and a decline in product quality. The prediction of tool wear is a complex and challenging task, as it involves numerous variables. This paper introduces a pioneering hybrid approach, the NCA-SMA-GRU model. It involves the hybridization of three major components and is designed to enhance the precision and expedite the process of tool wear prediction. The NCA component is adept at filtering and retaining the most relevant features associated with milling tool wear from the raw signals, and it also improves the model’s interpretability. Subsequently, SMA optimizes the GRU network’s hyperparameters, including the initial learning rate, hidden layer neurons, network training iterations, and the L2 regularization factor, to identify an optimal combination that bolsters predictive performance. The modeling steps and the development of fitness function are explained in detail. The model’s efficacy is rigorously evaluated using data from the 2010 High Speed CNC Machine Tool Health Prediction Contest (PHM 2010), which encompasses wear data from both single and multiple cutters. The performance is compared using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2), and computational time. To assess the ability and characteristics of the proposed approach, other popular hybrid models are constructed for comparative analysis. The results demonstrate that the proposed model addresses the limitations of traditional prediction methods and provides insights into the development of deep learning and optimization algorithms for tool wear prediction.

Study of an ISSA-XGBoost model for milling tool wear prediction under variable working conditions

Study of an ISSA-XGBoost model for milling tool wear prediction under variable working conditions

Cutting Performance of a Longitudinal and Torsional Ultrasonic Vibration Tool in Milling of Inconel 718

Inconel 718 has excellent thermal and chemical properties and is widely used in the manufacture of aerospace parts; however, there are some problems in the machining of Inconel 718, such as a large milling force, serious tool wear, and poor surface quality. In this research, a type of longitudinal–torsional ultrasonic milling (LTUM) tool is designed based on theoretical computations and FEM simulation analysis. To verify the design rationality of the developed LTUM tool, milling experiments are performed. It is verified that the LTUM tool can realize an elliptical vibration path at the tool tip. The resonance frequency of the tool is 21.32 kHz, the longitudinal amplitude is 6.8 µm, and the torsional amplitude is 1.4 µm. In the milling of Inconel 718, the experimental data of LTUM are compared with those of conventional milling (CM). The comparative experiments show that the LTUM tool can effectively lessen the milling force and tool wear in the milling of Inconel 718, improve the surface quality, inhibit the generation of burrs, and improve the chip breaking ability. The application potential of the LTUM tool in high-performance milling of Inconel 718 parts is proven.

In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation

Rapid tool wear conditions during the manufacturing process are crucial for the enhancement of product quality. As an extension of our recent works, in this research, a generic in-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation is proposed. With the engagement of dynamic mode decomposition, the real-time response of the sensing physical quantity during the end milling process can be predicted. Besides, by constructing the graph structure of the time series and calculating the difference between the predicted signal and the real-time signal, the anomaly can be acquired. Meanwhile, the tool wear state during the end milling process can be successfully evaluated. The proposed method is validated in milling tool wear experiments and received positive results (the mean relative error is recorded as 0.0507). The research, therefore, paves a new way to realize the in-situ tool wear condition monitoring.

A milling tool wear predicting method with processing generalization capability

Tool Wear Prediction (TWP) is crucial to ensure machining quality. Compared to traditional prediction methods based on full-life or failure data, labelled data are limited or nonexistent when a new tool or similar tools have emerged. This makes it extremely challenging to predict wear using limited tracking data. This work aims to provide an innovative method for accurate long-term wear prediction of milling processes using limited monitoring data of individual tools. First, the Variational Modal Decomposition (VMD) and Pearson Correlation Threshold (PCT) are combined to adaptively filter the corresponding machining signals. Second, fitness analysis is performed by selecting features strongly related to tool degradation using feature monotonicity metrics, and sensitive information is fused based on Kernel Principal Components Analysis (KPCA). Third, a Stacked Bi-directional Long Short-Term Memory with attention mechanism (AT-SBiLSTM) model is constructed to ascertain the connection between sensitive feature and the future wear state of the tool by multi-step advance rolling prediction. The results of the various milling experiment show that the proposed approach can provide accurate forecasts of tool wear using limited monitoring data.

DP2Net: A discontinuous physical property-constrained single-source domain generalization network for tool wear state recognition

Cross-conditions tool wear monitoring has a wide application prospect in manufacturing. However, the data distribution discrepancies caused by the inconsistency of process elements restrict the generalization of models under cross-conditions or even similar conditions. The existing methods based on diversity enhancement make it difficult to effectively establish the correlation between the source domain and target domains, which limits the improvement of model generalization. Therefore, this paper details the cause of data distribution discrepancies and proposes a discontinuous physical property-constrained single-source domain generalization network for milling tool wear monitoring. Firstly, a spatial attention mechanism is introduced to weight key signal segments adaptively. Secondly, the generation module is constrained by a standard sample and is used to generate diverse samples with physical properties. Thirdly, extensive recognition experiments on an open dataset and machining experiments with three distribution discrepancy levels were conducted to verify the effectiveness of the proposed method. Finally, feature visualizations provide consistency and interpretability.

Milling Tool Wear Monitoring via the Multichannel Cutting Force Coefficients

Tool wear monitoring (TWM) is of great importance for improving the machining quality and the efficiency of the milling process. Extracting a discriminative tool wear feature is the key to TWM. Cutting force coefficients, which reflect the tool–chip and tool–material contact form, are good indicators of tool wear condition. However, in the existing studies, only the tangential and radial cutting force coefficients are adopted to monitor tool wear. The axial coefficients extracted from the axial cutting force are neglected. Preliminary experiments have shown that, although the axial cutting force has a small amplitude, the axial cutting force coefficients are very discriminative regarding the tool wear condition. Fusing the axial coefficients and the traditional tangential and radial coefficients can improve the monitoring accuracy. Based on such a consideration, this study proposes a milling tool wear monitoring method in which the multichannel cutting force coefficients, viz., the tangential, radial, and axial cutting force coefficients, are fused to indicate the tool wear. A long short-term memory (LSTM) network is adopted to sequentially estimate the progressive tool wear value from the multichannel cutting force coefficients. The effectiveness of the proposed monitoring method is examined using the PHM 2010 data. The results show that the proposed method outperforms the traditional method. With the fusion of the multichannel coefficients, the monitoring accuracy improves by 2.74–6.35%.

Investigation of surface roughness and tool wear in milling of AISI 4340 steel

Investigation of surface roughness and tool wear in milling of AISI 4340 steel has been conducted with a 4-flutes endmill. The relationships between cutting force, surface roughness and cutting parameter were investigated To determine the tool life of the tool. It is required to research the wear of the tool and the roughness of the machining results on the material tested. In this research, the test material used is AISI 4340 steel with a cooling method in the cutting process. The cutting model is slot mill cutting. To determine the wear of the cutting, a variation of the cutting thickness was performed where the selected cutting thickness was 0.5mm, 1mm, and 1.75mm. The tests that have been done are surface roughness testing, tool wear, and tool photos after cutting. The result obtained from the study is the highest level of roughness on the material cutting poses with a cutting thickness of 1.75 mm. In addition, the most optimal wear results on cutting with a cutting depth of 1 mm.

Machine learning models for prediction and classification of tool wear in sustainable milling of additively manufactured 316 stainless steel

Machine learning (ML) is a subfield of Artificial Intelligence (AI) that uses data, learns the hidden pattern from the data, and makes predictions for future instances with greater accuracy and prediction capabilities, not hallucination (overfitting) with the current data. The application of ML is quite popular in the machining sector because the ML models can be used to predict tool wear, surface roughness and other important machining aspects. With this aim, the present work evaluates the tool wear and class separation by predicting the variation of flank wear (Vb) as a test dataset. A predictive model is proposed for utilizing minimum quantity lubrication (MQL), cryogenic, and MoS2+MQL conditions. Predictions are made using several machine learning (ML) methods, including linear regression, support vector machine, random forest, and multilayer perceptron. The study's findings show that Multi-layer perceptron (MLP) is superior to other methods in classification, with a prediction accuracy of over 95% on average across both training and testing datasets. Even with limited information, the proposed method can help classify situations and recommend the best course of action for machining.

Experimental research on mechanical, material, and metallurgical properties of Inconel 600: Application in elevated temperature environment

Experimental research on mechanical, material, and metallurgical properties of Inconel 600: Application in elevated temperature environment

An online monitoring method of milling cutter wear condition driven by digital twin

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process.

Research on multi-signal milling tool wear prediction method based on GAF-ResNext

Research on multi-signal milling tool wear prediction method based on GAF-ResNext

Intelligent milling tool wear estimation based on machine learning algorithms

Intelligent milling tool wear estimation based on machine learning algorithms

Experimental investigation on tool wear and hole quality in helical milling of CFRPs

Experimental investigation on tool wear and hole quality in helical milling of CFRPs

Multiple input CNN architecture for tool state recognition in the milling process based on time series signals

The study presents a tailored application of a multiple-input convolutional neural network (CNN) for tool state recognition in the milling process. Our approach uniquely applies an 11-input CNN to classify tool wear in chipboard milling, utilizing scalogram images derived from time-series signals. The primary objective was to categorize tool wear into three classes: green, yellow, and red, signifying the progression of wear. The study involved 75 samples (25 samples per class), each comprising 11 signals transformed into scalograms via continuous wavelet transform. The dataset of 825 scalogram images enabled the development of a CNN-based diagnostic model, achieving a notable accuracy of 96.00%, which is an improvement over a previous methods (93.33%).