Tool Wear In Milling Research Articles

SE-U-Lite: Milling Tool Wear Segmentation Based on Lightweight U-Net Model With Squeeze-and-Excitation Module

SE-U-Lite: Milling Tool Wear Segmentation Based on Lightweight U-Net Model With Squeeze-and-Excitation Module

Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces

The spindle current of a machine tool contains a wealth of machining information and by analyzing its signal characteristics wear monitoring of milling tools can be achieved. Most current research in tool wear monitoring has been achieved by intelligent classification methods, but such methods are unable to identify wear states when cutting parameters change. To address this challenge, the paper proposes a new tool wear monitoring framework that identifies the wear state of milling cutters under different cutting parameters. The first step is to establish the mapping relationship between the spindle current signal and the milling force signal through neural networks under a variety of cutting parameters. Based on the mapping property between two signals, a TCN-LSTM based neural network prediction model is proposed to achieve accurate prediction of milling forces by spindle current signal. Various cutting information is then calculated based on the predicted milling forces and PLC communication, and a method is proposed to identify the machining status of the tool: the milling force coefficient monitoring method. The method reflect the wear status of the milling cutter by monitoring the range of threshold values, making the identification of the tool wear state without interference from variable spindle speeds, feeds and depths of cut. The proposed method provides a new solution for tool wear monitoring, breaking the inherent thinking of intelligent tool wear classification, and the experimental results prove the effectiveness and generality of the proposed method.

Effects of Machining Parameters and Tool Reconditioning on Cutting Force, Tool Wear, Surface Roughness and Burr Formation in Nickel-Based Alloy Milling.

Nickel-based superalloys are among the most difficult materials to machine because they have high thermal strength, they are prone to hardening, carbides severely abrade the tool, and they have very poor thermal conductivity. Slot milling is a specific issue as it is characterized by rapid tool wear and frequent tool breakages. This is why reconditioned tools are frequently employed in industrial environments, as they can considerably decrease the expenses associated with tools. The chosen machining strategy also plays a crucial role in the tool's lifespan and the quality of the machined surface, making it essential to select the appropriate strategy. Hence, the authors have opted for two conventional trochoidal strategies, namely the circular and swinging toolpath, along with a contemporary toolpath known as the Autodesk Inventor HSM Adaptive strategy. The authors investigated the effects of technological parameters and toolpaths on cutting forces, tool wear, surface roughness and burr formation on machined edges. The results show that lower cutting parameters and adaptive strategies lead to the smallest tool loads, tool wear, the best quality of surface roughness and burr formation on machined edges.

Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling

It is an effective means to improve the machining quality of product by monitoring the tool wear conditions in micro milling. However, due to the small size and high speed of the micro milling cutter, it is difficult to capture the clear image of tool wear by CCD camera. Therefore, a double dictionary sparse coding super-resolution (SR) method is proposed to improve the resolution of micro milling tool wear images in this work. Based on the traditional single dictionary, this work proposes the residual dictionary to reconstruct the residual image of the tool, achieving the double dictionary coefficient encoding reconstruction. In order to extract local features of low resolution (LR) tool wear images, the Gobor filter banks are designed, and then the PCA method is used to reduce dimension of extracted features. The dictionary learning based on a single high resolution (HR) image is realized, which reduces the dependence of super-resolution algorithm on large datasets. The effectiveness of Gabor filter and double dictionary for restoration tool wear image details is verified by experiments. Compared with proposed method, traditional interpolation method, sparse coding super resolution model [32] and super resolution convolutional neural network model [26], the PSNR mean value of those methods are 43.62, 42.19, 43.20, 43.21, respectively, which shows that the proposed method has excellent performance in super-resolution reconstruction of micro milling tool images for tool wear monitoring.

Identification of tool wear and surface morphology measurements in sustainable milling of Al 6082 hybrid metal matrix composite

Aluminium hybrid metal matrix composites have significantly increased in many advanced applications owing to their unique properties. However, these hybrid composites are difficult to machine because of hard reinforcements in the matrix. After identifying the optimum cutting conditions, it's crucial to identify the tool wear mechanisms to keep surface roughness within the desired range, especially with hybrid composites. Hence, the present work mainly focuses on identifying the wear mechanism of PVD-coated TiAlN cutting tool inserts during the end milling of Al 6082 hybrid metal matrix composites. MQL, CO2, and a combination of MQL + CO2 cooling conditions were employed over the cutting zone to decrease tool wear. In the context of 300 mm of cutting length, employing CO2, MQL, and MQL + CO2 conditions led to a significant reduction in tool wear by 34.5 %, 50.1 %, and 74.7 %, respectively, compared to dry cutting. The results showed that the MQL + CO2 condition produced better results than the other cooling conditions, improving tool life and surface quality.

Influence of feed and cutting-edge radius on tool wear, surface roughness, and electric current in milling of VP20TS steel

Influence of feed and cutting-edge radius on tool wear, surface roughness, and electric current in milling of VP20TS steel

On-line milling tool wear monitoring under practical machining conditions

Accurate diagnosis of milling tool wear monitoring during high-speed cutting processes is essential for ensuring machining surface precision, enhancing tool efficiency, and extending the service life of machine tools. However, challenges in data screening during each stage of tool movement and signal migration caused by changes in machining parameters have not been sufficiently addressed. To tackle these issues, this paper presents a novel MB-DAAN model, which considers signals from each stage of tool movement and diagnoses tool wear under dynamic machining parameters. Firstly, the multi-branch classification module is introduced, incorporating two independent classifiers to form a multi-branch classification module. This module adaptively extracts features of tool wear states during the cutting stage. Subsequently, the dynamic adversarial factor adjusts the global and local losses between features, aligning conditional and edge distributions across different machining parameters. The models were evaluated in the milling tool wear data set. MB-DAAN achieving a diagnostic accuracy of 97.3%-a significant improvement compared to CNN, DAN, and DAAN models.

Investigation on cutting forces and tool wear in high-speed milling of Ti-6Al-4V assisted by longitudinal torsional ultrasonic vibrations

Investigation on cutting forces and tool wear in high-speed milling of Ti-6Al-4V assisted by longitudinal torsional ultrasonic vibrations

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.

Investigation of Laser-Assisted Micro-Milling Process of Inconel 718

While Inconel 718 is a widely used engineering material in industrial fields such as the aerospace and automotive fields, the machined surface integrity has a significant effect on the performance of its components and parts. In this work, the laser-assisted micro-milling process of Inconel 718 is investigated using a combination of experiments and finite element simulations. Firstly, an experimental platform of laser-assisted milling is built, and a three-dimensional thermal–mechanical coupled finite element model of laser-assisted milling of Inconel 718 is then established. Secondly, laser-assisted milling experiments and finite element simulations are conducted to investigate the impact of laser assistance on cutting force, chip morphology, tool wear and surface topography of Inconel 718 under a milling process. The results indicate that laser-assisted milling results in a moderate reduction in cutting forces while enhancing surface integrity and chip continuity, as compared with ordinary milling. Thirdly, orthogonal experiments of laser-assisted milling of Inconel 718 are conducted to discover the optimal processing parameters, including spindle speed, feed per tooth, milling depth and laser parameters. Finally, single-factor experiments are conducted to investigate the effect of laser power on cutting force, chip morphology, tool wear, groove burr and surface roughness in the laser-assisted milling of Inconel 718. And, a minimal surface roughness Sa of 137 nm for Inconel 718 accompanied by minimal tool wear is experimentally obtained via laser-assisted milling. These findings highlight the effectiveness of applying laser assistance in enhancing the machinability of difficult-to-machine materials for achieving desirable machined surface integrity.

A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics

With the trend of lightweight in the field of intelligent electric vehicles and 3C, the demand for high precision machining of aluminum alloy parts is growing. And tool condition monitoring (TCM) is very important for quality control of parts, so intelligent high-accuracy wear prediction of aluminum alloy high precision machining tools has great industrial application value at present and in the future. This paper presents a novel TCM model (Conv-PhyFormer) of Transformer with physics informed. The model has excellent ability to capture short-term and long-term dependencies from nonlinear cutting time series data when there are few training samples. The embedded hard physical constraint and soft physical constraint in the model make the model partially interpretable. Soft physical constraint in the form of one-dimensional causal convolution can help the proposed model better learn the local context. Hard physical constraint in the form of the mathematical equation representing cutting physical knowledge are embedded, thus the model does not need to learn this knowledge from time series data from scratch. A large number of analysis results of aluminum alloy machining experimental data show that the proposed Conv-PhyFormer has significantly superior prediction accuracy and robustness compared with the current three popular deep learning models for TCM. Embedded soft and hard physical constraints can significantly reduce the training epochs of Transformer prediction model.

NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning

NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.

Oxidation mechanism of high-volume fraction SiCp/Al composite under laser irradiation and subsequent machining

Conventional mechanical machining of a composite material comprising an aluminum matrix reinforced with a high volume fraction of SiC particles (hereinafter referred to as an SiCp/Al composite) faces problems such as rapid tool wear, high specific cutting force, and poor surface integrity. Instead, a promising method for solving these problems is laser-induced oxidation-assisted milling (LOAM): under laser irradiation, the local workpiece material reacts with oxygen, thus forming loose and porous oxides that are easily removed. In the present work, the oxidation mechanism of SiCp/Al irradiated by a nanosecond pulsed laser is studied to better understand the laser-induced oxidation behavior and control the characteristics of the oxides, with laser irradiation experiments performed on a 65% SiCp/Al composite with various laser parameters and auxiliary gases (oxygen, nitrogen, and argon). With increasing laser pulse energy density, both the ablated groove depth and the width of the heat-affected zone increase. When oxygen is used as the auxiliary gas, an oxide layer composed of SiO2 and Al2O3 forms, and CO2 is produced and escapes from the material, thereby forming pores in the oxides. However, when nitrogen or argon is used as the auxiliary gas, a recast layer is produced that is relatively difficult to remove. Under laser irradiation, the sputtered material reacts with oxygen to form oxides on both sides of the ablated groove, and as the laser scanning path advances, the produced oxides accumulate to form an oxide layer. LOAM and conventional milling are compared using the same milling parameters, and LOAM is found to be better for reduced milling force and tool wear and improved machined surface quality.

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

Synthetic Minority Oversampling Enhanced FEM for Tool Wear Condition Monitoring

Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool wear condition monitoring methods, heavily relying on large training samples. However, the high cost of tool wear experiment and the uncertainty of tool wear change in the machining process lead to the problems of sample missing and insufficiency in the model training stage, which seriously affects the identification accuracy of many AI models. In this paper, a novel identification method based on finite-element modeling (FEM) and the synthetic minority oversampling technique (SMOTE) is proposed to overcome the problem of sample missing and sample insufficiency. Firstly, a few tool wear monitoring experiments are carried out to obtain experimental samples with low cost. Then, a FEM model based on the Johnson–Cook constitutive model was established and verified according to the experimental samples. Based on the verified FEM model, the simulated missing sample in the experiments can be supplemented to compose a complete training set. Finally, the SMOTE is employed to expand the sample size to construct a perfect training set to train the SVM classification model. End milling tool wear monitoring experiments demonstrate that the proposed FEM-SMOTE method can obtain 98.7% identification accuracy, which is 30% higher than that based on experimental samples. The proposed method provides an effective approach for tool wear condition monitoring with low experimental cost.

A Time-Series Driven Mechanical System State Description Method and Its Application in Condition Monitoring

Nonlinear and time-varying characteristics of mechanical systems may lead to the variation of fault modes which makes it hard to be accurately evaluated. To fulfill this requirement, the authors propose a time-domain signal-driven mechanical system state description method in this research. Based on the theory of stochastic subspace identification, an abnormal evaluation model is established to investigate the performance of mechanical systems. Firstly, by constructing the projection matrix of the established Hankel matrixes from the given time series at different times, the system state matrix can be obtained. Subsequently, based on the eigenvectors of the reference state matrix, the testing samples can be decomposed. Finally, by calculating the difference between the reference and testing signal, the anomaly can be acquired, and the quantitative evaluation of the health of the mechanical system can also be realized. The proposed method is validated in bearing performance degradation, bearing fault diagnosis, and milling tool wear experiments and received positive results. The proposed method, therefore, provides a new possibility for monitoring the service performance of mechanical systems.

Milling tool wear prediction: optimized long short-term memory model based on attention mechanism

To improve the prediction accuracy of milling tool wear, a prediction method based on Attention-LSTM is proposed. In the training phase, first, the data are pre-processed by truncation, downsampling, and the Hampel filtering method, and then features are extracted by the time domain, frequency domain, and time-frequency domain analysis methods. Second, a deep neural network is designed to describe the complex nonlinear function between features and tool wear. Last, aiming at the insufficient prediction accuracy due to the LSTM lacking feature extraction and enhancement, the Attention mechanism is introduced to optimize the model. The results suggest that this prediction method provides an efficient strategy for milling tool wear prediction.

Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods

Accurate prediction of tool wear in multi-conditions is still a thorny problem, and rapid and accurate construction of prediction models for multi-conditions is an essential part of achieving intelligent manufacturing. For multi-conditions, a novel method of rapid selection of milling tool wear prediction model based on linear discriminant analysis and the ensemble method was proposed in this paper. The linear discriminant analysis is used to divide similar working conditions, and determining the optimal prediction model for different working conditions relies on ensemble methods. Compared with the traditional transfer learning algorithm, the proposed method can be used for the rapid construction of new models and prediction of new working conditions, which are validated by real experiments. The results show that the selection of prediction models based on working condition division is superior to other models. Therefore, this method provides a guarantee for rapidly constructing accurate tool wear multi-condition predictive models.

An effective LSSVM-based approach for milling tool wear prediction

An effective LSSVM-based approach for milling tool wear prediction