Tool Condition Monitoring Methods Research Articles

Denoising diffusion probabilistic model enhanced tool condition monitoring method under imbalanced conditions

Abstract In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this issue, a novel TCM method combined with multiscale permutation entropy (MPE), denoising diffusion probabilistic model (DDPM) and a residual network (ResNet) is proposed under conditions of sample imbalance. First, the one-dimensional sensing signal data is converted to a grayscale recurrence plot (RP) by minimizing the MPE of the signals in each channel. Second, combine and splice these grayscale RPs from different channels in each sample into color RPs. After that, the generated RP images using DDPM are added to the imbalanced dataset to augment the data to achieve a balanced state of the dataset. Finally, the balanced mixed data set of real and fake samples is input into a ResNet for recognition and monitoring tool conditions. TCM experiments are conducted to verify the performance of the proposed method with imbalanced dataset, and the results of experimental investigation demonstrate that the accuracy of the proposed method improved by 2%–18.8% compared to that of the other four sample augmentation methods using ResNet18 when the imbalance rate is 1:200.

Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

Anomalous Change Detection in Drilling Process Using Variational Autoencoder with Temperature Near Drill Edge

The different flexibility and diversity requirements for respective manufacturing units have made modern cutting tool management much more crucial and complicated, as a greater variety of tools and more frequent tool changes are required to enhance production efficiency and avoid unplanned manufacturing downtime. Developing in-process anomalous change detection methods has been identified as an essential challenge. Machine learning techniques have been widely applied in tool condition monitoring and anomalous change detection. As anomaly data is rare in manufacturing processes, supervised machine learning approaches (such as regression and classification) are not applied to the anomalous change detection problem. Rather, self-supervised machine learning (a representative type of unsupervised machine learning) is applied. This study describes a variational autoencoder (VAE) neural network and proposes a VAE-based method for tool condition monitoring and change detection in a drilling process using the temperature near a drill edge. The proposed VAE evaluates the drill tool condition based on the reconstruction error between the input temperature and its estimate per a drill unit process through the trained network. Computational simulations demonstrate that the proposed VAE network model can avoid overfitting to the anomaly data and that its expressive power is greater than that of the conventional autoencoder model.

Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoring

Transfer learning (TL) has been successfully implemented in tool condition monitoring (TCM) to address the lack of labeled data in real industrial scenarios. In current TL models, the domain offset in the joint distribution of input feature and output label still exists after the feature distribution of the two domains is aligned, resulting in performance degradation. A multiple feature spatial distribution alignment (MSDA) method is proposed, Including Correlation alignment for deep domain adaptation (Deep CORAL) and Joint maximum mean difference (JMMD). Deep CORAL is employed to learn nonlinear transformations, align source and target domains at the feature level through the second-order statistical correlations. JMMD is applied to improve domain alignment by aligning the joint distribution of input features and output labels. ResNet18 combining with bidirectional short-term memory network and attention mechanism is developed to extract the invariant features. TCM experiments with four transfer tasks were conducted and demonstrated the effectiveness of the proposed method.

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

Due to issues such as limited variability in monitoring data across different tool wear conditions and interference during the machining process, data-driven monitoring models are susceptible to misclassification. Therefore, this paper proposes a pioneering approach that takes into account the tool wear law and the characteristic distribution of tool wear monitoring data. Specifically, the paper proposes an automatic feature extraction and tool condition monitoring method based on Siamese Long Short-term Memory Networks (SLSTMs) to transform the original data distribution, enhance the differentiation of different tool wear condition monitoring data, and achieve accurate prediction of tool wear condition. Additionally, a hybrid data and mechanism-driven tool wear monitoring method is proposed that takes advantage of the fact that tool wear is continuous and irreversible to enable reliable adjustment of the monitoring results of the data-driven model without human intervention. The study conducted milling experiments on a three-axis vertical machining center, using TA2 titanium alloy as the workpiece material. Vibration signals from the spindle in three directions were collected as input to the network, with tool wear state labeled as “0″ or ”1″ as the output. Classical machine learning algorithms such as Decision Trees, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), as well as classical deep learning algorithms such as Convolutional Neural Networks (CNN), Sparse Stacked Autoencoders (SSAE) and Long Short-Term Memory Neural Networks (LSTM), were used to construct and compare feature extraction and state monitoring models. The proposed model based on Siamese Long Short-Term Memory Neural Networks (SLSTMs) achieved testing average accuracy of 98.2% (σ2 = 0.44), outperforming typical deep learning algorithms such as CNN, LSTM and SSAE as well as traditional machine learning algorithms such as Decision Trees, KNN and SVM in terms of accuracy and robustness. Moreover, the proposed data and mechanism hybrid-driven tool wear monitoring method can effectively improve the monitoring accuracy and robustness of data-driven models such as CNN and SSAE. The monitoring accuracy of CNN and SSAE increased from 95.9% to 97.6% and 91.0% to 94.3%, respectively.

The accuracy losing phenomenon in abrasive tool condition monitoring and a noval WMMC-JDA based data-driven method considered tool stochastic surface morphology

As a critically issue in the maintaining of desired part quality and manufacturing productivity, tool condition monitoring (TCM) problem is treated by multi data-driven machine learning methods with training and testing datasets generated by stochastic segmentation. For existing methods, it is assumed that samples collected from same type tools follow identical probability distribution. Different from regular tools, abrasive tools dissatisfy this assumption because of their stochastic surface morphologies. Therefore, traditional data-driven methods loss their accuracies in abrasive tool changing cases. In data driven TCM models, the macroscopic effect of abrasive tool micro-stochasticity is discovered. For the abrasive tool frequently changing cases, an abrasive tool condition monitoring (ATCM) method based on unsupervised domain adaptation is presented to deal with changes caused by stochastic surface morphology. In feature-based transfer learning, source and target domain datasets are projected into a transferred subspace to enhance the distribution alignment similarity of two domains. A weighted maximum margin criterion (WMMC) is adopted in the domain adaptation process to make the transformed samples in same class closing but segregated from those in different classes. With unsupervised domain adaptation mechanism and multilayer perceptron (MLP), the unsupervised domain adaptation is proposed to deal with traditional model invalidation after abrasive tool changes. The proposed method is verified by the robotic belt grinding experiment results. Compared with the existing data-driven method, the weighted maximum margin criterion joint distribution adaptation (WMMC-JDA) based ATCM method still maintains the predicting effectiveness after abrasive tool changes.

Tool Condition Monitoring with Convolutional Neural Network for Milling Tools and Turning Inserts

Tool wear is one of the cost drivers in the manufacturing industry because it directly affects the quality of the manufactured workpiece and production efficiency. Identifying the right time to replace the cutting tool is a challenge. If the tool is replaced too soon, the production time can be disrupted, causing unscheduled downtime. Conversely, if it is replaced too late, there will be an additional cost to replace raw materials damaged by broken tools. Therefore, researchers continue to develop tool condition monitoring (TCM) methods to analyze tool wear. A recent popular method is machine vision with convolutional neural networks (CNN). The present research aims to develop classification models that can categorize the image data of milling and turning inserts into GO (suitable for use) and NO GO (not suitable for use). Two approaches are selected for the modeling process, custom learning and transfer learning, with image data input from smartphones and microscope cameras. The experimental results show that the best model is the transfer learning approach using Inception-V3 architecture with a smartphone image. The model reaches 92.2% accuracy, hence demonstrating a relatively good performance in determining whether the tool is suitable for use or not.

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

The current state-of-the-art review on tool condition monitoring for turning of titanium-based superalloys is presented in this paper. Titanium (Ti) superalloys are widely utilised in aerospace industry, automobile industry, petrochemical applications. Ti superalloys are also used in fabrication of biomedical components due to their outstanding combination of mechanical properties and strong corrosion resistance at extreme temperatures. But these superalloys are difficult-to-cut because to their low heat conductivity, low elastic modulus, high strength, and strong chemical resistance. Literature review highlights the drastic reduction in tool life of titanium superalloys at highspeed and feed rates throughout the machining process. The review paper focuses on (i) various reasons to deploy tool condition monitoring; and (ii) study of tool condition monitoring methods based on machine learning techniques to identify the ideal parameters for the prevention of catastrophic tool failure.

A milling tool wear monitoring method with sensing generalization capability

Tool wear monitoring is considered a core technology for next-generation computer numerical control machine tools. However, differences in the characteristics of acquired signals prevent the monitoring model from generalizing between sensing channels, which greatly limits the application in actual industrial environment. To overcome the shortcomings of existing methods, a tool condition monitoring method with sensing generalization capability is proposed. Firstly, the adaptive superposition function of multi-feature is established to automatically adjust the weight of each feature so that the domain features can be adaptively reinforced or penalized. After that, the reinforced time series data are fed into the proposed stacked bidirectional GRU model. The flank wear values are output through a fully connected layer to monitor the tool condition in real-time. Finally, the generalization capability of the proposed method between different types of sensing data is verified through a comparative study of the performance of local features, reinforced features and ARIMA model. The proposed feature reinforcement algorithm reduces the variation in recognition accuracy of different sensing signals by 76.28%, which ensures sensing generalization capability. The adaptive reinforcement and penalization of time series data resulted in a 59.41% improvement in the performance of local features. The average accuracy of the proposed model is 65.61% better than that of the ARIMA model. Thanks to the feature reinforcement algorithm and extracted fractal features, the proposed method is not only applicable to the machining of block-shaped workpieces but also suitable for the milling of thin-walled parts. This study provides a theoretical reference for the standardized application of tool wear monitoring in intelligent manufacturing systems.

Smart Tool Wear Monitoring of CFRP/CFRP Stack Drilling Using Autoencoders and Memory-Based Neural Networks

The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance.

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring.

Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling

Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling

Deep Spatial-Temporal Feature Extraction and Lightweight Feature Fusion for Tool Condition Monitoring

Tool condition monitoring (TCM) is vital to maintain the quality of workpieces during machining. Recently, data-driven methods based on multisensory data have been applied to TCM. The quality of extracted features is a key to realizing a successful data-driven TCM. However, the extracted features in the previous study are focused on the multicollinearity of multisensory data, which is incapable of identifying the informative and discriminative information in the long time period aspect. This article proposed a novel method for TCM using deep spatial-temporal feature extraction and lightweight feature fusion techniques. A key to the proposed method is the extraction of multicollinearity as spatial features (SPs), and the capture of long-range dependencies and nonlinear dynamics as temporal features (TFs), to fully characterize tool wear change using multisensory data. Then, a lightweight feature fusion method is used to fuse SPs, TFs, and statistical features for further removing redundant information employing the kernel-principal component analysis. Finally, support vector machines is used to predict the tool conditions using the fusion feature. Experiments on a milling machine and a gear hobbing machine are carried out to verify the effectiveness and generalization of the proposed method respectively.

Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network

Current milling tool condition monitoring (TCM) methods mainly rely on commercially available sensors with cutting force and vibration being the most widely used. However, they suffer from inconvenient installation or invasive machining when the installation position is close to the cutting area. Aiming at the problem of TCM, this paper proposes a TCM system based on a self-developed smart toolholder which is capable of sensing triaxial cutting force, torque and triaxial vibration simultaneously for the first time. The results of modal test and circuit system test demonstrate that the smart toolholder has a dynamic natural frequency of up to 1 kHz. The milling test results show that vibration sensed by the smart toolholder are much larger than the accelerometers installed on the spindle, workpiece and workbench in terms of maximum value, root mean square and kurtosis characteristics. Subsequently, a milling cutter with two physical vapor deposition (PVD) coated inserts was subjected to a full life cycle milling test on a 304 stainless steel workpiece. To build the TCM model, an improved residual network is proposed, which adopts the structure of the first layer of multi-scale large convolution kernels and the subsequent residual network, which can take the raw cutting force and vibration signals as input and automatically extract features. The results show that the established TCM model can identify the degree of tool wear up to 97.5% with lower fluctuations, which provides a practical solution for factory automated milling tool change.

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet.

Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 μm and 100 μm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.

Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

A novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism

Tool wear does great damage to product quality and machining efficiency. In this paper, a new tool condition monitoring (TCM) method based on a multi-sensor data fusion imaging and attention mechanism is proposed to indirectly measure and monitor tool wear. First, the multi-sensor signals collected during the operation of the machine tool are encoded and fused. A novel triangular matrix of angle summation method is proposed to fuse multi-sensor time-series signals at the data layer and to image them as two-dimensional images. The method used here effectively retains the detailed characteristics of the data, and also effectively retains the internal time relationship of the signal. In addition, a deep residual network with a convolution block attention module is used to extract the deep features from the encoded image and identify the wear stage of the tool. The proposed deep-learning network is then applied to TCM. It selects information in the channel and spatial domains based on its attention mechanism, and increases network depth by combining residual blocks to realize deep feature extraction. Compared with traditional machine learning methods, this end-to-end deep learning model does not rely on feature engineering, which requires a lot of expert knowledge and artificial experience. In our experimental study, a public data set was used to train the model, and the results showed that the accuracy of the model proposed in this paper was as high as 94%. To further verify its feasibility and effectiveness, the proposed model was also compared with other methods.

A tool wear condition monitoring approach for end milling based on numerical simulation

As an important research area of modern manufacturing, tool condition monitoring (TCM) has attracted much attention, especially artificial intelligence (AI)- based TCM method. However, the training samples obtained in practical experiments have the problem of sample missing and sample insufficiency. A numerical simulation- based TCM method is proposed to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology, in which the KL divergence and cosine similarity are used as the evaluation indexes. Second, samples under various tool wear categories are obtained by the optimized numerical model above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling TCM experiments. The results indicate the classification accuracies of four classifiers (SVM, RF, DT, and GRNN) can be improved significantly by the proposed TCM method.