Wear Monitoring Research Articles

A technique for flank wear monitoring in milling machines based on the novel tool wear indicator

ABSTRACT This research investigates an innovative method for monitoring flank wear by analyzing these signal variations. By dissecting the signal bands into multiple frequency bands using Wavelet Packet Transform, a precise examination of the signal contents is enabled. Using the signal power spectrum to Shannon entropy (SPSSE) node power spectrum analysis metric, the specific frequency band that exhibits a stable rising trend in the proposed metric correlated with flank wear is identified. This frequency band serves as a basis for constructing a tool wear indicator that utilizes Mean Square Error (MSE) comparisons of a healthy baseline signal sample with the sample under test. The calculated indicator closely correlates with the actual measured levels of flank wear, allowing for effective evaluation of the tool condition and differentiation between the healthy tool and the tool exhibiting any levels of flank wear. The performance of the proposed method is evaluated using the publicly available benchmark NASA Ames Milling tool dataset. This innovative approach holds promise for real-time assessment of machining processes, facilitating timely intervention based on the tool wear estimation.

Integrating Friction Noise for In-Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

Abstract Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in-situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction.

Tire wear monitoring using feature fusion and CatBoost classifier

Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.

Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning

Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning

Research on milling cutter wear monitoring based on self-learning feature boundary model

Research on milling cutter wear monitoring based on self-learning feature boundary model

Recent Advances in Intraoral Scanners.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 2.8 % in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Research on Tool Wear Monitoring Based on Enhanced Convolutional Neural Networks

Abstract Identifying the wear status of cutting tools during the machining process is essential because failure to promptly replace severely worn tools can significantly impact the quality of workpiece machining. Presently, machine learning methods are predominantly utilized for monitoring cutting tool wear status. However, these methods rely on manual feature extraction and exhibit low accuracy. This study introduces a novel RRP-Net model, built upon the RepVgg and ResNet frameworks, integrating a parameter-free self-attention mechanism called SimAM to expedite the model’s solving speed without increasing parameters. Within the foundational module of the model, a structural reparameterization approach is employed to transform the multi-branch structure during training into a single-branch structure during validation. This method not only enhances model accuracy but also accelerates the model validation process. The publicly available cutting data from PHM2010 is employed for model training and validation. The findings demonstrate that RRP-Net surpasses classical convolutional neural network models in identifying cutting tool wear status within the PHM2010 dataset, achieving an average accuracy of 98.65% and enhancing recognition accuracy on relevant datasets by 2.41%. To verify the model’s practical applicability, specificity and recall during the Break stage are computed at 99.73% and 98.18%, respectively, affirming the model’s exceptional robustness and stability. The heightened accuracy and efficiency of RRP-Net further broaden its applicability within the industrial domain.

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.

A novel IoT sensor and evolution model for grinding mill liner wear monitoring

Tracking mill liner wear is essential for improved plant reliability and grinding performance. This study developed a novel IoT wear sensor and a Discrete Element Modelling coupled methodology to predict and continuously evolve shell liner’s wear pattern. The IoT sensor was purposely developed to track and report the live thickness of a shell liner. Global wear pattern was then obtained by coupling the qualitative wear intensity obtained in DEM and sensor results, from which a topological evolution model was established to generate the shell liner’s progressive wear profiles. Predictions of the wear evolution model were compared with 3D laser scan measurements collected during operation. Results indicated that the wear evolution model showed less than 8% error with laser scan measurements in quantitative wear rate comparison.

Automated monitoring of tooth wear progression using AI on intraoral scans

ObjectivesThis study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. MethodsEight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. ResultsThe automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. ConclusionsThe proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. Clinical significanceGeneral practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Microservice-based digital twin system towards smart manufacturing

Digital Twin (DT) is a promising technology that offers versatile services to enhance manufacturing intelligence. However, the agility, reliability and analysis capabilities of existing DT services are severely challenged when applied and deployed at large-scale production lines. To address the aforementioned issues, a microservice-based DT system with redundant architecture is proposed. First, a scalable microservice-based DT system compatible with standard and tailored plug-and-play DT services is constructed for DT protocol adaptation, stream processing, information and model management. Concurrently, a generic information model is proposed to represent the entire production lifecycle from design, operation, and maintenance in a structured manner. Second, an industrial multi-task DT model is introduced, leveraging the aforementioned architecture, to effectively achieve parallel monitoring of surface roughness and tool wear. Finally, industrial manufacturing cases are introduced to verify the feasibility and effectiveness of the proposed system. The results show that heterogeneous DT data are transferred and managed reliably, with a mean absolute percentage error of 1.28% for surface roughness prediction, and 85.71% accuracy in tool wear diagnosis.

A variable modal decomposition and BiGRU-based tool wear monitoring method based on non-dominated sorting genetic algorithm II

ABSTRACT In the metal cutting process, it is important to realize the effective monitoring of tool wear to ensure the quality of parts machining. A variable modal decomposition (VMD) and bi-directional gated recurrent neural network (BiGRU) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for tool wear monitoring (TWM) problem. The method takes the envelope entropy and kurtosis of the decomposed signal as the objective function, and iteratively optimizes the number of decomposition layers k and the penalty factor α of the VMD by NSGA-II to obtain the optimal solution set. Meanwhile, an evaluation method of entropy weight- technique for order preference by similarity to ideal solution (EW-TOPSIS) is proposed to obtain the optimal parameter combinations in VMD. The VMD decomposed signals are screened by calculating the correlation coefficients, the time domain and frequency domain features of the screened signals are extracted, and the screened signals and features are mirrored. To realize the in-depth mining of the feature information of time series data, a BiGRU deep learning monitoring model is established. By comparing three classification methods, it has been proven that this method has achieved better results in monitoring tool wear state.

PSPMoni: A Robust Wear Monitoring Method of Pantograph Slide Plate

The pantograph slide plate (PSP) plays a crucial role in the daily maintenance of trains by transmitting electrical energy from the contact wire (CW) to the train. Automatic monitoring of PSP wear condition is essential to notice abnormal wear promptly. In field applications, the target PSP edge may be obscured by the rear side PSP due to the operating attitude of the pantograph, leading to false detections and interfering with the field maintenance work. Meanwhile, the scenario of the PSP occlusion is characterized by randomness, which brings a considerable challenge to the reconstruction of the occluded edges. A robust PSP wear monitoring method is proposed to improve the performance of wear detection in field applications, considering the PSP occlusion scenarios. Firstly, the wear area of the PSP is localized using the YOLOv5s model. Subsequently, a fusion of the morphology method and the Canny operator is employed to extract the edge of the PSP. Different occlusion types are identified through detailed observation, and corresponding edge reconstruction methods are proposed for occluded edges. Finally, a robust wear condition evaluation method and five evaluation criteria for assessing wear condition are introduced. Experimental results demonstrate that the system achieves accurate PSP wear detection with an accuracy of 0.6 mm, and the reconstructed edge at occlusion locations closely matches the target edge compared to other automatic systems.

Ball-end tool wear monitoring and multi-step forecasting with multi-modal information under variable cutting conditions

Tool condition recognition is considered an indispensable solution with significant advantages in improving production cost and quality in intelligent manufacturing. However, the emergence of complex problems such as variable cutting conditions and feature engineering further causes the technology to have a low generalization performance, which severely limits its application in engineering practice. To overcome the above problems as much as possible, a technological framework for monitoring and multi-step forecasting of ball-end tool wear based on multi-modal information under different cutting conditions is proposed. Firstly, a two-stage hybrid deep feature extraction method is proposed by monitoring the cutting vibration and power signals of the spindle. Secondly, a tool wear monitoring model based on SBiLSTM_Multihead Self-attention is proposed to adapt to different cutting conditions. On this basis, a multi-step forecasting model with CNN_SBiLSTM_Multihead Self-attention is proposed to realize the future forecasting of tool wear trend. Finally, the generalization performance of the proposed methods is investigated based on three-axis and five-axis milling experiments. The results show that the correlation coefficient of the enhanced features can reach a maximum value of 87 %. The average accuracy of the proposed monitoring model is improved by an average of 23.84 % over the conventional method. In particular, the multi-step forecasting method is more suitable for long-term forecasting under different cutting conditions. Its average accuracy reaches an average of about 0.013 in the 24-step forecasting. Therefore, the study can provide theoretical references for the application of tool condition recognition in complex machining environments in engineering practice to some extent.

Early monitoring of inlay wear after total knee arthroplasty on plain radiographs using model-based wear measurement

Wear of the ultra-high molecular-weight polyethylene (UHMWPE) component in total knee arthroplasty contributes to implant failure. It is often detected late, when patients experience pain or instability. Early monitoring could enable timely intervention, preventing implant failure and joint degeneration. This study investigates the accuracy and precision (repeatability) of model-based wear measurement (MBWM), a novel technique that can estimate inlay thickness and wear radiographically. Six inlays were milled from non-crosslinked UHMWPE and imaged via X-ray in anteroposterior view at flexion angles 0°, 30°, and 60° on a phantom knee model. MBWM measurements were compared with reference values from a coordinate measurement machine. Three inlays were subjected to accelerated wear generation and similarly evaluated. MBWM estimated inlay thickness with medial and lateral accuracies of 0.13 ± 0.09 and 0.14 ± 0.09 mm, respectively, and linear wear with an accuracy of 0.07 ± 0.06 mm. Thickness measurements revealed significant lateral differences at 0° and 30° (0.22 ± 0.08 mm vs. 0.06 ± 0.06 mm, respectively; t-test, p = 0.0002). Precision was high, with average medial and lateral differences of − 0.01 ± 0.04 mm between double experiments. MBWM using plain radiographs presents a practical and promising approach for the clinical detection of implant wear.

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.