Tool Wear Monitoring Research Articles

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data

A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data

Optimizing tool life in SiCp/Al composites milling with acoustic emission analysis: A comprehensive monitoring and implementation strategy

The machining of SiCp/Al composites is hampered by tool wear monitoring challenges. SiCp/Al composites are used in industries like aerospace where tool wear can lead to workpiece loss, necessitating real-time monitoring. Acoustic emission (AE) sources during machining are complex due to SiC particles. This paper attempts to isolate AE frequencies for plastic deformation and silicon carbide particle behavior through scratch experiments at different depths. The relationship between signal energy distribution and tool wear during milling is analyzed using the wavelet packet method. The frequency band correlating best to tool wear is identified outside the scratch experiment's frequency range. A monitoring strategy is proposed based on this, and the effect of machining parameters on AE monitoring is analyzed, with refinements to the monitoring model. The proposed strategy's effectiveness has been validated through experimental trials, resulting in an average tool utilization rate of 85 % during milling.

Energy Consumption and Tool Condition in Friction Stir Processing of Aluminum Alloys

AbstractFriction Stir Welding (FSW) and Friction Stir Processing (FSP) are solid-state joining and material processing techniques that have garnered considerable attention for their versatility and industrial applicability. In the present work, FSP was performed on AA 6056 T4, dealing with the issue of monitoring tool wear and assessing its impact on the process. The impact of tool wear on power requirements was analyzed, and it was expanded the understanding of tool behavior and its implications for the overall process performance. Specifically, variations in energy consumption, temperatures, and vibrations are observed with changing tool conditions. Further insights are provided by analyzing the microhardness and the pin volume ratio, which show distinct trends as the tool wears. Two tool maintenance ways are proposed, that are cleaning the tool with a sodium hydroxide solution and increasing the tool’s rotational speed. Both the strategies exhibit the potential to partially restore the tool’s initial characteristics. This study highlights the critical importance of assessing tool condition, energy consumption, and process sustainability, particularly in industrial settings where material processing requires efficiency and quality assurance.

Cutting-edge tool wear monitoring in AISI4140 steel hard turning using least square-support vector machine

ABSTRACT This research addresses the critical challenge of tool wear monitoring in AISI4140 steel hard turning through the innovative application of a Least Square-Support Vector Machine (LS-SVM) prediction model. While acknowledging the growing significance of hard turning over conventional grinding techniques, the background also recognizes the challenges posed by accelerated tool wear and decreased productivity. The main goal is to create a reliable tool wear prediction model that is tailored to harsh turning circumstances. Material selection, cutting force measurement, acceleration measurement, and accurate tool wear measurement are all included in the experimental procedures. Using time and frequency domain analyses, the research methodically examines how cutting force and acceleration affect tool wear. For feature selection, non-linear regression is used to determine which parameters have the greatest influence on wear. A comprehensive training and validation procedure, structural clarification, and a detailed mathematical foundation are used in the development of the LS-SVM-based tool wear prediction model. The presentation of the findings and related discussions explores the impact of acceleration and cutting force on wear. The LS-SVM model’s successful application for precise tool wear prediction in AISI4140 steel hard turning is highlighted in the conclusions, demonstrating the model’s potential to improve tool lifespan and manufacturing efficiency.

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Tool Wear Prediction Based on Residual Connection and Temporal Networks

Immune system inspired smart maintenance framework: tool wear monitoring use case

As the manufacturing industry is moving towards the fourth industrial revolution, there is an increasing need for smart maintenance systems that could provide manufacturers with a competitive advantage by predicting failures. Despite various efforts by researchers, there are still challenges for these systems to work reliably in the industry such as lack of adaptability, resilience, reaction to disturbances, and future-proofing. Bio-inspired frameworks like artificial immune systems provide an alternative approach to satisfying these challenges. But existing immune-based frameworks focus only on adaptive immunity characteristics and ignore innate immunity which is important for quick detection and faster response. There is a need for a holistic view of the immune system in developing an adaptive & resilient maintenance framework. This paper presents a holistic view of the human immune system with a focus on the intelligence & response mechanism of both innate & adaptive immunity. Inspired by this holistic view and considering the emerging computer technologies — Internet of Things, Edge & Cloud Computing, Multi-Agent System, Ontology, Big Data, Digital Twin, Machine Learning, and Augmented Reality — we present a smart maintenance framework. The proposed framework is used for tool condition monitoring to demonstrate its implementation.

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%.

An investigation on using artificial intelligence models to predict crater wear of tungsten carbide tool

In this study, artificial intelligence (AI) tools were utilised to predict and analyse the progression of crater wear in cutting tools made of tungsten carbide during machining of aluminium 7075 alloy with a CNC lathe. The study investigated the impact of corner radius, feed rate, cutting speeds, and cut depth on the wear of the tools. Thirty experiments were conducted, with 24 used for training 11 independent AI models and the remaining 6 used for testing. This study stands out for its novelty as it pioneers the evaluation of 11 distinct AI models for the prediction of tool wear. With a high level of accuracy and a lower average deviation, the most effective model identified in this study was the gradient boosting model. By integrating AI algorithms into manufacturing processes, the monitoring of tool wear becomes more efficient, leading to reduce experiments, minimise testing costs, predict tool life, prevent failures, and boost productivity.

Accurate tool wear and breakage monitoring method for milling process based on vision and laser sensor fusion

Accurate online acquisition of tool wear degradation indicators is an important prerequisite for tool wear monitoring and tool remaining life prediction. The tool degradation process is usually accompanied by the flank face of cutting tool wear and blade breakage, however, the existing degradation indicators only consider the flank face wear value, but not the tool breakage value, resulting in the lack of accuracy of degradation indicators. To this end, an online identification method of tool degradation indicators based on the fusion of image sensors and laser displacement sensors is proposed, which adopts the VGG16-UNet network to identify the wear value in the image and obtains the tool breakage value based on the time series data of the laser displacement sensor. Finally, the tool wear and breakage degradation label containing wear and breakage values is established. Compared to manual measurements, the absolute average error is within 15 µms for cutter face damage values and within 3 µms for cutter face wear values.

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.

Auxiliary input-enhanced siamese neural network: A robust tool wear prediction framework with improved feature extraction and generalization ability

Tool wear monitoring is essential for automated and resilient manufacturing, as it can prevent catastrophic failures caused by severe wear on cutting edges during machining. The conventional tool wear monitoring approaches depend on features extracted from signals, which require sensitive signals and consistent tool wear. In practical scenarios, however, neither the sensitivity of collected signals to the tool wear status nor the consistency of the actual tool wear evolution is hard to meet the requirement of the tool condition monitoring algorithm, which greatly limit the wide spread of its industrial applications. To overcome this challenge, we propose an Auxiliary Input-enhanced Siamese Neural Network (AISNN) framework by incorporating a Siamese structure into the feature extraction part of a convolutional neural network (CNN), and introducing an auxiliary input to its nonlinear regression part. The Siamese structure, instead of extracting features directly from signals, distinguishes the difference between the features extracted from signals of the examined cut and the first cut, and uses this difference as the indicator of tool wear status. Moreover, the auxiliary input provides an additional feature that has heavy dependence on the tool wear, which enables the model learning the general wear evolution of the examined cutting tool. The effectiveness of the proposed AISNN framework is verified in a set of milling experiments where input signal is insensitive to the flank wear of cutting tool and different tools’ wear evolution exhibits obvious inconsistency. Compared to the traditional CNN, the proposed AISNN significantly improves the accuracy on the verification set from 63% to 95% and on the testing set from 50% to 81%. The results demonstrate that the AISNN framework achieves significant improvement in feature extraction and generalization ability. The proposed AISNN, as a universal framework, can empower most existing deep learning-based tool wear prediction methods, enhancing their robustness in handling insensitive signals and inconsistent wear evolution and thereby promoting more industrial applications.

Computer numerical control machine tool wear monitoring through a data-driven approach

The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods.

Hybrid prognostics to estimate cutting inserts remaining useful life based on direct wear observation

The aim of this study is to monitor tool wear through recurrent direct observation, to automatically and optimally assess when it is really necessary to change tools. To achieve this goal, a hybrid prognosis algorithm is formulated to estimate cutting tools’ remaining useful life. Since cutting speed, and more generally process parameters, influences the rate of tool degradation, an adaptive prognosis strategy is presented on the basis of flank wear measurements through the application of a Particle Filter (PF) framework. The adaptability feature allows tracking changes in flank wear evolution. The idea is to fit available degradation curves of cutting tool flank land measurements, through the use of data-driven models, i.e. Multi-Layer Perceptrons (MLP) and cubic polynomials (P3). The Remaining Useful Life of the cutting tool is estimated together with its probability density function, by using a PF framework to adapt MLP weights (or P3 coefficients) along with online flank wear measurements. The devised algorithm was proven to adapt to wear trends from the field, obtained with cutting parameters not previously tested, making it suitable for a robust implementation. The approach was tested when trained upon a single run-to-failure, and validated upon four run-to-failures in different cutting conditions according to a cross-validation inspired technique. P3 was found to be more reliable (from the metrics perspective), whereas MLP allowed to be accurate with greater advance, offering a practical advantage. The proposed algorithm may also be adapted to integrate physical features, like specific force coefficients, with direct wear measurements.

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELMs) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, particle swarm optimization-least squares support vector machine, long short-term memory, stacked sparse autoencoder, recurrent neural network, and dung beetle optimizer-DELM.

Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS

Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS

Machine learning coupled with acoustic emission signal features for tool wear estimation during ultrasonic machining of Inconel 718

The material removal in the ultrasonic machining process is due to the fracture of the workpiece material. The fracture is due to energy transfer by vibrating abrasive particles. The vibration energy is induced by a tool oscillating at a frequency of more than 20 kHz. The vibrating particles not only impact the workpiece surface but also impact the oscillating tool, and it leads to the fracture of the tool, which is of interest to understand to guarantee efficient machining with accuracy and precision. In the current investigation, the tool wear during the ultrasonic machining of Inconel 718 super alloy is attempted. An acoustic emission sensor is integrated with the machining setup, and signal information is extracted in the time and time-frequency domains. The features and process parameters are input to a support vector regression model to estimate tool wear. The model developed for tool wear prediction yields an accuracy of 96.13% compared to the model developed with only process parameters, which delivers an accuracy of 84.89%. The developed model can be beneficial for the real-time monitoring of tool wear during the ultrasonic machining process for industrial and remote applications.

Application of audible sound signals in tool wear monitoring: a review

Application of audible sound signals in tool wear monitoring: a review

Tool Condition Monitoring for milling process using Convolutional Neural Networks

Tool condition monitoring has emerged as an essential approach in the machining industry to drive cost reductions and enhance productivity. In the era of Industry 4.0, tool wear monitoring can be done using various Internet of Things sensors, such as those recording acoustic emission, vibration, temperature, and pressure. Signals acquired can be processed by extracting numeric features or converting them into images.In this study, experimental milling campaigns with different input machine parameters were conducted, leading to the creation of a dataset based on tool wear measurements and acoustic emission registrations. Then we investigated a deep convolutional neural networks approach to classify image representations of the registered acoustic signals, aiming to predict the status of tool wear. Since the dataset was imbalanced, a class weights approach was used to improve model performance. An accuracy exceeding 90% was reached, demonstrating a great potential of the model in predicting tool wear state.