On-line Tool Wear Research Articles

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.

Tool Wear State Recognition Based on One-Dimensional Convolutional Channel Attention.

Tool wear state recognition is an important part of tool condition monitoring (TCM). Online tool wear monitoring can avoid wasteful early tool changes and degraded workpiece quality due to later tool changes. This study incorporated an attention mechanism implemented by one-dimensional convolution in a convolutional neural network for improving the performance of the tool wear recognition model (1DCCA-CNN). The raw multichannel cutting signals were first preprocessed and three time-domain features were extracted to form a new time-domain sequence. CNN was used for deep feature extraction of temporal sequences. A novel 1DCNN-based channel attention mechanism was proposed to weigh the channel dimensions of deep features to enhance important feature channels and capture key features. Compared with the traditional squeeze excitation attention mechanism, 1DCNN can enhance the information interaction between channels. The performance of the model was validated on the PHM2010 public cutting dataset. The excellent performance of the proposed 1DCCA-CNN was verified by the improvement of 4% and 5% compared to the highest level of existing research results on T1 and T3 datasets, respectively.

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.

On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)

Since tool wear during machining process imposes a significant limitation on production quality as well as efficiency, on-line tool wear monitoring (TWM) is one of considerably crucial issues for intelligent manufacturing system. However, there is still a lack of universal on-line TWM techniques appropriate for the practical production process with variable machining parameters. Therefore, this study proposes a highly condition-adaptive method for tool wear state monitoring and remaining useful life (RUL) estimation under time-varying operation conditions. Multi-domain features are first extracted from vibration, acoustic emission and sound signals, and subsequently fused by the kernel principal component analysis (KPCA) to highlight discriminative information correlated to tool wear. A condition-adaptive hidden semi-Markov model (CAHSMM) is established to describe the evolution of tool wear, in which the accelerated failure time model (AFTM) embedded with Taylor tool life equation is utilized to determine the state transition probabilities that are strongly dependent on cutting parameters and duration time. Moreover, to extrapolate the Gaussian mixture models (GMMs) used to represent the observation probability beyond the range where they were trained, a Moment matching based on-line unsupervised transfer learning (OUTL) method is developed. After obtaining the state transition and observation probabilities, a modified forward algorithm is eventually implemented so as to assess tool wear online. Both the dataset from milling experiments of Ti6Al4V and PHM 2010 are employed for verifying the effectiveness of the proposed method. The results show that the presented method enables to reliably evaluate wear state and RUL of the cutter even under time-switching operation parameters, which demonstrates its promising potential for industrial applications.

On-line quality control and tool wear evaluation in trimming process by data analytics techniques

Sheet metal is an extremely common form of use in broad engineering sectors and represents the main consumption of steel and aluminum in Europe. All production processes where sheet metal is used require cutting operations. Therefore, it is an extremely relevant procedure, common and of great economic importance in material-intensive sectors such as automotive, construction and engineering. In a competitive environment where the demand for quality increases continuously and using harder to process high-performance materials, online monitoring and quality control become an attractive prospective. In this regard, application of data analytics techniques offers a promising toolset for fast and efficient analysis. This work presents a pilot demonstration of an instrumented sheet metal cutting line that integrates online quality control and predictive maintenance concepts, namely detection of wear in tools as well as problems arising from the raw material and process. Automated sequential cutting was performed using a straight trimming die mounted on a hydraulic press to cut High-Mn TWIP steel strip. Three piezoelectric force sensors were located in the trimming tool, collecting cycle data. Tests performed included studying the effect of blunting on the M2 (1.3343) tool, as well as the influence of correct lubrication. The resulting data were analyzed through the extraction of several parameters from each cycle to correlate die wear-induced force response evolutions with the shear-induced deformation in High-Mn TWIP steel sheets. Extracted parameters were the maximum and minimum force of each cycle and the impulse (integrated force over time) was computed and segmented by plastic deformation and crack contributions. Our analysis show that advanced algorithms can be used to analyze sensor data on an automated installation, revealing OK/NOK conditions and anomalous events.

An adaptive method of measuring the rake face wear of end mills based on image feature point set registration

An acceptable tool wear evaluation and acquisition approach is critical for developing an accurate, rapid, and practical online tool wear prediction system. The one-dimensional crater wear depth is difficult to quantify, and relying solely on flank wear width is very simplistic. External conditions such as vibration and illumination have a large impact on two-dimensional wear area and three-dimensional volume. In practice, these are difficult to successfully characterize tool wear. This work proposes a two-dimensional rake face edge loss area as an additional indicator to assess milling cutter wear. An adaptive measurement approach based on 2D image feature point set matching is presented to suppress the area calculation error caused by image position offset and chips or built-up edges. 201 images were collected in two groups of cutting experiments for comparison with the proposed method and the other two methods, manual registration and non-registration. The results show that the proposed method's measurement values are closer to the actual values and have a higher measurement efficiency. Furthermore, the proposed indicator is sensitive to micro-scale changes to the blade edge, especially at the tip, and can be used as a supplement one to monitor tool wear more comprehensively.

Development of tool wear condition on-line monitoring method for impeller milling based on new data processing approach and DAE-BP-ANN-integrated modeling

Tool wear condition monitoring plays an important role in the maintaining machining accuracy and machining efficiency of complex surface parts. In this study, a new on-line tool wear monitoring method based on a self-developed data processing approach for the impeller milling was proposed. To achieve that, a new tool wear experimental platform was first built to collect both the spindle current signal and thermal deformation data in entire life cycle of cutter. Based on collected data, features of the time domain, frequency domain and time-frequency domain were extracted indiscriminately, and a 38 × 156 feature-sample set was subsequently established. To further reduce the dimensions of this feature-sample set and rise its characterization capability, the feature set was further processed using the sensitivity analysis and deep auto-encoder algorithm. Finally, 12 synthesized features were filtered out and then used to build the mapping model of signal synthesized features to different tool wear conditions by adopting the structural artificial neural network (ANN) integrated with back propagation (BP) algorithm. To verify the reliability of the proposed BP-ANN-integrated tool wear condition monitoring model, another comparison analysis of different data processing approaches was conducted. The comparison results showed that the proposed method for tool wear condition online monitoring had reliable performance and the recognition accuracy was 88.9%.

Initial tool wear and process monitoring during titanium metal matrix composite machining (TiMMC)

When machining titanium metal matrix composites (TiMMCs), the abrasiveness of the reinforcement particles of this material leads to numerous machinability challenges, namely severe tool wear and poor machined surface quality. This paper aims to study the initial tool wear and the phenomena occurring during machining of titanium metal matrix composites. Initial wear refers to tool wear that occurs early in the cutting process. When the tool life is relatively short, the initial wear could significantly influence the remaining machining process. Therefore, a better understanding of the initial tool wear mechanisms and characteristics could lead to solutions extending the tool life, thereby improving the cutting process and production efficiency. Hence, the flank tool wear was assessed and thoroughly investigated using a scanning electron microscope (SEM), an energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). A tool condition monitoring system (TCMS) for online tool wear evaluation was employed to detect the transition times between wear stages during the machining process.Based on results, the types of wear in the initial zone were characterized. Abrasion, adhesion and cracking are considered as the critical morphological changes. The reduction of workpiece surface damage and tool wear rate could be related to the formation of a protective layer as a thermal barrier at the first transition time, when the initial tool wear ends and the steady state wear region begins. This moment, as the first transition time can be a short period, less than 15 s, which was detected by fractal analysis of force and vibration signals during machining of TiMMCs. The clear signatures of the transition times were suggested by the results of the fractal analysis.

A transformer‐based end‐to‐end data‐driven model for multisensor time series monitoring of machine tool condition

AbstractOnline determination of a cutter's health status is crucial for the attainment of condition‐based automated tool change in computer numerically controlled (CNC) machining. Due to the impracticalities associated with direct condition measurements, data‐based modeling of monitoring signals provides a viable practical route. However, the highly noisy and redundant nature of the associated data impacts negatively on model's accuracy and typically calls for additional initial preprocessing before modeling. Additionally, the long sequential data entails widely varying condition distributions exhibited by different cutters, even from the same batch on similar machining parameters, posing a challenge to model generalization. An end‐to‐end model has thus been developed to work directly on unprocessed data to establish global sensitive features from varying distributions for online tool wear estimation in CNC machining. The model utilizes three main functional blocks. First, a data denoising and feature selection block automatically processes raw multisensor data directly, dispensing with scaling or preprocessing of inputs as conventionally done. Each sensor channel's independence is preserved at initial processing ensuring complementary information from different sensors is utilized while simultaneously minimizing existing redundancies. The weighted denoised data is then processed through a transformer encoder block for determination of global dependencies in the time‐series sequence, regardless of the time‐step position. The learned features are then fed to an upper supervised learning block for association with the monitored wear condition. The developed model works directly on raw noisy data irrespective of scaling differences, saving on preprocessing computational cost. The global associations extracted on long sequences by the transformer‐encoder allow for model generalization to varying wear distributions. The parallel processing structure of all channels ensures complementary information is utilized minimizing unforeseen model bias. The model's performance as evaluated on experimental milling data and further comparison with other reported models on same dataset shows attainment of comparable state‐of‐art results.

Dense-Block Structured Convolutional Neural Network-Based Analytical Prediction System of Cutting Tool Wear

Cutting tool wear strongly affects machining processes. Conventionally, multiple types of sensors are installed on the main rotating axis and platform of a machine, and signal analysis is used to determine the condition of the tool in real time through machine learning. With the traditional approach, to measure tool wear, it is required to remove the tool and place it underneath the microscope for measurement to obtain relatively precise results. The time cost, however, is relatively high; it is not cost-effective. With the charge-coupled device (CCD) camera installed in the machine tool, tool wear may be estimated through programed processing. The CCD camera, however, will affect spatial configuration in the machine tool and additional time is needed for tool wear measurement upon completion of processing each time. With the accelerometer sensor combining an offline or online tool wear measurement system and using the training method in this research model upon completion of the dataset, forecasting can be done with the precompleted model, which saves the time needed for offline or online wear measurement. However, the deployment of multiple sensors is difficult and expensive in practice. Used in this study were milling data, namely acoustic emission (AE), vibration, and current data from sensors installed on the rotating axis and platform of a machine. The data were from a National Aeronautics and Space Administration (NASA) dataset. The accelerometer data were used to develop a novel machine-learning algorithm. The vibration signals were integrated with other machining parameters. Signal preprocessing was used to reduce interference from environmental noise, and parameter records and feature signals were analyzed. A 1-D convolutional neural network (CNN) model similar to the DenseNet framework was developed. The model was optimized with framework and parameter adjustment and verified using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -fold cross-validation. The model had a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.09. Compared with other machine-learning models, the developed model has higher accuracy.

Online tool wear monitoring by super-resolution based machine vision

The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the self-similarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.

Design and research of digital twin machine tool simulation and monitoring system

The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool.

Analysis of Acoustic Emissions for Determination of the Mechanical Effects of Scratch Tests

Acoustic Emission (AE) is a promising technique for measuring tool wear online and in real time. In this work, scratch tests were conducted to better understand the “pre-wear” AE response based on loading conditions that were not sufficient to generate galling. The scratch tests used the same type of indenter against two different sheet materials: aluminum and steel. The results showed that AE parameters such as the mean frequency, Centroid frequency and Shannon entropy outperformed other frequency domain techniques by discriminating between the two sheet materials in scratch tests. From the literature, the frequency region of interest was expected to be sub 300 kHz. However, in this study, activity below this threshold was found to be noise, whereas distinct frequencies were found at much higher frequencies than expected. These results are compared against single grit “SG” tests of both mild steel- and nickel-based superalloys to allow comparison of the two test methods and materials used. This comparison showed that the SG tests excited the acoustic emission in ways in which the scratch tests did not. Another factor when using acoustic emissions to monitor sheet metal forming is the differences obtained in energy–frequency mapping, where many report the galling phenomena between a certain amplitude and frequency range. Such results are specific to the setup and the materials/geometries used. Further work presented here compares different scratch tests where energy–frequency mapping is different for different materials/geometries.

Quantitative evaluation method of tool wear based on morphological characteristics of machined surfaces

Tool wear monitoring techniques have been extensively applied in the modern manufacturing sectors to carry out predictive maintenance and to avoid the massive loss induced by sudden downtime. However, the online tool wear detection based on either the machining vibration or the variation of cutting power always encounters the interference caused by the excessive change of workpiece materials or even by the fluctuations of the coolant pressure. In the present work, a rapid half-online tool wear monitoring method based on the standard sample cutting and focus-variation scanning technique (FVST) was designed to quantify the tool wear extents. Invar alloy was used as the standard sample due to its low thermal expansion and ductile machinability. The results show that the texture of the machined surface is determined by the change of the cutting edge, and thus the surface morphologies can be regarded as the index of tool wear. Tool wear condition was revealed and quantified through the assessment models established based on the characteristic parameters including the width/depth of the texture and the surface roughness of the standard sample machined by new and worn tools under the identical cutting conditions. The results show that the tool wear can be revealed efficiently via the help of the surface morphological analysis, and the method is more none-destructive, rapid, and reliable compared with the conventional tool wear monitoring methods. In sum, the quantitative evaluation method of tool wear based on the morphological characteristics of the machined standard sample surfaces is confirmed capable of improving the accuracy and efficiency of tool wear monitoring.

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

MicroEye : A low-cost online tool wear monitoring system with modular 3D-printed components for micro-milling application

Tool detachment during the machining process is often required by many image-based tool wear monitoring (TWM) systems. Tool detachment prevents the online mode of the wear measurement, extends the machining time, and contributes to measurement inaccuracy. Other alternatives of the image-based TWM systems have been developed with the image-acquisition device located statically near the tool position without the requirement for the tool detachment. However, due to its proximity to the machining site, the image-acquisition device may experience obstruction from the workpiece chips and the splash of coolant fluid during the machining process, resulting in non-optimal TWM. This article presents MicroEye – an online image-based TWM system with modular 3D-printed components to overcome the two problems. MicroEye offers great flexibility in its operation through the use of an active 6-DOF (degree of freedom) robotics arm with a camera at the end-effector. MicroEye does not require tool detachment to perform tool wear monitoring and can be safely placed outside the machining area. MicroEye is the first open-sourced, 3D-printed components and active dynamic-type TWM system for the application of micro-milling. MicroEye can be built at a low-cost (approximately US$ 872, including the camera). MicroEye is suitable for various micro-milling sites, from laboratory scale to middle-low workshop.

The method of self-learning based online tool wear monitoring in semi-finishing or finishing working step

The method of self-learning based online tool wear monitoring in semi-finishing or finishing working step

Tool wear mechanism of turning Inconel 718 and EEMD-based feature analysis

In the cutting process of high temperature alloys, the performance of the tool is closely related to the surface quality of the workpiece. In this paper, a turning test was conducted on nickel-based high-temperature alloy Inconel 718 using PVD-coated carbide tools, and the microscopic morphology of tool wear was observed by scanning electron microscopy (SEM), and the chemical elemental composition of the tool wear surface was analyzed by energy spectrum analyzer (EDS), and the wear mechanism of PVD-coated carbide tools in three stages of tool wear was analyzed in detail. On this basis, the characteristic quantities closely related to tool wear were extracted in the time and frequency domains by the collected cutting force signals, which provide effective characteristic inputs for the subsequent study of online tool wear monitoring.

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Tuning the parameters of Computerized Numerical Control (CNC) is essential for practical manufacturers. Well configured parameters ensure the efficiency of production and the accuracy of the products. However, with the abrasive wear on the flank of the milling cutter, the milling processing parameters should re-configure to adapt to the increment of the abrasive wear. This paper aims to propose a method to predict the abrasive wear rate increment on the flank of the milling cutter and optimize the processing parameters of CNC milling. Firstly, we set a cutting data acquisition system to sample the processing time and cutting force among X, Y coordinates based on the five-factor and four-level orthogonal experiments. Then, the sampled cutting force data increment is transformed into the abrasive wear rate increment by applying the incremental model. Next, five processing parameters for CNC milling are optimized by the gray relational method, which takes the limited abrasive wear rate increment of the flank face and the non-increasing processing time as the constrained conditions. We obtain the relationship between five processing parameters and abrasive wear rate increment. We also find the basic principle of selecting process parameters is to reduce the abrasive wear rate without increasing the processing time. The experimental results verify that the optimized process parameters make the gray relational degree increase by 0.02, and the abrasive wear rate increment decreases by 0.42432 × 10−10 mm3/s without affecting the production efficiency. In the prediction section, by applying the Back Propagation (BP) neural network, we obtain an accurate prediction model from measurable five factors to the abrasive wear increment on the flank of the milling cutter. The maximum error between the predicted value and the actual value is 0.0003, and the predicted value curve fits well with the actual value curve. From the perspective of abrasive wear rate increment prediction, it provides a new idea for online tool wear monitoring.

Development of a tool wear online monitoring system for dry gear hobbing machine based on new experimental approach and DAE-BPNN-integrated mathematic structure

Development of a tool wear online monitoring system for dry gear hobbing machine based on new experimental approach and DAE-BPNN-integrated mathematic structure