Cutting Tool Wear Research Articles

Development of a Conceptual Scheme for Controlling Tool Wear During Cutting, Based on the Interaction of Virtual Models of a Digital Twin and a Vibration Monitoring System.

This article discusses the issue of the joint use of neural network algorithms for data processing and deterministic mathematical models. The use of a new approach is proposed, to determine the discrepancy between data from a vibration monitoring system of the cutting process and the calculated data obtained by modeling mathematical models of the digital twin system of the cutting process. This approach is justified by the fact that some coordinates for the state of the cutting process cannot be measured, and the vibration signals measured by the vibration monitoring system (the vibration acceleration of the tip of the cutting tool) are subject to external disturbing influences. Both the experimental method and the Matlab 2022b simulation method were used as research methods. The experimental research method is based on the widespread use of modern analog vibration transducers, the signals from which undergo the process of digitalization and further processing in order to identify arrays of additional information required for virtual digital twin models. The results obtained allow us to formulate a new conceptual approach to the construction of systems for determining the degree of cutting tool wear, based on the joint use of computational virtual models of the digital twin system and data obtained from the vibration monitoring system of the cutting process.

Classification-Based Parameter Optimization Approach of the Turning Process

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

Effect of Material on Lathe Rotation at HSS (High Speed Steel) M42 Tool Cutting Tool Wear Level

In an era where science and technology are rapidly developing, in the era of globalization it opens a wide door for every individual to embrace change. Human resources, being the pulse that pumps industrial life, must be proficient in mastering science and technology, being able to apply it in every step of the industry, and steel has become an important milestone in various sectors, from building construction to machine components. However, behind the glitter of this civilization, technical processes such as turning the HSS M42 chisel face its own challenges. At certain rpm cycles, the tool experiences an erratic level of wear, as evidenced by the difference between the initial and final weights as well as the difference in the initial and final lengths. In the HSS M42 tool turning attempt, the HSS M42 1200 type tool with a size of 3/8x3/8x4" and a rotation speed of 700, 900, and 1200 rpm, with a cutting depth of 1 mm and a feed rate of 0.09, almost encountered a weakness. Tools that wear out quickly, especially at 900 rpm, make turning results less satisfactory. However, there was a light at the end of the hallway. In the same process, the use of HSS tools with HSS M42 1200 type, dimensions of 3/8x3/8x4", was able to answer the challenge with more promising success. At 900 rpm rotation, the wear that occurs is more controlled, resulting in a workpiece with a satisfactory level of fineness, reaching N6 in the measurement of surface roughness. As if to be a symbol of struggle, HSS chisels are able to cut finer traces in the ocean of HSS M42 chisels

Evaluation of lubrication mechanism of hybrid nanolubricants in turning hardened AISI D6 tool steel

Turning of hardened tool steels has posed a significant challenge for machining professionals. Usually, hardened tool steels are machined under dry with ceramic or PCBN inserts. However, dry machining imposes a condition where high cutting temperatures are developed, which becomes prohibitive when the part's geometric and dimensional accuracy is required. On the other hand, using mineral oil-based cutting fluid has encountered increasing restrictions because of its unsustainable nature. In this context, developing new sustainable lubri-cooling techniques, such as using vegetable oils blended with nanoparticles, could be an appropriate alternative. Thus, the main objective of this study was to investigate the performance of three different vegetable oil-based nanolubricants blended with three different nanoparticles (CuO, a-C:H, and CuO + a-C:H), applied under MQL when machining a quenched and tempered AISI D6 tool steel. For this purpose, facing turning trials were performed using solid PCBN inserts with the following cutting parameters: Vc = 100 m/min, ap = 0.3 mm, and f = 0.1 mm/rev. For comparison, facing turning tests were performed under dry and pure vegetable oil (without nanoparticles) and applied under MQL. Output variables included average surface roughness (Ra), flank wear (VBC), wear mechanisms of the cutting edge, chip shape, and chip compression ratio (Rc). The results showed that the vegetable oil-based nanolubricants applied under MQL improved the tribological conditions in the chip-tool and workpiece-tool interfaces, mainly in the case of CuO + a-C:H nanolubricant. In this case, it enhanced the lubricating action of the vegetable oil, decreasing cutting tool wear probably because of the combination of rolling mechanism, - provided by the CuO nanoparticles, and the formation of a protective film, supplied by the a-C:H nanoparticles.

Development of an expert system for technological support of required roughness when machining hardened steel parts on numerically controlled machines

This work addresses the problem of improving the surface quality of parts of rotation bodies, which are made of hardened steels, in the course of their machining on lathes with numerical program control. The research object was the surface roughness of parts. The methodology involved system analysis and synthesis, artificial neural networks, fuzzy logic, experiment, and processing of experimental results. A decomposition scheme for the expert system structure was developed, which can be used as the basis for formulating requirements to a future expert system for monitoring and prediction of roughness parameters. It was established that the models currently applied to describe the relationship between surface quality and the technological regimes used to ensure the technological level of roughness give a high error of over 20%. The possibility of using models that apply artificial intelligence and contain neural network blocks and decision-making devices based on fuzzy logic is substantiated. It is shown that such a combination makes it possible to customize the system for processing parts of a certain production range, as well as a more correct assessment of the onset of catastrophic wear of cutting tools. The neuro-fuzzy model was confirmed to have an error of less than 10%, which is significantly lower than when using spectral or correlation models. According to the testing results, the proposed expert system for monitoring and prediction of roughness parameters enables a 2.5-fold reduction in the scatter of roughness parameters under an increase in tool wear, compared to without its application. Thus, the proposed system makes it possible to assess the level of cutting tool wear more correctly and determine the onset of its limit state.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

Evaluation and quantification of diffusion wear between cutting chip and workpiece using forging press

The state of the interface between the workpiece and the cutting tool affects the cutting temperature and pressure on the tool surface during the cutting process. In particular, while cutting difficult-to-cut materials such as Ni-based alloy 718, the workpiece exhibits a high affinity for cutting tool materials and could easily adhere to them. Adhesion can, at times, adversely affect productivity. The diffusion between the cutting tool and the workpiece is a factor considered to contribute to the adhesion phenomenon during cutting. Addressing this issue involves choosing tool materials and coated materials with high resistance to diffusion and optimizing cutting conditions, particularly the cutting speed, which significantly impacts cutting temperature. However, because cutting tool wear comprises various forms, clarifying the effect of diffusion on tool wear remains open. In this study, to reproduce the diffusion phenomenon between cutting tool and workpiece, two pairs of test specimens were prepared: (1) cemented carbide-AISI 1045 and (2) cemented carbide-Alloy718, which could be held at high temperature under vacuum conditions by a forging press. The degree of diffusion phenomena was evaluated at each tool-work material interface, and the quantification of diffusion amount was performed by diffused element in each work material. Additionally, the theoretical analysis of the diffusion phenomenon using the thermodynamic and phase diagram calculation software Thermo-Calc was also performed.

Experimental Investigation of the Effects of Coolant Temperature on Cutting Tool Wear in the Machining Process

Experimental Investigation of the Effects of Coolant Temperature on Cutting Tool Wear in the Machining Process

Investigation of coated and uncoated carbide cutting tool wear in dry turning of en aw 2007 aluminum alloy

Investigation of coated and uncoated carbide cutting tool wear in dry turning of en aw 2007 aluminum alloy

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.

Understanding tool cutting-edge microstructure and deformation mechanism induced by adhesive wear in the turning of nickel-based superalloys

Nickel-based superalloy has great potential in the aerospace industry as key components. However, difficult-to-machine characteristics have further limited its application. Thermal-barrier coatings of the titanium-based family on carbide tools offer exceptional performance in cutting superalloys, combining high-temperature stability and remarkable toughness. This work primarily concentrates on understanding cutting-edge microstructure and deformation induced by tool adhesive wear in the turning of nickel-based superalloys with experiment and modelling. The dominant tool wear mechanisms are revealed to be adhesive wear and abrasive wear by SEM/EDS. The microstructure of the cutting-edge interface is qualitatively and quantitatively investigated by SEM/EDS and EBSD. The adhesive layer thickness at cutting edge is about 10–30 μm. The GND density of WC grains at cutting edge is 15-20 × 1014/m. The nanomechanics properties of the tool wear interface were quantitatively evaluated by nanoindentation. The average hardness of WC and Ni-superalloy at cutting-edge interface is evaluated to be 15–19 GPa and 6.2–6.5 GPa, respectively. Further, the underlying deformation mechanisms induced by tool wear behaviours are revealed through the transient heat conduction model and cutting-edge stress distribution model. This research offers a framework and mechanism for the cutting tool wear surface/interface characteristics targeted to the difficult-to-cut superalloy materials.

Theoretical study on gas‐phase reactions during chemical vapor deposition of TixAl1–xN from TiCl4, AlCl3, and NH3

AbstractFcc‐TixAl1–xN (TiAlN) coatings synthesized via chemical vapor deposition (CVD) reduce cutting tool wear. Although CVD conditions reportedly influence coating quality, no quantitative guidelines are yet available. To quantitatively study the film‐forming mechanism of TiAlN CVD, the gas composition over the surface must be known. Therefore, we developed a gas‐phase elementary reaction model for TiAlN CVD derived from TiCl4/AlCl3/NH3. First, we constructed a novel thermodynamic dataset including molecules that contained both Ti and Al, and calculated the equilibrium composition. Thermal equilibrium calculations in the gas phase showed that the most stable species were AlCl3 and TiCl3 rather than TiCl4. An elementary reaction model was constructed based on the kinetics of the gas‐phase species that were generated. Kinetic analysis revealed that gas‐phase reactions were largely absent under our reactor conditions. The thermal equilibrium calculations indicated that TiCl4 may have given rise to TiCl3. Thus, other reaction pathways of TiCl4 to TiCl3 were explored. We calculated the reaction rate constants of 12 reactions of Ti species and added them to the model, which revealed that TiCl4 decomposed to TiCl3 via TiCl3NH2. Under our conditions, TiCl4 and TiCl3NH2 are the major Ti species and AlCl3 and AlCl2NH2 are the major Al species, which suggests that some of these species may form films. Unlike in the earlier reaction model, NH2 and H radicals were produced, which may have contributed to the surface reactions. For reactors with large Surface/Volume ratio of reactor, the effects of gas‐phase reactions should be considered. Our reaction model enables estimation of the partial pressures of reactor gas species and will therefore aid study of the TiAlN deposition mechanism.

Digital twin-based anomaly detection for real-time tool condition monitoring in machining

Real-time tool condition monitoring (TCM) has been emerging as a key technology for smart manufacturing. TCM can improve the dimensional accuracy of products, minimize machine tool downtime, and eliminate scraps and re-work costs. Digital twins offer new opportunities for real-time monitoring of machining processes, which can, in principle, take into account changes in machining processes and operating environments, help understand mechanisms of cutting tool wear, and improve the anomaly detection accuracy and fault diagnosis results. The present study exploits these potential advantages of digital twins and proposes a new digital twin-based anomaly detection framework for real-time TCM in machining. The framework of the digital twin consists of three parts: the physical product, the virtual product and data flow connections. Within this framework of the digital twin, the “physical product” represents the machining processes. The “virtual product” includes a real-time data-driven model representing the dynamic relationship between vibration data measured from machining processes as well as the model frequency features (MFFs)-based diagnostics for cutting tool anomaly detection. The “data flow connections” involve real-time measured vibration data and machine tool numerical controller (NC) signals providing real-time information on machine tool dynamics and various machining processes. The novelty is associated with an innovative integration of real-time data-driven modeling, MFFs extraction, and MFFs and machine tool NC signal-based tool wear diagnostics. This, for the first time, enables the concept of digital twins to be potentially applied to the TCM for complicated dynamic machining processes which, as far as we are aware of, has never been achieved before. Comprehensive field studies have demonstrated the effectiveness of the proposed digital twin-based TCM framework and its potential industrial applications.

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

RETRACTED: An acoustic imaging recognition based cutting tools wear state prediction method

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Investigation of Cutting Tool Wear in the Milling Process of the Inconel 718 Alloy

Investigation of Cutting Tool Wear in the Milling Process of the Inconel 718 Alloy

Analisis Kinerja Mesin CNC Milling 3 Axis pada Pembuatan Pipa Jet Oil

The study evaluated the performance of a 3-axis CNC milling machine in the production of jet oil pipes with a focus on achieving a high level of precision. Through variation of cutting parameters and feeding speed, the machine succeeded in reaching the industry standard for jet oil pipe production. Optimization of parameters improved production efficiency, demonstrated by results that achieved the desired precision tolerance. Cutting tool wear analysis provides insight into machine performance and possible repair actions. This research confirms the potential of 3 axis CNC milling machines in addressing the challenge of high-precision jet oil pipe production, contributing to the development of CNC Milling technology in the context of the oil and gas industry.

Local regularization assisted split augmented Lagrangian shrinkage algorithm for feature selection in condition monitoring

Feature selection plays a vital role in improving the efficiency and accuracy of condition monitoring by constructing sparse but effective models. In this study, an advanced feature selection algorithm named the local regularization assisted split augmented Lagrangian shrinkage algorithm (LR-SALSA) is proposed. The feature selection is realized by solving a l1-norm optimization problem, which usually selects more sparse and representative features at less computational costs. The proposed algorithm operates in two stages, namely variable selection and coefficient estimation. In the stage of variable selection, the primal problem is converted into three subproblems which can be solved separately. Then individual penalty parameters are applied to every coefficient of the model when dealing with the first subproblem. Under the Bayesian evidence framework, an iterative algorithm is derived to optimize these hyperparameters. During the optimization process, redundant variables will be pruned to guarantee model sparsity and improve computational efficiency at the same time. In the second stage, the coefficients for the selected model terms are determined using the least squares technique. The superior performance and efficiency of the proposed LR-SALSA method are validated through two numerical examples and a real-world cutting tool wear prediction case study. Compared with the existing methods, the proposed method can generate a sparse model and ensure a good trade-off between estimation accuracy and computational efficiency.

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.