Evaluation Of Tool Wear Research Articles

Evaluation of tool wear and surface roughness in high-speed dry turning of Incoloy 800

Evaluation of tool wear and surface roughness in high-speed dry turning of Incoloy 800

Evaluation of tool wear during micro-milling of ultrasonically assisted abrasive peened Ti-6Al-4V

When a cutting tool shears through a workpiece, the interaction causes wear of the tool which is based on relative hardness, surface characteristics of the tool and the workpiece, cutting environment and thermal behaviour at the work/tool interface. In this study, wear of a micro-end milling tool is investigated while milling of as-received and peened work material (in this case, Ti-6Al-4V). The peening is performed with the help of an in-house developed ultrasonic cavitation process. The process excites the abrasives suspended in the cavitating medium, which impart on the work surface and induce compressive residual stress and uniform grain refinement. As the cutting process advances, a built-up is formed more rapidly in the as-received workpiece, leading to higher cutting forces and earlier chipping of the cutting edge. Under the peened conditions, it is found that the increase in tool edge radius and flank wear width reduce by 50 % and 54 % respectively compared to the as-received one. Additionally, peening-induced grain refinement leads to a 26 % decrease in surface roughness of the machined surface and inhibits burr formation by 60 %. A tool wear chipping model is employed to predict the tool diameter, cutting edge radius and flank wear width of the worn tool. Results show that the magnitude of flank wear, edge radius of worn tool and tool diameter deviate from experiments by 15.2 %, 14 % and 13 % respectively.

Experimental Evaluation of Surface Roughness, Burr Formation, and Tool Wear during Micro-Milling of Titanium Grade 9 (Ti-3Al-2.5V) Using Statistical Evaluation Methods

Titanium grade 9 (Ti-3Al-2.5V) stands out as a preferred material in various industrial applications because of its suitable properties. Its applications span diverse sectors, including precision manufacturing, where it is utilized to produce honeycomb structures for advanced aeronautics, as well as for certain biomedical components. In parallel, micro-milling has gained widespread utilization across medical, aerospace, and electronic industries due to the increasing demand for miniature products in these domains. This current research study aims to explore the impact of various micro-milling process parameters—specifically, feed rate, cutting speed, and depth of cut—on the surface quality, burr formation, and tool flank wear of titanium grade 9. Research findings reveal that the feed rate plays a major role in influencing surface roughness (contribution ratio (CR): 62.96%) and burr formation (CR: 55.20%). Similarly, cutting speed and depth of cut significantly affect surface roughness, contributing 20.32% and 9.27%, respectively, but are insignificant factors for burr width. Tool flank wear is primarily influenced by cutting speed (CR: 54.02%), with feed rate contributing 33.18%. Additionally, the feed rate and cutting speed are significant factors in determining the length of the burr, with contribution ratios of 77.70% and 7.77%, respectively. Confirmatory tests conducted at optimum parameters selected from the main effects plot validated the experimental results.

Evaluation of tool wear during turning of Ti6Al4V alloy applying MQL technique with Cu nanoparticles diversified in terms of size

In case of hard-to-cut materials, as titanium alloy Ti6Al4V, eco-friendly cooling methods or their modifications are still being developed. Therefore, various types of nanofluids are applied in the cutting process to reduce friction in the cutting zone and thus heat accumulation in the tool corner. This paper presents an analysis of the selected tool wear indicators, as well as the wear mechanisms, after turning with various cooling/lubricating techniques, including: MQL with the addition of copper nanoparticles (CuNPs), dry machining and the MQL technique without nanoparticles. Four sizes of CuNPs were applied: 22 nm, 35 nm, 65 nm and 80 nm. Analysis of SEM images and maps of elemental distributions on the cutting edge working surfaces allowed to determine that the smallest sizes of copper nanoparticles introduced in the cutting fluid result in reduced tool flank wear, as well as the reduced width and depth of the crater wear on the rake face. With the smallest size of copper nanoparticles, a “ball bearing” and tribofilm formation effects were obtained, which prevented intense adhesive and abrasive wear. Turning, applying MQL + CuNPS 22 nm method resulted in a decrease of selected tool wear rates from 25.7% to 55% compared to effects reached during dry machining, and from 11.7% to 39.2% compared to effects of MQL turning without addition of nanoparticles to the cutting fluid.

Researches on tool wear progress in mill-grinding based on the cutting force and acceleration signal

Tool wear progression has greatly impact on the machining quality, tool wear evaluation is useful to obtain tool states. Mill-grinding experiments are performed using the electroplated diamond tool to reveal tool wear progression. Both time and frequency domain, dimensional and dimensionless characteristics of cutting force and acceleration signals are analysed. Among time domain characteristics, peak and average values exhibit low and stable amplitudes at the initial wear stage, and evolve to large and fluctuant amplitudes at the severe wear stage. For frequency domain analysis, gravity frequency exhibits a decrease trend with tool wear progression. Among dimensionless characteristics, kurtosis and margin factors show a maximum value when tool wear progression goes into the severe wear stage, and can be applied to evaluate tool wear progression. Tool wear modes include the wearing flat process and fracture of diamond grains, tool wear mechanisms include the abrasive wear, adhesive wear and grain fracture.

Experimental evaluation of tool wear and surface roughness under different conditions in high-speed turning of Ti6Al4V alloy

Experimental evaluation of tool wear and surface roughness under different conditions in high-speed turning of Ti6Al4V alloy

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.

Simulation of Work Hardening in Machining Inconel 718 with Multiscale Grain Size.

Machining nickel-based alloys always exhibits significant work-hardening behavior, which may help to improve the part quality by building a hardened layer on the surface, while also causing severe tool wear during machining. Hence, modeling the work-hardening phenomenon plays a critical role in the evaluation of tool wear and part quality. This paper aims to propose a numerical model to estimate the work-hardening layer for a deeper understanding of this behavior, employing both recrystallization-based and dislocation-based models to cover workpieces with multiscale grain sizes. Different user routines are implemented in the finite element method to simulate the increase in hardness in the deformed area due to recrystallization or changes in the dislocation density. The validation of the proposed model is performed with both literature and experimental data for Inconel 718 with small or large grain sizes. It is found that the recrystallization-based model is more suitable for predicting the work-hardening behavior of small-grain-size Inconel 718 and the dislocation-based model is better for that of large-grain-size Inconel 718. Further, as an important type of cutting tool in machining Inconel 718, the chamfered tools with different edge geometries are employed in the simulations of machining-induced work hardening. The results illustrate that the uncut chip thickness and chamfer angle have a significant influence on the work-hardening behavior.

Wide-bandwidth cutting force monitoring via motor current and accelerometer signals

Cutting forces are valuable for monitoring machining processes, as they allow the evaluation of tool wear, process stability, and workpiece quality. This paper presents a new non-intrusive method for monitoring cutting forces using a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoothing. The method incorporates structural acceleration measurements as well as feed drive motor currents, thereby achieving wide estimation bandwidth and steady-state (DC) gain preservation. The KF + RTS smoothing are parameterized using spindle speed-sweeping cutting tests, which significantly reduces the experimental effort for model identification. The approach was validated in different machining conditions, which demonstrated good agreement with dynamometer measurements.

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.

Evaluation of Surface Roughness, Tool Wear and Chip Morphology during Machining of Nickel-Based Alloy under Sustainable Hybrid Nanofluid-MQL Strategy

Nickel-based super alloys exhibit high strength, oxidation and corrosion resistance; however, the machining of these alloys is a challenge that can be overcome with effective cooling/lubrication techniques. The use of a minimum quantity lubrication (MQL) technique is limited to lower cutting parameters due to the tremendous heat produced during the machining of Inconel 718. Sustainable and eco-friendly machining of Inconel 718 can be attained using MQL and lubricants based on nanofluids because of their improved heat transfer capabilities. For that purpose, the performance of hybrid nanofluid-MQL is examined. In this novel study, graphene and hexagonal boron nitride (hBN) nanoparticles are reinforced with palm oil and delivered to the machining interface using an MQL setup. The machining experiments are performed under the conditions of dry, wet, MQL and MQL with graphene/hBN deposited in palm oil. The machining performance under selected cutting conditions is assessed by analyzing the surface roughness, tool wear, chip morphology and surface quality of the machined workpiece. A comparison of results showcased the effectiveness of hybrid nanofluid-MQL with improvement in surface finish, reduction in tool wear and favorable chip forms concerning all other machining conditions.

Local-feature and global-dependency based tool wear prediction using deep learning

Evaluation of tool wear is vital in manufacturing system, since early detections on worn-out condition can ensure workpiece quality, improve machining efficiency. With the development of intelligent manufacturing, tool wear prediction technology plays an increasingly important role. However, traditional tool wear prediction methods rely on experience and knowledge of experts and are labor-extensive. Deep learning provides an effective way to extract features of raw data and establish the mapping relationship between features and targets automatically. In this paper, a new local-feature and global-dependency based tool wear prediction method is proposed. It is a hybrid approach combining manual features with automatic features. Firstly, an enhanced CNN network is designed and applied on the transformed wavelet scalogram to learn the local single-scale specific features and multi-scale correlation features automatically. Secondly, sequence of local feature vectors combining manual features with automatic features are fed into multi-layer LSTM step by step for the global dependency. A fully connected layer is then trained to predict tool wear. Finally, two statistics are proposed to illustrate the overall prediction performance and generalization ability of the model. An experiment illustrates the effectiveness of our proposed method under multiple working conditions.

Quantitative Evaluation of Tool Wear in Cold Stamping of Ultra-High-Strength Steel Sheets

Quantitative Evaluation of Tool Wear in Cold Stamping of Ultra-High-Strength Steel Sheets

Evaluation of tool performance and wear through vibration signature analysis in drilling of IS3048 steel

In this paper, a connection between vibration amplitude and tool wear when drilling of IS3048 steel utilizing different dimensioned tools is dissected through tests. Discriminant features, which are sensitive to drill wear and breakage, were developed. These were discovered to be somewhat impervious toward sensor location and cutting conditions. In the process, the vibration amplitude features a checking highlight dependent on ascertaining both the tools and their performance over vibrations, which was discovered to be somewhat powerful for on-line identification of drill tool breakage in both frequency and time domains. These vibrational amplitude signal features are directly affected, related to the tool geometry, which give higher chances of tool selection criteria during the drilling process. The experiments were carried out using solid carbide tool with change in tool geometry under dry conditions where the vibration amplitude for both is evaluated. The results revealed that cutting tool vibrational amplitude and tool wear were relatively dependent showing the tool selection of suitable tool geometry.

Vibration-assisted micro milling of AISI 316L produced by laser-based powder bed fusion

Additive manufacturing is suitable for the economical production of highly individual parts and components, but requires machining to produce surfaces of sufficient quality - as required on functional surfaces, for example. In many cases, the functionality of surfaces can be additionally enhanced by microstructuring e.g. via micro milling. The layered structure of additive manufactured materials, however, influences the process and process results of micro milling applications.In this paper it is analyzed whether the influence of the anisotropic material properties of additively manufactured workpieces can be compensated by vibration-assistance. Micro milling of additively produced AISI 316L with additional vibration-assistance is investigated. Besides the qualitative evaluation of tool wear, a quantitative analysis of the process forces, the surface roughness and the burr formation are used to evaluate the micro milling process. A significant influence of the vibration-assistance on the process forces and burr formation was observed.

Primary and secondary tools’ life evaluation for soft ground TBMs

Tool wear and tool life are very important issues for soft ground tunnel boring machines (TBMs) as they are directly related to the time and cost of tunneling. These issues may even become more important in planning costly interventions. In this regard, there are a few methods available to assess the tool life; however, the need for a thorough discussion and the development of new models is still highly demanded by the industry. This paper presents a summary of different methods for soft ground tool life, and tool wear evaluation. A comparative study is performed to indicate the predictability of available tool life evaluation methods based on the collected data from 23 soft ground TBM projects from around the world. Based on this database, new formulas are developed to evaluate tool life and tool wear for both primary and secondary cutting tools. The data analysis is conducted with single and multiple regression methods. The results show the equivalent quartz content and the standard penetration test are among the major influencing parameters for the evaluation of tool life of the primary cutting tools. In this regard, an estimation chart is generated for the quick evaluation of tool life for both earth pressure balance and slurry TBMs. The outcomes of this paper can be very useful in both the planning and construction stages of a TBM tunneling project where the intervention interval length and tool life/wear become critical decision-making factors for scheduling and cost evaluation and for reducing the risks of unwanted severe damage on the cutters.

Tool Wear Evaluation Based on Design Space Exploration Coupled with Explicit Cutting Simulations and Cutting Forces Excitation Signature

Cutting operations are still one of the main methods used in the industry for surface generation in mass production. The dynamics of these processes are complex and having a good insight into the interdependencies of the nonlinear physical phenomena can be translated into better cutting performance. One of the main cutting tool geometrical parameter, usually associated with wear, is the cutting edge radius. Continuous direct evaluation of this parameter exhibits some important limitations offline, if this evaluation needs to be online the whole process becomes very complicated due to the measurement limitations that might appear. For online cases, the best approach is to determine the amplitude of this parameter indirectly using various side effects that can be correlated. One possible option is to monitor the vibrations generated by the resulting cutting forces. In the latest years, the usage of explicit finite element methods (FEM) to simulate the cutting processes has grown exponentially with the continuous increase of computation efficiency. With the help of Computer-Aided Engineering (CAE in short) solutions and using the latest advances in design space exploration (DSE) solutions, it is possible to create models able to parametrically explore a design space (DS), having precise targets, and also get the important correlations for all the important, quantifiable, cutting parameters. This paper presents an innovative method to create tool wear evaluation models focused mainly on the cutting edge radius indirect evaluation. The proposed method uses guided simulation loops able to generate a variety of dynamic signatures which are further post-processed to get a complex correlative model. The model can be applied in real cutting cases using the reciprocity property and can determine online the state of the cutting edge radius for further tool wear evaluation. The paper concludes with an analysis of the obtained model and the applicability of the data for the intended purpose.

Comparative Evaluation of Surface Quality, Tool Wear, and Specific Cutting Energy for Wiper and Conventional Carbide Inserts in Hard Turning of AISI 4340 Alloy Steel.

This paper presents an experimental study into the comparative response of wiper and round-nose conventional carbide inserts coated with TiCN + AL2O3 + TiN when turning an AISI 4340 steel alloy. The optimal process parameters, as identified by pre-experiments, were used for both types of inserts to determine the machined surface quality, tool wear, and specific cutting energy for different cutting lengths. The wiper inserts provided a substantial improvement in the attainable surface quality compared with the results obtained using conventional inserts under optimal cutting conditions for the entire range of the machined lengths. In addition, the conventional inserts showed a dramatic increase in roughness with an increased length of the cut, while the wiper inserts showed only a minor increase for the same length of cut. A scanning electron microscope was used to examine the wear for both types of inserts. Conventional inserts showed higher trends for both the average and maximum flank wear with cutting length compared to the wiper inserts, except for lengths of 200–400 mm, where conventional inserts showed less average flank wear. A higher accumulation of deposited chips was observed on the flank face of the wiper inserts than the conventional inserts. The experimental results demonstrated that edge chipping was the chief tool wear mechanism on the rake face for both types of insert, with more edge chipping observed in the case of the conventional inserts than the wiper inserts, with negligible evidence of crater wear in either case. The wiper inserts were shown to have a higher specific cutting energy than those detected with conventional inserts. This was attributed to (i) the irregular nose feature of the wiper inserts differing from the simpler round nose geometry of the conventional inserts and (ii) a higher tendency of chip accumulation on the wiper inserts.

Evaluation of tool wear and cutting performance considering effects of dynamic nodes movement based on FEM simulation

An accurate estimation of tool wear morphology can provide the opportunity to investigate the influence of tool wear on cutting performance as well as reduce the overall production cost. However, tool wear prediction is still a very challenging research issue. In this paper, a novel method for simulating the actual chip formation and wear evolution thorough the 3D finite element model has been carried out. In order to improve the accuracy of simulation results, the influence of worn tool, stress and temperature distribution on wear rate are considered. Then cutting experiment has been conducted by turning AISI1045 with uncoated carbide tools to validate the accuracy of the proposed model. The comparison between experimental and simulation results show good agreement which proves the ability of the proposed model in forecasting the tool wear. The validated finite element model has been further utilized studying how the worn tool affects the cutting performance including actual cutting rake, stress distribution, cutting force and temperature. The results of this paper not only provide a clear understanding of wear evolution between tool rake face and chip, but also are meaningful to optimize tool design and cutting parameters.