Tool Wear In Machining Research Articles

Enhancing Machining Efficiency: Real-Time Monitoring of Tool Wear with Acoustic Emission and STFT Techniques

Tool wear in machining is inevitable, and determining the precise moment to change the tool is challenging, as the tool transitions from the steady wear phase to the rapid wear phase, where wear accelerates significantly. If the tool is not replaced correctly, it can result in poor machining performance. On the other hand, changing the tool too early can lead to unnecessary downtime and increased tooling costs. This makes it critical to closely monitor tool wear and utilize predictive maintenance strategies, such as tool condition monitoring systems, to optimize tool life and maintain machining efficiency. Acoustic emission (AE) is a widely used technique for indirect monitoring. This study investigated the use of Short-Time Fourier Transform (STFT) for real-time monitoring of tool wear in machining AISI 4340 steel using carbide tools. The research aimed to identify specific wear mechanisms, such as abrasive and adhesive ones, through AE signals, providing deeper insights into the temporal evolution of these phenomena. Machining tests were conducted at various cutting speeds, feed rates, and depths of cut, utilizing uncoated and AlCrN-coated carbide tools. AE signals were acquired and analyzed using STFT to isolate wear-related signals from those associated with material deformation. The results showed that STFT effectively identified key frequencies related to wear, such as abrasive between 200 and 1000 kHz and crack propagation between 350 and 550 kHz, enabling a precise characterization of wear mechanisms. Comparative analysis of uncoated and coated tools revealed that AlCrN coatings reduced tool wear extending tool life, demonstrating superior performance in severe cutting conditions. The findings highlight the potential of STFT as a robust tool for monitoring tool wear in machining operations, offering valuable information to optimize tool maintenance and enhance machining efficiency.

Influence of Tool Wear and Workpiece Diameter on Surface Quality and Prediction of Surface Roughness in Turning

In turning, tool wear and cutting vibration are inevitable, which have great influence on surface quality. Analyzing the influence mechanism of tool wear and cutting vibration on surface quality is important to achieve the accurate prediction of surface roughness before machining and improve machining quality. In this paper, a turning vibration experiment is conducted to reveal that the diameter of shafts is an important factor affecting the vibration amplitude and frequency. In addition, based on machining parameters, tool wear and workpiece diameter, this empirical model, the response surface method and a support vector machine are used to model and predict surface roughness. The fitting accuracy, prediction accuracy and generalization performance of the proposed methods are compared in detail. The results show that the response surface modeling method has the highest fitting accuracy, but the exponential empirical modeling method has the highest prediction accuracy and best generalization performance. Additionally, the prediction results indicate that the surface roughness increases with the increase in tool wear and decreases with the increase in workpiece diameter.

Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO2-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity vc and feed rate fz are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (vc = 60.00 m/min, fz = 0.08 mm/z, ap = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

Effects of magnetic intensity on the machining quality and tool damage in nickel-based superalloys subjected to magnetic-assisted cutting

Nickel-based superalloys are widely used in engine components due to their excellent high-temperature strength. However, their low plasticity and low thermal conductivity pose significant challenges in machining, leading to poor quality of machined parts and severe tool wear. Magnetic-assisted machining has received considerable attention as a clean and effective machining method, but its application to nickel-based superalloys is rarely reported, necessitating further exploration of its potential. This study focuses on GH4169 alloy and explores the effects of magnetic-assisted cutting (MAC) with varying intensities on the machining quality and tool wear. The experimental results indicate that the introduction of a magnetic field improves the machining quality by reducing tool wear and built-up edge adhesion. Additionally, both the plasticity and thermal conductivity of GH4169 alloy are improved under the effect of magnetic field, resulting in an 11.4 % decrease in cutting temperature. However, when the magnetic intensity ranges from 0.03 T to 0.12 T, secondary serration of the chip edges manifests, resulting in notch wear on the tool nose and a deterioration of surface quality. This secondary serration is attributed to the combined effect of tool nose radius and material plasticity under the magnetic field. As the magnetic field intensity increases further, the secondary serration gradually diminishes, and the surface roughness can be reduced by 26.5 % compared to traditional cutting. This study analyzed the relationship between machined surface, tool wear, chip morphology and magnetic field, and revealed the reasons for the differences in machining quality at different magnetic field intensities. These findings provide more possibilities for future applications of magnetic-assisted cutting to improve the machining quality of nickel-based superalloys and other difficult-to-machine metals.

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

The utilization of flexible job shops is on the rise, driven by the decreasing life cycle of consumer products. In dynamic flexible job shops, the occurrence of disruptive events like machine breakdown, tool wear, and new job arrivals is common, emphasizing the critical need for efficient scheduling to achieve productivity and cost-effectiveness. Additionally with the growing concerns about energy consumption and global warming, the imperative to contemplate workshops with lower energy usage is becoming increasingly significant. Therefore, in this study a multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with insertion of new jobs for optimization objectives: makespan, total energy consumption and schedule instability is addressed. A novel multi-objective black widow spider algorithm (MOBWSA), inspired by the mating behavior of black widow spiders, is proposed. The MOBWSA ensures an optimal balance between minimizing makespan and energy consumption through its superior evolutionary processes of procreation, mutation and cannibalism. Furthermore, a strategic on/off strategy is implemented to enhance energy efficiency. The disruptions in schedule at rescheduling stage are addressed through the proposed sigmoidal weighted instability function. To handle the multi-objective nature of the problem this work incorporates Pareto-optimality approach in combination with novel hybrid crowding distance metric. The optimization is further aided by exploitation of search space through an ensemble of local search operators. Testing and evaluation of the MOBWSA with 30 benchmark problems is conducted, and its performance for multiple metrics is compared with recently published state-of-the-art algorithms. The results establish the effectiveness and efficiency of the proposed algorithm.

Extendable machine tool wear monitoring process using image segmentation based deep learning model and automatic detection of depth of cut line

Automating the monitoring of machine tools poses a significant challenge, with previous studies relying on machine vision to address this issue, primarily focusing on measuring specific tool wear using custom algorithms. In this study, we introduce an automated tool wear monitoring process capable of assessing wear across various tools. Utilizing deep neural network models for image classification and segmentation, our proposed process effectively masks areas where tool wear occurs. The image classification model selects the best-performing backbone model based on the highest F1 score. To account for varying depths-of-cut lines among different tools in the masked area, we introduce an algorithm that utilizes the Hough transform to determine the horizontal angle with the cut line. By systematically measuring the maximum flank wear of diverse tools, including indexable, ball, and solid endmills, using this method, we can replace conventional manual processes. This approach can be extended and automated for various machine tools, offering substantial potential for enhancing manufacturing tool monitoring processes.

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

This study presents an approach that combines variational mode decomposition (VMD) and relevance vector machine (RVM) as a prediction technique for surface roughness during turning. The method helps machinists detect irregularities during machining, such as looseness in machine parts or tool wear. The study uses work hardened EN8 steel and extracts vibration signal features into five modes. PSO optimized RVM prediction models are developed using these modes with and without combining cutting parameters. The most sensitive mode is selected for accurate surface roughness prediction. The results show that the first mode of decomposition when combined with cutting parameters provides the least mean square error of 0.1095. Surface roughness was found to be primarily influenced inversly by cutting speed and directly by feed rate respectively. The signal with high surface roughness value has high amplitude noise scattered over the large frequency range. The first decomposed mode of vibration signal increases noise with a large amplitude as surface roughness increases. The approach can be integrated into a microcontroller to regulate machine settings based on vibration data.

Optimization of surface roughness and tool wear in laser-assisted machining of alumina ceramics using Taguchi method

Optimization of surface roughness and tool wear in laser-assisted machining of alumina ceramics using Taguchi method

Effect of the Built-Up-Edge on Tool Wear in Machining of STAVAX

Effect of the Built-Up-Edge on Tool Wear in Machining of STAVAX

Tool wear and surface roughness characteristics in sustainable machining of additively manufactured titanium alloys

Titanium is widely acknowledged as a challenging metal due to its inherent characteristics. Manufacturers often encounter challenges such as tool wear, reduced tool lifecycle, and increased cutting heat when working with Ti64. This research investigates the cutting characteristics of additively manufactured (AMed) Ti64 under dry cutting, Minimum Quantity Lubrication (MQL), Cryogenic Carbon dioxide (CO2), and a hybrid approach combining MQL and CO2 conditions. Surface roughness, flank wear, temperature, chip morphology, and microhardness were analyzed to assess the impact of cooling strategies. Results indicate that the hybrid approach outperforms individual methods, showing superior surface finish and reduced tool wear. Machined Surface roughness (Ra) measurements reveal a substantial improvement in the hybrid condition, reducing Ra values by 62.44–67.02%, 35.65–41.38%, and 18.68–27.59% compared to dry, MQL, and CO2. Tool wear assessments exhibit significantly lower flank wear values in the hybrid condition, emphasizing the synergistic benefits of lubrication and cryogenic cooling. This research provides valuable insights into tailoring cooling strategies for optimal precision in machining AMed-Ti64 material, which is crucial for achieving high-quality manufacturing outcomes.

Boundary fluid constraints during electrochemical jet machining of large size emerging titanium alloy aerospace parts in gas–liquid flows: Experimental and numerical simulation

Large size titanium alloy parts are widely used in aerospace. However, they are difficult to manufacture using mechanical cutting technology because of severe tool wear. Electrochemical jet machining is a promising technology to achieve high efficiency, because it has high machining flexibility and no machining tool wear. However, reports on the macro electrochemical jet machining of large size titanium alloy parts are very scarce, because it is difficult to achieve effective constraint of the flow field in macro electrochemical jet machining. In addition, titanium alloy is very sensitive to fluctuation of the flow field, and a turbulent flow field would lead to serious stray corrosion. This paper reports a series of investigations of the electrochemical jet machining of titanium alloy parts. Based on the flow analysis and experiments, the machining flow field was effectively constrained. TB6 titanium alloy part with a perimeter of one meter was machined. The machined surface was smooth with no obvious machining defects. The machining process was particularly stable with no obvious spark discharge. The research provides a reference for the application of electrochemical jet machining technology to achieve large allowance material removal in the machining of large titanium alloy parts.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications, especially in the fields of automotive and aerospace. They undergo extensive cutting or machining while making products out of them. Dry cutting, a sustainable machining method, causes more friction and adhesion at the tool-chip interface. One of the promising solutions to this problem is cutting tool surface texturing, which can reduce tool wear and friction in dry cutting and improve machining performance. This paper aims to investigate the impact of dimple textures (made on the flank face of cutting inserts) on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy. The results show that the cutting speed was the most significant factor affecting tool flank wear, followed by feed rate and cutting depth. The tool wear mechanism was examined using scanning electron microscope (SEM) images and energy dispersive X-ray spectroscopy (EDS) analysis reports, which showed that at low cutting speed, the main wear mechanism was abrasion, while at high speed, it was adhesion. The chips are discontinuous at low cutting speeds, while continuous at high cutting speeds. The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Uncertainties of Surface Roughness and Tool Wear in Machining of AISI 4140 Alloy Steel

Uncertainty is the probability of statistical dispersion of a quantity from the desired value. Every manufacturing process has some types of uncertainties. In this investigation, individual, combined and expanded uncertainties of surface roughness and tool wear in machining of AISI 4140 alloy steel are quantified and compared. First, the individual and combined uncertainty of two responses namely tool wear, and surface roughness are measured using a deterministic method called the law of propagation of uncertainties (LPUs), and the principal contributors to uncertainties are identified. This method produces uncertainty of surface roughness as 0.1162[Formula: see text][Formula: see text]m and that of tool wear as 7.4036[Formula: see text][Formula: see text]m. It is further observed that cutting speed contributes 77.946% and 57.4853% on overall uncertainties for surface roughness and tool wear respectively. A stochastic method called Monte Carlo simulation (MCS) technique is further employed using 100,000 no. of iterations to compute the uncertainties in surface roughness and tool wear. This method produces uncertainties as 0.0581[Formula: see text][Formula: see text]m and 3.687[Formula: see text][Formula: see text]m in surface roughness and tool wear respectively. The results obtained from the deterministic model (LPU) and the stochastic model (MCS) are compared and it is observed that the MCS method is more reliable and accurate than the LPU method.

Hybrid physics data-driven model-based fusion framework for machining tool wear prediction

Hybrid physics data-driven model-based fusion framework for machining tool wear prediction

Research on prediction method of tool wear degree based on SVM

Tool wear of machine tools not only directly affects the processing quality, but also leads to the life of processing equipment and production costs. It is of great significance to correctly identify, classify and predict the state of tool wear. In this paper, through the collection of machine tool operation data, using machine learning modeling, using the model to identify the tool wear state, and then predict and classify the tool wear state, using the classification results to determine whether the tool can continue to use. After simulation verification, the results show that the model can identify and predict the wear state more realistically, and has strong practicability.

Study on wear of diamond during scratching SiC using molecular dynamics simulations

In this work, molecular dynamics (MD) simulations have been performed to investigate the wear mechanisms of diamond probe scratching 3C-SiC substrate. The number of worn atoms, the number of atoms in contact with the substrate, the friction force and normal force all increased significantly with the increase of the scratching depth. It is also found that as the scratching depth increased, the high stress region gradually shifted from the bottom to the rear end of the probe, indicating the risk of probe failure changing from gradual wear to fracture. Moreover, the SiC substrate undergone high temperature, high compressive stress and structural transformation in the wear process. We also found that only single atoms or atomic clusters are detached from the tip under the attractive interaction, indicating the occurrence of atomic attrition. The insights gained can shed light on the wear mechanism of diamond tool in machining SiC and provide a theoretical reference for controlling diamond tool wear in ultra-precision machining.

Enhancing the borosilicate glass micromachining by using mixed alkaline electrolytes in the ECDM process

ABSTRACT This article investigates the effects of mixing different alkaline electrolytes on the geometrical features created by the ECDM. Sodium hydroxide (NaOH) and potassium hydroxide (KOH) were mixed in varying volumetric proportions. Effect of critical process parameters on the depth and width of the microchannels and the associated tool wear was investigated. Pure NaOH electrolyte has resulted in the highest depth compared to the pure KOH electrolyte. Process mechanism behind the role of mixed electrolytes on the opening sizes and depths of microchannels is explained. Higher material removal by the NaOH electrolyte was due to the larger number of sodium (Na+) ions in the electrolyte, resulting in more aggressive electrochemical discharge; however, it also resulted in severe tool wear. Mixed electrolytes with a KOH yielded lower width and low tool wear. Increasing the volumetric proportion of KOH in the mixed electrolyte gradually decreased the machining depth, width, and tool wear.

In Situ Measurement of the Machining State in Small-Diameter Drilling by Acoustic Emission Sensing

In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or the adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting high-frequency AE signals at about 1 MHz, caused by the adhesion of the workpiece material. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested.

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.