Tool Wear Progression Research Articles

An innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account

Tool wear is critically important for the optimization of cutting parameters. However, the increasing nature of tool wear presents challenges to traditional meta-heuristic cutting parameter optimization methods. To address this issue, we propose an innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account. More specifically, we use the Markov Decision Process to simulate the optimization process of cutting parameters. Firstly, an innovative deep transfer learning algorithm is used for monitoring tool wear. With the progress of tool wear, the proximal policy optimization method of the transformer with multi-head attention mechanism interacts with the processing environment through a process of trial and error, and accumulates a wealth of experience in selecting cutting parameters through the reward function. The deep reinforcement learning model has quickly discern the best cutting parameters, relying on real-time tool wear value. The experimental results show that the proposed method outperforms other algorithms.

Investigations on the machinability performance of Al2O3/TiCN CVD coated carbide tools in sustainable high-speed hard-turning of AISI 4340 alloy steel

Abstract This study investigates the effectiveness of CVD coated carbide inserts in turning hardened AISI 4340 steel at high-machining speed V=300,350m/min under sustainable dry cutting environment. Experiments were executed in accordance to Taguchi L4 orthogonal array and crucial machinability aspects tool life, tool wear progression and mechanism, cutting force, and surface roughness were evaluated in detail. The results of this study revealed that Al2O3/TiCN coating successfully inhibited the faster progression of tool wear at low cutting speed (300m/min), feed rate (0.05mm/rev) and depth of cut (0.1mm), thus delivering the highest tool life of 19.25 min and lowest cutting force of 76.8 N. However, better surface finish of 0.305µm was obtained at high cutting speed (350m/min), low feed rate (0.05mm/rev), and high depth of cut (0.1mm). Nevertheless, at high cutting speed (350 m/min) and feed rate (0.1 mm/rev), involvement of intense thermal-mechanical effects suppressed the effectiveness of coating and tool completed its useful life at 9.24 min. Severe flaking on the cutting edge was observed at high machining parameters. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis revealed that adhesion, oxidation, and small scale chipping and abrasion were the primary wear mechanisms attributing in competing the tool life criteria (Vb=300µm) in all cutting conditions. Furthermore, it was established that the cutting force and surface roughness increased with increasing tool flank wear due to coating delamination, material adhesion and tool chipping. This research emphasizes the significant potential of adopting Al2O3/TiCN coated carbide inserts for high-speed industrial hard-turning of AISI 4340 steel.

Tool Wear Monitoring In Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter

In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.

Effects of chip breaker groove and tool nose radius on progressive tool wear and behavior when turning GH3536 nickel-based superalloys

Nickel-based superalloys are typically challenging to machine and often exhibit severe tool wear during the turning process, thus attracting substantial research attention. This study not only explores the tool wear mechanisms when turning GH3536 nickel-based superalloys but also introduces a focus on the role of tool geometry configuration. Tools with different chip breaker groove structures and nose radii were investigated, with results indicating the tool wear mechanisms were predominantly abrasive and adhesive. For the 04HA chip breaker, which has a 9° rake angle and a "V"-shaped bottom, there is a single point of impact with the chip during cutting, while the 04VP3 chip breaker, with a 15° rake angle and a flat bottom, has two points of impact with the chip during cutting, increasing chip deformation and favoring chip breaking, thereby suppressing the formation of built-up edge. Additionally, changes in the nose radius influenced the degree of work hardening and consequently influence the severity of notch wear, with the nose radius also impacting the chip flow angle. Based on this finding, a theoretical mathematical model describing the initial chip flow angle was established and validated. Additionally, an examination of the effects of varying operating parameters on tool life was conducted, revealing that cutting speed was the most significant factor affecting tool life, followed by feed rate and depth of cut.

Milling Tool Wear Monitoring via the Multichannel Cutting Force Coefficients

Tool wear monitoring (TWM) is of great importance for improving the machining quality and the efficiency of the milling process. Extracting a discriminative tool wear feature is the key to TWM. Cutting force coefficients, which reflect the tool–chip and tool–material contact form, are good indicators of tool wear condition. However, in the existing studies, only the tangential and radial cutting force coefficients are adopted to monitor tool wear. The axial coefficients extracted from the axial cutting force are neglected. Preliminary experiments have shown that, although the axial cutting force has a small amplitude, the axial cutting force coefficients are very discriminative regarding the tool wear condition. Fusing the axial coefficients and the traditional tangential and radial coefficients can improve the monitoring accuracy. Based on such a consideration, this study proposes a milling tool wear monitoring method in which the multichannel cutting force coefficients, viz., the tangential, radial, and axial cutting force coefficients, are fused to indicate the tool wear. A long short-term memory (LSTM) network is adopted to sequentially estimate the progressive tool wear value from the multichannel cutting force coefficients. The effectiveness of the proposed monitoring method is examined using the PHM 2010 data. The results show that the proposed method outperforms the traditional method. With the fusion of the multichannel coefficients, the monitoring accuracy improves by 2.74–6.35%.

Tool Wear Monitoring System Using Seq2Seq

The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R2, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.

Cutting process in non-step drilling of deep hole with tool wear progress

Cutting process in non-step drilling of deep hole with tool wear progress

A New Architecture Paradigm for Tool Wear Prediction during AISI 9840 Drilling Operation

In conventional machining processes based on the chip removal mechanism, the progressive wear of the tool determines a change of the geometric characteristics of the cutting edge. Tool wear is a complex phenomenon and related tool life depends on several factors, such as cutting parameters, lubrication, and tool-workpiece relative trajectories. Tool wear progression affects the quality of the machined parts, making the tool replacement necessary even before its breakage. Moreover, in industrial practice, tool replacement cannot depend on the subjectivity of the operator, thus, the definition of an optimized strategy for cutting tool wear monitoring before tool failure is mandatory. This work compares Random Forest and Neural Network models for predicting tool wear in drilling. For the development of predictive models, tool life tests were performed by drilling through holes on AISI 9840 steel parts, with a coated tungsten carbide drill of 8 mm of diameter and by using constant cutting parameters. Flank wear of the tool was monitored. A set of statistical features computed from the vibration signals, the acoustic emission signals, the power signals and the torque signals constitute the input of the algorithms. The classification accuracy was 88% and 91% for Neural Network and Random Forest models respectively. In order to correctly define the tool replacement policy during production, the developed Random Forest model has been implemented in an industry, through production management software, achieving promising results.

Chip Morphology Prediction in Inconel 718 Milling through Machine Learning to Control Surface Integrity

A nickel-based aerospace superalloy, Inconel 718 presents machining challenges because of its hardness and strength. Monitoring and predicting chip morphology during milling is essential for early defect detection and process optimisation. This study examines the correlation between sensor signals with surface roughness and chip morphology in milling Inconel 718 using machine learning (ML). Due to progressive tool wear and heat generation, the surface roughness varies in addition to the chip exhibiting different morphologies, such as continuous, discontinuous, and oxidised chips. AE signals were analysed in the time and frequency domains to identify chip morphology transitions. An accelerometer captured cutting vibration signals that showed higher instability during discontinuous chip formation. Chip colour due to oxidization varies with milling forces as a result of tool wear. Based on multiple sensor data fusion, a random forest model predicts better chip morphology from different machining parameters. The integrated ML system enables real-time monitoring of chip morphology mechanisms through diverse signals. This permits early diagnosis of surface integrity and chip morphologies indicating imminent tool wear. The approach enhances process stability and tool life when milling difficult-to-machine alloys. It demonstrates the viability of relating sensor signals to fundamental mechanisms through AI for intelligent machining.

Tool wear prediction in edge trimming of MD CFRP considering interlaminar effect

The tool flank wear progresses rapidly in edge trimming of carbon fiber reinforced polymer (CFRP) components, affecting the machined surface quality and process efficiency. This paper presents a tool wear prediction method considering the unique interlaminar effect of the multi-directional (MD) CFRP in the edge trimming process. The experimental results show that different laminar configurations of MD CFRP change the tool wear progression of unidirectional (UD) plies at specific fiber orientations. The interlaminar effects from the adjacent layers with different fiber orientations are explained by their varying supporting strengths on the target layer due to the change of bouncing back height. Therefore, the interlaminar effect from two sides of a UD ply is not a simple summation of the contributions by the individual side. The machined surface roughness of unidirectional ply varies up to 30 % due to the interlaminar effect. A long short-term memory (LSTM) - back propagation (BP) network is developed to predict tool wear length, including the effect of different interlaminar configurations. With the proposed model, the effect of the interlaminar configurations on tool wear progression in edge trimming of MD CFRP is predicted quantitatively. The prediction of the wear length progression based on the developed machine learning model is validated by the experimental results.

Quality Evaluation and Analysis of Force Signals in CFRP Helical Milling

Nowadays, drilling is one of the most widespread operations due to the need to obtain holes for mechanical joints. This operation is particularly relevant in the aeronautical sector due to the high number of operations performed, the high demands and the use of materials that are difficult to machine. The use of materials that enhance aircraft performance, such as carbon fiber composites (CFRP), poses a challenge for axial drilling operations. The characteristics of these materials and typical defects such as delamination are difficult to control due to the axial forces produced during drilling operations. Therefore, more efficient alternatives are required. In this context, helical milling operations have advantages such as lower axial stress, higher flexibility, and higher efficiency in heat and chip evacuation that put it in the spotlight. In this work, research has been carried out in which helical milling operations have been performed on CFRP. The main objectives were the analysis of surface quality, delamination, tool wear and force analysis. For this purpose, a working methodology has been developed combining machining parameters that define the kinematics of the cutting process (cutting speed, axial feed rate, and tangential feed rate). Finally, this information has allowed finding a correlation between quality indicators such as delamination and the forces generated during the cutting process and associated with progressive tool wear. The results show that the force signal could be used for on-line monitoring of the machining process.

Characterization of Formed TRIP1180 Steel Sheet Surface After Stamping with PVD-Coated Tools

The wear tests are conducted on bending punches deposited with PVD CrN and AlTiCrN coatings using the newly proposed progressive die. Then, the surface quality of the formed product is characterized through the surface roughness measurement after forming of TRIP1180 steel sheets. The correlation between the tool wear, in terms of wear depth and roughness and the product surface roughness can be quantitatively analyzed. The results show that the roughness remains comparable to that of the as-received surface before failure occurs, which represents smooth product surface without severe scratches and defects. While micro scratches on the punch surface have no effect on the quality of the product surface, severe fretting wear on the punch surface leads to a deterioration in the surface quality. Once initiated in the stamping process, the wear progresses exponentially within short time. The wear is also characterized as less than the coating thickness, but it results in complete removal of the coating layer. The partially worn punch plows the product surface, causing surface scratches with grooves and ridges, resulting in the roughness of 1.0 μm. In contrast, the surface with completely damaged coatings is extremely rough, with the roughness of 2.0 μm. This study presents the efficient method to evaluate the tool wear progression by indirectly measuring the product surface quality with reliably high precision.Graphical abstract

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.

Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions

Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions will cause fluctuations in the signal amplitude and thus generate false alarms. This study introduced a novel method for tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions. The amplitude ratio (AR) and the energy ratio (ER) were proposed according to the power spectrum of the spindle vibration signal, which represents the change of amplitude and the energy distribution, respectively. The AR and ER are normalized and integrated into a unified indicator for real-time breakage monitoring. The floating monitoring threshold is designed based on the Gaussian distribution. Moreover, the material removal rate (MRR) is selected as a secondary indicator to accurately identify tool breakage based on determining the amplitude fluctuation caused by cutting conditions or teeth breakage. The effectiveness of the proposed method for tool breakage monitoring has been verified under the constant, time-varying, and entry/exit cutting conditions. The results show that the proposed indicators have higher sensitivity than the traditional root mean square (RMS) features and eliminate false alarms during condition change transients. This research provides a potential solution for tool breakage monitoring under complex cutting conditions.

Energy balance model to predict the critical edge radius for adhesion formation with tool wear during micro-milling

Cutting edge of the micro-milling tool rapidly loses its sharpness and experiences early adhesion that degrade the machinability and surface quality. Although rapid edge-rounding and early adhesion were reported in the literature, the condition that promotes the adhesion development was not established. For dry machining of Ti-6Al-4V with TiAlN-coated WC-6Co micro-mills, an energy balance model is proposed in this article that guides the condition for the onset of adhesion with progressive tool wear. Initially, tool wear progression through successive stages of rapid abrasion, steady abrasion, adhesion, and edge-chipping is experimentally characterised. Adhesion at the tool-tip is found to develop when the abrasively worn-out edge radius reaches a critical value. Thereafter, a model is presented considering the dynamic growth of the dead metal zone (DMZ) with tool wear to capture the changes in the tribological contact and stress distribution at the interfaces. The chip-DMZ contact is the crucial factor in governing the critical edge radius for adhesion initiation, while the chip-tool contact has no direct role. Adhesion occurs when worn-out edge radius reaches 3.98–4.26 μm during micro-milling at a low feed of 1.0 μm/flute, whereas 4.0 μm/flute feed permits adhesion-free cutting up to 5.02–5.32 μm edge radius. Higher speed can favourably retard adhesion development. The inherently occurring phenomena like chip accumulation and non-cutting passes in low-feed micro-milling can favourably extend adhesion-free machining. However, coating delamination promotes adherence at an early stage in high-feed machining.

The correlation of surface roughness and tool edge condition under sustainable cryogenic machining

This paper investigates the correlation between surface roughness of Inconel 718 and tool edge condition of ball nose inserts when milled at high speed. The cutting parameters were varied as follows; cutting speed: 120–140 m/min, feed rate: 0.15–0.25 mm/tooth, and axial depth of cut: 0.3–0.7 mm. For a sustainable machining approach, the experimental works were carried out under a smooth supply of cryogenic coolant which is a mix of liquid CO2, gas CO2, and compressed air. The experimental results revealed that the range of surface roughness obtained is from 0.114 to 0.197 µm. Along the cutting process, the tool wear patterns such as the abrasion, chipping, and the intermittent build-up-edge near the depth of cut cause the rapid increase of tool wear as well as the roughness of the machined surface with a significant correlation between them. However, the roughness was slowly reduced and became stable with the increase of notch wear. The finding could be used as a prediction reference for monitoring surface roughness and tool wear progress under cryogenic conditions. It also provides foundations for further research on machinability under this sustainable approach.

Study on ploughing phenomena in tool flank face – workpiece interface including tool wear effect during ball-end milling

Ploughing phenomena occurring during precise machining processes affect the formation of surface finish and progress of tool wear. Therefore, this study presents an evaluation of ploughing phenomenon by studying the ploughing forces in tool flank face – workpiece interface during precise ball-end milling of AISI L6 alloy steel. Developed original model of ploughing forces involved the effect of minimum uncut chip thickness hmin and ploughing volume during ball-end milling. Estimated ploughing forces are sensitive to variations of surface inclinations and progressing tool wear. Moreover, intense growth of ploughing force during milling with worn tool is affected by irregular and non-circular profile of worn cutting edge below the stagnant point, and by the appearance of attrition and micro-grooves on tool flank face.

Study on the effect of wear models in tool wear simulation using hybrid SPH-FEM method

The prediction of the tool wear progression using numerical methods has been widely studied in recent years, and various wear models considering different wear mechanisms have been implemented in the simulation work. Typically, these wear models take the physical fields such as contact pressure, sliding velocity and temperature at the tool-workpiece interface into account and are applied either individually or in combinations into the wear simulation. However, how and to what extent the physical parameters in these models affect the generated wear profiles have not been explored in detail in the simulation. In this paper, the behaviors of several typical wear models are studied by simulating the tool wear of cutting Ti6Al4V using a hybrid SPH-FEM method. Considering different combinations of physical contact parameters in the wear model, the simulated wear progression is discussed, and the resulting worn tool geometry is qualitatively compared to the experimental result. Furthermore, insights into the calibration of wear models are proposed.

Tool life prediction in end milling using a combination of machining simulation and tool wear progress data

Tool life prediction in end milling using a combination of machining simulation and tool wear progress data

Material Defects in Friction Stir Welding through Thermo–Mechanical Simulation: Dissimilar Materials with Tool Wear Consideration

Despite the remarkable capabilities of friction stir welding (FSW) in joining dissimilar materials, the numerical simulation of FSW is predominantly limited to the joining of similar materials. The material mixing and defects' prediction in FSW of dissimilar materials through numerical simulation have not been thoroughly studied. The role of progressive tool wear is another aspect of practical importance that has not received due consideration in numerical simulation. As such, we contribute to the body of knowledge with a numerical study of FSW of dissimilar materials in the context of defect prediction and tool wear. We numerically simulated material mixing and defects (surface and subsurface tunnel, exit hole, and flash formation) using a coupled Eulerian-Lagrangian approach. The model predictions are validated with the experimental results on FSW of the candidate pair AA6061 and AZ31B. The influence of tool wear on tool dimensions is experimentally investigated for several sets of tool rotations and traverse speeds and incorporated in the numerical simulation to predict the weld defects. The developed model successfully predicted subsurface tunnel defects, surface tunnels, excessive flash formations, and exit holes with a maximum deviation of 1.2 mm. The simulation revealed the substantial impact of the plate position, on either the advancing or retreating side, on the defect formation; for instance, when AZ31B was placed on the AS, the surface tunnel reached about 50% of the workpiece thickness. The numerical model successfully captured defect formation due to the wear-induced changes in tool dimensions, e.g., the pin length decreased up to 30% after welding at higher tool rotations and traverse speeds, leading to surface tunnel defects.