Tool Breakage Research Articles

Research on tool wear and breakage state recognition of heavy milling 508III steel based on ResNet-CBAM

Milling water chamber head material 508 III steel belongs to extreme manufacturing, and the tool failure is very serious in the process of machining. How to monitor and identify the tool wear damage state in time and effectively is a key problem to be solved urgently. Identifying chip morphology changes under machining conditions plays a very important role in characterizing tool wear and breakage. Tool wear has an important influence on the chip morphology during the process of milling 508III steel, and changes in chip morphology can also reflect the tool wear and breakage state. Therefore, this paper takes the chip morphology image as an important feature for tool wear and breakage recognition, and uses Gaussian fuzzy estimation morphology method to preprocess the chip morphology image. Build a ResNet network combined by convolutional block attention module (ResNet-CBAM) tool wear and breakage recognition model, and explore the selection of the model backbone network, determination of the ResNet backbone network depth, and fusion methods of different attention mechanisms. Through the ablation experiment and visual analysis of the attention mechanism module, it is verified that the ResNet-CBAM model proposed in this paper has an accuracy rate of 96.67% in tool wear and breakage state recognition, especially in the stage of severe tool wear and breakage. This study realizes the prediction and early warning of tool life, and provides effective guarantee for the efficient cutting of large parts of high-end equipment and the stable operation of manufacturing system.

The Effects of Alloy Composition and Surface Integrity on the Machinability of Austenitic Stainless Steels 304 and 304L

Austenitic stainless steels, such as 304 and 304L, are extensively utilized in diverse industries due to their favorable properties, including biocompatibility, high durability, ductility, toughness at cryogenic temperatures, and excellent corrosion resistance. Additionally, these steels exhibit notable resistance to fatigue and oxidation. Despite these advantages, they are challenging to machine due to characteristics such as high work hardening, built-up edge formation, and low heat conductivity. The material 304L distinguishes itself from material 304 through its lower carbon content, making it more resistance to corrosion. 304L is experiencing a consistent rise in industrial demand. It is anticipated that this advanced material will progressively supersede 304 in various applications. The variability in alloy compositions and surface integrity of blanks can influence the tool wear and may even lead to abrupt tool breakage, necessitating supervision during machining operations. This study delves into the correlation between the alloy compositions, micro structure, surface integrity, and machinability of these special steels, focusing on turning processes. Various blanks of 304 and 304L in the form of bars, sourced from different manufacturers, were utilized in the study. These blanks exhibited slight variations in alloy composition (albeit within the standard range) and differed in the state of surface integrity characterized by variations in microstructure, grain size, microhardness, and residual stress. All blanks (across this array of materials) were subjected to turning using the same tool specifications and sets of machining parameters for comparative analysis. Various machinability indicators, including cutting forces, surface roughness, burr formation, tool wear, and chip morphology, were thoroughly examined. The findings highlight that the key factors influencing machinability include the microhardness of the surface and the residual stress state in the subsurface of the bars before the turning process. In contrast, changing the alloy composition within the standard range has hardly any effect on the machinability of these steels. The machinability of the examined specimens was adversely affected when the hardness exceeded 350 HV from the surface up to 2 mm below the surface and simultaneously the surface compressive residual stress exceeded −130 MPa.

Investigation on the material removal mechanism in ion implantation-assisted elliptical vibration cutting of hard and brittle material

Ductile-regime machining has been used to generate damage-free surface of hard and brittle materials by setting the cutting depth to be smaller than the ductile-brittle transition depth (DBTD). However, the ductile-regime cutting of sapphire remains challenging owing to its extreme hardness, small DBTD, serious surface fractures, and severe tool wear. To solve this problem, ion implantation-assisted elliptical vibration cutting (Ii-EVC) has been proposed in this study to enhance the machinability of hard and brittle materials. Taking sapphire as an example, high-energy phosphorus ions were implanted into the workpiece to modify its surface. Nanoindentation tests revealed that the modified materials undergo plastic and elastic deformation more easily due to the decrease in hardness and modulus. Compared with nanocutting without implantation assistance, the DBTD of implanted sapphire has been increased by more than five times. The advantageous effects of Ii-EVC achieve great enhancement in machinability, including surface fractures suppression, tool-wear reduction, chips morphology transformation from discontinuous to continuous, and cutting force decrease. Furthermore, even near the cracks in the brittle region after Ii-EVC, the subsurface microstructure showed a more complete lattice arrangement and a strain distribution close to zero, indicating that crack propagation was effectively suppressed. Due to the promoted localized plastic deformation, the stress distribution in the implanted material is much smaller than that in pristine workpiece. Implantation-induced defects not only serve as a core for absorbing external energy from the high-frequency vibration and improving the in-grain deformation but also facilitate the formation of shear bands. The interface with high distortion between the modified layer and substrate can effectively dissipate strain energy and hinder crack propagation to the free surface. The turning experiments verified that Ii-EVC can achieve better surface quality, less tool wear and higher optical transmittance. Overall, Ii-EVC addresses the challenges of tool breakage and surface fracture caused by high-frequency collision between tool and workpiece in traditional EVC, overcomes the problem of limited modification depth in ion implantation, and increases the ductile-regime removal depth of extremely hard and brittle materials to several microns. Such findings demonstrate that Ii-EVC is a promising method for the ultra-precision manufacturing of advanced materials.

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

EDM-Grinding composite machining and drilling of polycrystalline diamond tool

ABSTRACT Polycrystalline diamond (PCD) is widely used for micro tools due to its high hardness and wear resistance. However, micro PCD tools face challenges in machining with high accuracy and efficiency. Therefore, electrical discharge machining grinding (EDM-grinding) composite machining was proposed. The effects of pulse discharge parameters on surface quality and microhole drilling performance of PCD tool were investigated. The results show that the surface roughness of helix flute and flank face of the fabricated PCD tool were 0.125 µm and 0.158 µm, respectively, as well as the inner wall roughness achieved 0.331 µm after drilling of microhole. The average current displayed a greater effect on the machining quality of PCD tools, with tool breakage in excessive average current. The preferred pulse discharge parameters were determined as an open-circuit voltage of 95 V, an average current of 1400 mA, a pulse width of 400 ns, and a pulse interval of 2000 ns.

Experimental Analysis of Robotic Cortical Bone Specimen Drilling Performance: Effect of Cryogen.

In orthopedic surgery, precise bone screw insertion is crucial for stabilizing fractures, necessitating a preliminary cortical bone drilling procedure. However, this process can induce temperatures exceeding 70 °C due to the low thermal conductivity of cortical bone, potentially leading to thermal osteonecrosis. Furthermore, significant cutting forces and torque pose risks of tool breakage and bone damage, underlining the need for high precision and optimal processing parameters. Traditionally, drilling relies on the surgeon's experience and often results in imprecise outcomes due to inconsistent feed rates. Therefore, this study proposes the use of a 6-axis robot for controlled drilling, offering precise control over angular velocities and consistent feed rates. Additionally, explore the use of cryogenic liquid nitrogen (LN2) as a novel cooling method compared to conventional saline solutions, examining its efficacy under various cutting conditions. The results demonstrate that LN2 cooling conditions lead to a reduction in thrust and torque under specific processing conditions, and facilitate smoother chip evacuation. Additionally, LN2 significantly lowers the peak temperature around the drilling site, thus minimizing the risk of thermal osteonecrosis. Consequently, the use of a 6-axis robot provides consistent feed rates, and LN2 cooling achieves optimal processing conditions, enabling a more controlled and effective drilling process.

Predicting Factory Equipment Lifespan Through Manufacturing Data Analysis using AI

Recently, research on applying artificial intelligence (AI) to various industries, especially manufacturing, is being actively conducted. In the field of smart factory, the purpose is to improve productivity based on data generated in the process of producing or processing products. The tool breakage during metal product processing causes fatal difficulties of predicting tool life. Moreover, if tool life is not predicted, defects may occur product reliability deteriorate, which may adversely affect product performance or economic aspects. In this paper, data related to machining is collected from CNC equipment in real time, and through machine learning and deep learning, which factors affect the wear of cutting tools are identified and the lifespan of cutting tools is predicted. An AI-based solution was applied to the system, productivity improved due to an increase in tool life.

Stability of Micro-Milling Tool Considering Tool Breakage

Micro-milling, widely employed across various fields, faces significant challenges due to the small diameter and limited stiffness of its tools, making the process highly susceptible to cutting chatter and premature tool breakage. Ensuring stable and safe cutting processes necessitates the prediction of chatter by considering the tool breakage. Crucially, the modal parameters of the spindle–holder–tool system are important prerequisites for such stability prediction. In this paper, the FRFs of the micro-milling tool are calculated by direct frequency response functions (FRFs) of the micro-milling cutter and cross-FRFs between a point on the shank and one on the tool tip. Additionally, by utilizing a cutting force model specific to micro-milling, the bending stress experienced by the tool is computed, and the tool breakage curve is subsequently determined based on the material’s permissible maximum allowable stress. The FRFs of the micro-milling tool, alongside the tool breakage curve, are then integrated to generate the final stability lobe diagrams (SLDs). The effectiveness and reliability of the proposed methodology are confirmed through a comprehensive series of numerical and experimental validations.

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.

Study on the resistance coefficient of hot dry rock cuttings in Herschel-Bulkley fluid: experiments and modeling

Most hot dry rock geothermal wells are small angle directional wells, and rock cuttings easily accumulate at the bottom of the borehole to form a cuttings bed, causing accidents such as drill sticking, reducing the rate of penetration, and drilling tool breakage. Accurately calculating the resistance coefficient and settling velocity of hot dry rock cuttings can improve cuttings transportation efficiency, design and optimize drilling hydraulic parameters, and is crucial to solving borehole cleaning problems. Through visual experiments, this paper obtained experimental data on the settlement of 167 groups of spherical pellets, 153 groups of granite cuttings, and 174 groups of carbonate cuttings in the Herschel-Bulkley fluid. First, a prediction model for the resistance coefficient of spherical pellets consistent with Herschel-Bulkley fluid was established. Based on this, form factor-Roundness is introduced as the starting point, and two prediction models for the resistance coefficients of granite cuttings and carbonate cuttings in the Herschel-Bulkley fluid were established. The average relative errors between the resistance coefficient model predictions and experimental measurements are 9.61% for granite cuttings and 6.59% for carbonate cuttings. The average relative errors between the predicted and measured values of settlement velocity are 7.27% for granite cuttings and 6.21% for carbonate cuttings, respectively, which verifies the accuracy and reliability of the prediction model. The research results can provide a theoretical basis and engineering application guidance for optimizing drilling fluid rheology and circulation displacement in engineering.

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

Experimental bison butchery using replica hafted Clovis fluted points and large handheld flakes

Late Pleistocene Clovis fluted points and stone flake tools have been found in association with bison at several archaeological sites in western North America. To gain insight into how these tools may have functioned in bison butchery, as well as to assess the assumptions or interpretations of these implements by archaeologists, we conducted a bison-processing experiment. We examined the relative efficacy in the butchering process of hafted fluted points versus handheld flakes and of different Clovis point forms; the types of tool breakage that might occur during butchering; cut-marks that might occur on bones; and, the effects of edge resharpening, both in terms of cutting efficiency and in the before/after form of the points and flake tools used in the process. We also recorded several unanticipated observations on, but not limited to, field-processor injury, the function of fluted point proximal-lateral edge grinding, and tool loss during the butchery event. Overall, our experiment emphasizes the prevalence of equifinality in archaeological interpretation, as it shows that numerous aspects of the Clovis archaeological record – as it is currently understood – can be equally explained by several distinct processes.

Elucidation of tool wear phenomenon in FSW using silicon nitride tool

Friction stir welding (FSW) has been widely used in welding of metal. However, it is difficult to weld high melting point metal by FSW because of short life or high price of FSW tools. Silicon nitride tool can have both long life and low cost. Although the silicon nitride tool, which has high temperature mechanical properties, could have a long tool life for FSW of steel, it is not clear how FSW conditions affected the tool life. In this study, changes in the shape of the FSW tool was focused. A comparative verification of the tool shape changes due to the FSW conditions was carried out, and specific shape changes in silicon nitride tools were verified. The silicon nitride tool was worn only at the shoulder and not at the probe. Furthermore, the wear shape was different depending on rotation speed and welding speed of the tool. The factors that cause them were analyzed using temperature and stress simulations for silicon nitride tools. Increasing the rotation speed broadened temperature distribution, increased the peak temperature and narrowed the stress distribution. When the welding speed was increased, the temperature distribution became narrower and peak temperature was lower, and the thermal asymmetry increased, while the stress distribution expanded and the stress asymmetry increased. When the welding speed was high, the agitation in the plastic flow zone was insufficient, and there were places where the stress and temperature were low around the shoulder. It is considered that these decrements of the stress and temperature lead to the concave part remaining on the outer circumference. Therefore, it is important to control both the welding speed and the rotation speed of the silicon nitride tool in order to suppress the protrusion which is main reason of the tool breakage.

Prediction and Optimization of Cutting Forces required to Minimize Tool Breakage during Dry Drilling Operations

Drilling is one of the most common and rudimental machining methods in the manufacturing industries for removal of unwanted material from the workpiece. How the cutting instrument and workpiece interact results in a mechanical force that causes the formation of chips during penetration, and these chips are evacuated through the flute created on the body of the drill tool. The interplay of forces at the drill point generates high temperatures, needed for the physical and chemical processes that weaken tools and lead to breakage. Optimal experimental designs are very important to obtain accurate optimization of engineering processes, an expert method was used to design the experimental layout and utilizing the Design Expert software, an experimental matrix which developed the parameters design of twenty experimental runs. The present study uses Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) to forecast and optimize cutting forces during dry drilling operations. In developing the model, a dataset that included several factors such as depth of cut, feed rate, and cutting speed was used. The RSM model demonstrated a significant correlation between input parameters and cutting forces, as evidenced by its high coefficient of determination (R²) of 0.9493. On the other hand, the ANN model, which was trained using 70% of the data and validated using 15% of the data, showed a little lower R² value of 0.81434, but it was still able to make accurate predictions. Cutting forces were well predicted by both models, with RSM exhibiting a somewhat better performance in terms of accuracy. The results indicate that both RSM and ANN can be useful instruments for dry drilling cutting force optimization, offering insights for increased productivity and efficiency in machining operations.

In Situ Evaluation of Drill Wear Using Tool Image Captured on Machining Center

Owing to the rise in demand for electric devices, there has been an increase in the need for manufacturing equipment that produces internal control board parts. To operate this machinery, several ceramic components, such as a chuck table and fastening parts, are required. Consequently, the need for efficiently and precisely machining ceramics has increased. However, ceramics are known for their high hardness, which can lead to tool breakage when using a small tool. This is often influenced by the state of the tool wear. If the drill tip breaks off and becomes embedded in the workpiece, it could take time to remove or destroy the workpiece. To prevent such problems, drills are replaced after a certain number of machining processes, or the operator visually inspects the drill’s wear condition. Unfortunately, these methods reduce machining efficiency. Therefore, we propose a device that captures drill images on a machine tool and measures the amount of drill wear to evaluate the drill’s condition. We fabricated a device to acquire drill images and attempted to quantify the drill wear condition, such as the area and width of the worn part, by analyzing the worn shape from an image of the bottom surface of the drill.

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.

Tool Failure Detection in CFRP/Ti6Al4V hybrid stacks drilling using Discrete Wavelet Transform

The study presents a procedure for detecting tool failure in drilling of CFRP/Ti6Al4V aeronautical structural components. To accurately detect tool breakage, a method based on the Daubechies 2 Discrete Wavelet Transform (DWT) is proposed to analyze the spindle power consumption signals in the time-frequency domain. The signals are collected from the automatic drilling machine of the industrial system. At each level of decomposition, statistical measures are calculated to extract signal features that are then used as inputs to a classifier algorithm to identify signal patterns or anomalies. The Recall and Precision scores of 1 indicate the effectiveness of the proposed method.

Assessing the torque and thrust forces in micro-drilling of thermal barrier coated nickel superalloys

Nickel superalloys are crucial materials for aerospace applications, offering exceptional performance at high temperatures. Key components in aircraft engines, such as turbine blades, guiding vanes, afterburners, and casings, require the intricate process of micro-drilling to enable effusion cooling. However, when dealing with nickel-based superalloys, tool breakage during micro-drilling is a substantial drawback. This study investigates the cutting forces acting at the tool-workpiece interface during the micro-drilling of thermal barrier-coated Nimonic 90. The micro-drilling was conducted under three lubrication conditions: dry, flood, and 0.5% Graphene-based NMQL, utilizing a 700 μm diameter TiAlN-coated tungsten carbide drill. Experiments were performed at spindle speeds of 1000, 2000, and 3000 rpm, with a constant feed rate of 3 μm/rev. Results revealed that under dry conditions, the micro-drill failed after drilling just 16, 18, and 15 holes at spindle speeds of 1000, 2000, and 3000 rpm, respectively. In contrast, no failures occurred under flood and 0.5% Graphene-based NMQL lubrication conditions, likely due to improved heat dissipation, resulting in reduced thrust forces and torque acting on the micro-drill. Thrust force and torque values were measured using a Kistler 3-component mini dynamometer, with maximum values of 26 N and 0.31 N-m at 1000 rpm under dry lubrication conditions. These values decreased to 24 N and 0.25 N-m and 22 N and 0.19 N-m at spindle speeds of 2000 and 3000 rpm, respectively. NMQL lubrication conditions consistently exhibited lower thrust force and torque values compared to dry and flood conditions, with the lowest recorded values (12.5 N and 0.06 N-m) at 3000 rpm in the NMQL lubrication condition. The NMQL condition facilitated for efficient and better drilling operation due to the rolling effect produced by the graphene nanoparticles.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring (TCM) is a key technology for intelligent manufacturing. The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs. Recently, an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed. Different from traditional signal feature-based monitoring, the data from sensors are utilized to build a dynamic process model. Then the nonlinear output frequency response functions, a concept which extends the linear system frequency response function to the nonlinear case, over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions. In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools, in the present study, a multivariate control chart is proposed for TCM based on the frequency-domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis. The feature dimension is reduced by principal component analysis first. Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises. The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wears of solid carbide end mills. The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Effects of Machining Parameters and Tool Reconditioning on Cutting Force, Tool Wear, Surface Roughness and Burr Formation in Nickel-Based Alloy Milling.

Nickel-based superalloys are among the most difficult materials to machine because they have high thermal strength, they are prone to hardening, carbides severely abrade the tool, and they have very poor thermal conductivity. Slot milling is a specific issue as it is characterized by rapid tool wear and frequent tool breakages. This is why reconditioned tools are frequently employed in industrial environments, as they can considerably decrease the expenses associated with tools. The chosen machining strategy also plays a crucial role in the tool's lifespan and the quality of the machined surface, making it essential to select the appropriate strategy. Hence, the authors have opted for two conventional trochoidal strategies, namely the circular and swinging toolpath, along with a contemporary toolpath known as the Autodesk Inventor HSM Adaptive strategy. The authors investigated the effects of technological parameters and toolpaths on cutting forces, tool wear, surface roughness and burr formation on machined edges. The results show that lower cutting parameters and adaptive strategies lead to the smallest tool loads, tool wear, the best quality of surface roughness and burr formation on machined edges.