Types Of Tool Wear Research Articles

Effect of grain size and cobalt content on machining performance during milling tungsten carbide with PCD tool

Tungsten carbide has been extensively utilized in the manufacturing field due to its exceptional properties. However, traditional methods such as grinding or electric discharge machining often result in a low material removal rate and inevitable surface defects, making tungsten carbide one of the most challenging materials to machine. Milling has emerged as a promising alternative for machining tungsten carbide. However, the milling of tungsten carbide has not been widely applied in the industry due to a lack of comprehensive understanding regarding the milling performance of different tungsten carbides under various conditions. In this paper, a polycrystalline diamond (PCD) tool was used in the milling of tungsten carbides to investigate the effects of tool grain size, workpiece Co content, and WC grain size on tool wear and machining surface quality. It was found that PCD tools with larger grain sizes exhibited higher wear resistance. Additionally, excessive Co content in the workpiece and large WC grain size increased tool wear and degraded the quality of the machined surface. The primary types of tool wear observed in the milling of WC-Co included adhesive wear, abrasive wear, phase change wear, and oxidation wear.

THE EFFECT OF ATOMIZED SOYBEAN OIL EMULSIONS USED AS CUTTING FLUID ON THE CUTTING PERFORMANCE

Within the scope of sustainable production, a natural liquid mixture that will act as cutting fluid is sprayed with a different system. The liquid consisting of water and soybean oil was pulverized by ultrasonic atomization method and used by spraying with 4 bar compressed air. In this study, it was aimed to investigate the effects of AISI 4140 steel on machinability in CNC lathe by using uncoated carbide insert (SECO WNMG080404-M1). Three cutting speeds (150, 200, 250 m / min), three feed rates (0.1, 0.15, 0.2 mm / rev) with four cutting fluids (1%, 5%, 10% soy oil-water mixture and commercial cutting fluid) and the comparison was made by making experiments under dry cutting conditions. In all experiments, the cutting depth was 1 mm and the cutting length was 100 mm. As a result of the experiments, cutting force, surface roughness and tool wear values were examined and contact angle measurements of cutting fluids were made. The best results in all parameters were achieved by spraying a 10% soybean oil-water mixture using an ultrasonic atomizer. The best results in all parameters were achieved by spraying a 10% soybean oil-water mixture using an ultrasonic atomizer. The worst values occurred in experiments conducted under dry cutting condition. In addition, SEM photographs were taken to observe the type of tool wear. The obtained values were analyzed with the ANOVA method and the results were evaluated with 3D surface graphics. After evaluating the analysis results, percentage impact rates, regression analysis equations and optimization values are proposed.

Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning

Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses. In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once, tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition during the turning process using MATLAB. The regression method with boosted decision trees and SVR with Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%, while SVR is 10.11%. Plus, R2 for regression is slightly better than SVR. It shows that the system successfully produced an accurate prediction of flank wear.

Effects of precipitation hardening on cutting force, surface roughness and tool wear in turning of 15‐5PH stainless steel

Abstract15‐5PH steel is a group of martensitic stainless steel which is strengthened by precipitation hardening. This paper is aimed to evaluate the effects of precipitation hardening on the machinability of 15–5PH. Machining was carried out at different feed rates and cutting speeds under heat treatment conditions H900 (meaning heat treatment at 900 °F) and H1150. The cutting force, work‐piece's surface roughness and tool wear were compared to the results of turning in solution condition. Scanning electron microscopy observation revealed that crater and flank wear were the main tool wear types in the turning of 15–5PH under the H900, solution and H1150 conditions. In addition, turning in the H900 condition causes defects such as “side flow” of the workpiece surface and “plowing” by the tool, compared to turning in the solution condition. The analysis of variance indicated that H1150 had the highest performance in reducing the cutting force, tool wear and surface roughness. The comparison between turning in H900 and solution conditions showed that the average cutting force and tool's flank wear in the H900 condition are 18 % and 11 % higher than solution condition, respectively. However, the mean roughness was 14 % lower under H900 than the solution condition.

Monitoring Built-Up Edge, Chipping, Thermal Cracking, and Plastic Deformation of Milling Cutter Inserts through Spindle Vibration Signals

Condition monitoring provides insights into the type of damage occurring in the cutting tool during machining to facilitate its timely maintenance or replacement. By detecting and analyzing machining consequences (vibrations, chatter, noise, power consumption, spindle load, etc.), correlating them with different tool conditions enables real-time monitoring and the automated detection of tool failures. Machine learning (ML) plays a vital role in making tool condition monitoring (TCM) frameworks intelligent, and most research is geared toward classifying various types of tool wear. However, monitoring built-up edges, chipping, thermal cracking, and plastic deformation of milling cutter inserts are challenging and need careful consideration. To effectively monitor these phenomena, spindle vibrations can narrate the corresponding dynamic behavior of tool conditions and therefore have been investigated in this research. The acquired vibration data are then analyzed using histogram features and trained through the Partial C4.5 (PART) classifier to extract meaningful recommendations related to the milling cutter inserts condition.

Tool wear classification based on convolutional neural network and time series images during high precision turning of copper

Artificial intelligent techniques are critical in predicting tool wear in the machining process. Effective assessment of the rate and state of tool wear are important in the high-speed turning process and makes it possible to replace the tool before catastrophic wear occurs. It is well known that cutting forces are often used as an indicator to monitor tool wear during the machining process. However, how to use deep learning methods to incorporate cutting forces, accurately predicting the tool wear, remains a challenge. In this paper, a novel tool wear classification method based on time series images of cutting forces and convolutional neural networks was proposed. The one-dimensional cutting force signals are converted into two-dimensional time series images using the Gramina Angular Summation Fields method. Then, a modified AlexNet network is used to classify tool wear types based on the processed cutting force time series images. The prediction results show the overall prediction accuracy of the four tool wear categories can reach around 90%. In addition, by balancing the number of cases in the dataset, the prediction accuracy of each category can be improved while the overall prediction accuracy is maintained.

Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network

In order to guarantee machining quality and production efficiency and as well as to reduce downtime and cost, it is essential to receive information about the cutting tool condition and change it in time if necessary. To this end, tool condition monitoring systems are of great significance. Based on the data fusion technology of multi-sensor integration and the learning ability of deep residual network, a tool wear monitoring system is proposed in this paper. For data acquisition, multiple sensors are used to collect vibration, noise and acoustic emission signals in the machining process. For signal processing, the data fusion method of angular summation of matrices is used, so that the multi-source data are fused into two-dimensional pictures. Then, the feature extraction module of the proposed system uses the pictures as input and takes advantage of the deep residual convolutional network in extracting the deep features from the pictures, and finally the identification module completes the recognition of the tool wear type. To verify its feasibility, a testing bed is built on a CNC milling machine, and multi-sensor data are collected during the machining process of a simple workpiece with one of the two selected cutting tools each time. The proposed system is trained using the collected data and then is used to monitor the wear condition of a new cutting tool by collecting real-time data under similar condition. The results show that the accuracy of wear state recognition is as high as 90%, which verifies the feasibility of the proposed tool wear monitoring system.

Performance investigations for sustainability assessment of Hastelloy C-276 under different machining environments

Hastelloy is categorized as difficult to cut superalloy widely used in aerospace, nuclear reactor components and chemical industry because of its magnificent strength and higher heat efficiency. Since, the machining of this material is quite difficult and hence suitable cooling systems are required to achieve sustainable manufacturing goals. The present investigation has been focused on the machining performance and sustainability assessment of turning Hastelloy C-276 in dry, flood and minimum quantity lubrication (MQL) environments. Taguchi L-9 array has been utilized to conduct and record the experimental output along with TOPSIS approach to evaluate the sustainability. The output responses viz. cutting forces, surface roughness, cutting temperature, energy consumption and carbon emission have been recorded at various levels of input variables. The experimental results revealed that MQL has minimized the cutting forces, surface roughness and temperature by margin of 20–38%. Likewise, energy expenditure and carbon emission was declined by 9–27% respectively compared to other conditions. Sustainability analysis explored best performance index during equal weightage criteria at 125 m/min, 0.246 and 0.8 mm doc under MQL. However, implementing assigned weightage system evaluated best condition for dry machining as 88 m/min and 0.246 mm/rev having same doc. SEM analysis of insert reported mainly abrasion and adhesion type of tool wear at all parametric range and machining conditions.

Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network

In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.

Reverse identification of constitutive parameters of Inconel 718 alloy based on analytical model and thermo-mechanical loads analysis of machined surface

Finite element simulation is able to predict cutting force, temperature, stress distribution and chip formation. Constructing a constitutive model suitable for cutting simulation can accurately describe the state of the cutting process, which plays an important role in improving surface quality as well as optimizing cutting parameters and tool geometric design. Nickel-based superalloys are widely used in aerospace due to their excellent physical and mechanical properties at high temperatures. In this study, J-C parameters reverse identification of Inconel 718 alloy was carried out in both the solution annealed state and precipitation hardened state. The average cutting force and chip thickness were obtained by conducting orthogonal cutting experiments, after which the shear angle was calculated. Through quasi-static compression experiments, the yield strength, strain hardening modulus and hardening index of the material were then obtained. Considering the influence of the cutting-edge radius on cutting force during the cutting process, the Waldorf model was introduced into the analytical model in order to describe the deformation characteristics (strain, temperature, strain rate and stress) of the primary shear zone, after which the shear force was modified. The J-C constitutive parameters of the solution annealed state and precipitation hardened state were inversely identified by the intelligent optimization algorithm. According to the given range of cutting conditions, the accuracy of the method was verified through comparison with the cutting force and chip morphology of the finite element simulation. The differences in quasi-static and cutting dynamic mechanical responses of the two states were subsequently expounded. The effects of tool wear types and cutting parameters on the material flow characteristics at the tool tip as well as the thermal-mechanical load on the machined surface of precipitation hardened state Inconel 718 alloy were analyzed via numerical simulation. Accordingly, this study may provide insight into the identification of constitutive parameters of superalloys as well as the control of machined surface quality.

An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model

An accurate detection of tool wear type in drilling process by applying PCA and one-hot encoding to SSA-BLSTM model

Influence of hybrid Cryo-MQL lubri-cooling strategy on the machining and tribological characteristics of Inconel 718

The poor thermal conductivity of Inconel 718 leads to higher cutting temperatures and, as a consequence, rapid tool degradation is a common phenomenon. As a result, a hybrid lubri-cooling environment for turning Inconel 718 alloys is proposed, incorporating the theory of cryogenic cooling and minimum quantity lubrication (Cryo-MQL). For improved lubri-cooling effect, Cryo-MQL integrates the application of a minimum quantity of vegetable oil and liquid nitrogen from two distinct nozzles in the cutting zone. Surface roughness, cutting temperature, tool wear, chip morphology, and micro-structure of the machined surface were evaluated for different lubri-cooling mediums: dry, MQL, Cryogenic, and Cryo-MQL. In comparison to a dry medium, the Cryo-MQL environment decreases surface roughness, cutting temperature, and tool wear by 60.6%, 37%, and 19.5%, respectively. Adhesion and abrasion were patented to be common tool wear types, as per SEM micro-graphs. Eventually, in the Cryo-MQL environment, a spike in micro-hardness value has been reported. However, during processing with Cryo-MQL, the grain structure of the working material is found to be smaller as compared to other mediums.

Assessment of Tool Wear and Surface Integrity in Ductile Cutting Using a Developed Tool

Machined parts of aluminum alloys are used in a wide range of engineering applications, such as automobile parts. Many types of tool wear may occur during machining of these alloys, which reduces tool life. Therefore, it is beneficial to undertake comprehensive studies to improve tool life (reduce tool wear) in machining aluminum alloys. In the present study, the micro-textured cutting tool's performance is compared to the non-textured tool with chip breaker (and without chip breaker) when used cutting fluid under different cooling-lubrication methods. A literature review denoted that no experimental work is available for a comparison study between texture and chip breaker performance. Scanning electron microscope (SEM) images and energy-dispersive X-ray spectroscopy (EDX) were utilized to analyze the workpiece's surface. Although many scholars have stated that the chip breaker has a good effect on cutting performance, this research showed that the chip breaker has no positive effect on reducing tool wear in aluminum machining. Nonetheless, the SEM images denote a maximum of 30% and 36% improvement in surface quality and tool life, respectively, a considerable decrease in adhesion, abrasion, and breakage wear, and a drastic reduction in the density of defective regions in the workpiece surface when the optimum micro-textured tool was used.

Evaluacija performansi obrade M2 reznog alata tretiranog tečnim azotom kod obrade na strugu

The present study is focused on liquid nitrogen treatment of M2 tool steel for 12 hours, 24 hours, and 36 hours at -196 0 C followed by tempering at 150 0 C. The treated tool samples are used for machining low carbon steel on a CNC lathe to varying combinations of Spindle speed (700, 1100 & 1800 rpm), Feed rate (0.1, 0.125 & 0.15 rev/mm) and Depth of cut (0.15, 0.3 & 0.45 mm). Taguchi's Design of experiments and ANOVA methods are used to statistically analyze the effect of cutting parameters and cutting forces on the tool wear and surface roughness after experimentation. SEM analysis is carried out to investigate the type of tool wear experienced by the cutting tools. The regression analysis has resulted in a model to predict the values of surface roughness, tool wear and friction coefficient, with a maximum error of ±6% from the actual experimental values.

MUTUAL CHANGE OF ACOUSTIC EMISSION STATISTICAL ENERGY PARAMETERS AT TREATING TOOL WEAR

The results of experimental studies of acoustic radiation energy during processing tool wear are considered. The regularities of acoustic emission signal statistical energy parameters changes at normal and catastrophic processing tool wear are determined. It is revealed that the regularities of change in the statistical energetic parameters of acoustic emission signals do not observe a characteristic features of the change which are associated with the appearance of a certain type of tool wear. The regularities of experimental acoustic emission signal statistical energy parameters mutual change during normal and catastrophic processing tool wear are determined. It is shown that the ratio of the acoustic emission signal average energy level to the average energy level standard deviation at a given analysis interval is a sensing parameters to the mechanisms and stages of cutting tool wear during materials machining.

Durability and tool wear investigation of HSSE-PM milling cutters within long-term tests

Tool durability is an important parameter in the evaluation of the performance of the cutting tools and inserts. It is directly connected with tool wear that significantly affects the quality of the workpiece. Only from an understanding of the nature behind the machining process in given conditions (a combination of cutting parameters, machined material, tool characteristics including its material, etc.), a tool wear mechanism can be identified and consequently estimate the tool life and its durability.The article deals with a durability of the milling cutters produced from the high-speed steel via powder metallurgy (HSS-PM) and coated by AlTiN coat. Effects of a varying combination of cutting speed and depth of cut to tool wear and surface roughness had been also investigated within the long-term tests. Conventional milling was selected as a technology to test the durability of HSSE-PM shank milling cutters at which the steel 1.4301 (AISI 304, DIN 1.4301) as a material that is very difficult to cut was machined without a coolant. Within the research, the tool wears in several points on a cutting edge were evaluated, while several types of tool wear have been identified. Achieved data were statistically processed and based on them the development of the tool wear of individual milling cutters in time has been processed as well as a dependency of the tool wear on cutting speed and cutting depth has been plotted. The accompanying phenomenon of tool wear increasing is a deteriorated quality of surface roughness and change in a chip formation. The average values of surface roughness Ra relative to machining time achieved by milling cutters working at a specific depth of cut were also assessed. The pictures of the chips approved that along with tool wear increasing the chip becomes thinner with larger rugged relief.

Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process.

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

Quantitative analysis of cooling and lubricating effects of graphene oxide nanofluids in machining titanium alloy Ti6Al4V

Ti6Al4V is widely used in industry due to its outstanding mechanical properties. However, the severe abrasion and high temperature at tool/chip and tool/workpiece interfaces cause various types of tool wear in machining Ti6Al4V. To ensure high machining efficiency and high quality of machined surface, cooling fluid is often used to reduce the cutting temperature and friction. In this paper, the cooling and lubricating effects of coolant with graphene oxide nanosheet suspension were investigated experimentally and theoretically. Cutting experiments were conducted to compare the performance of conventional coolant with that of the coolant with graphene oxide nanosheets of different weight percentages (0.1% and 0.5%). Cutting force and temperature on the rake face were measured in each cutting pass. A theoretical model based on computational fluid dynamics (CFD) was developed to investigate the temperature distribution and cooling efficiency quantitatively. Friction force and coefficient of friction at tool/chip interface and tool/workpiece interface were calculated to analyse the lubrication effects of different types of coolant. The results showed that the performance of cooling and lubrication of the coolant became better with the addition of graphene oxide nanosheets. Results from the analysis of flank wear and crater wear and the morphological characteristics proved that there was a significant further reduction in cutting temperature and friction force when coolant with graphene oxide nanosheets was used.

Tool wear and induced damage in CFRP drilling with step and double point angle drill bits

Drilling operations on Carbon Fiber Reinforced Plastics (CFRP) have become crucial for the manufacturing process of multiple components on the aerospace industry. The main objective of this research is to develop a comparative analysis between a step geometry drill bit, currently used in the aerospace industry for drilling processes in CFRP with a hole diameter of 9.54 mm, and the double point angle geometry. The tool material is tungsten carbide with a diamond coating. The performance of each cutting geometry is assessed based on the type of tool wear, the evolution of the thrust force and cutting torque and the onset of machining induced damage on the test specimens. Although the main wear mechanism suffered by both tools was very similar, it was observed a remarkable influence of the cutting geometry on the tool wear evolution and the associated thrust force. This different performance also affected the onset of the machining induced damage.

Tool Life Investigation of the Thread Making Tools

The article deals with an experimental investigation tool life of the thread making tools. During long-time tests, the uncoated taps for M12 threads making were used. They were produced from high speed steel with cone type C. Three helix angles ω = 0°, 15° and 35° of taps have been investigated at the cutting speeds of 10 mmin-1 (within the first experimental phase) and 20, 30, 40 mmin-1 (during the second phase). The holes for threads making were pre-drilled into workpieces that were bars (with a cross-section of 30 x 60 mm). They were produced from C45 steel that is a standard material used at long-term tests of tool durability. Microhardness of the workpiece bars was checked within preliminary tests as well as a rank angle of tools to be ensured the same conditions of experiments. The changes in a size of the M12 thread outside the tolerance field 6H, the visible changes in tool wear and the brittle fractures of the tools have been selected as the criteria for tool life evaluation. Achieved results were statistically processed and the dependencies of thread length (m) and tool life (min) respectively on the cutting speed were plotted. The types of tool wear have been also analysed at individual cutting speeds.