Surface model and tool-wear prediction model for solid lubricant-assisted turning

The application of environment-friendly cutting fluid in machining processes can strongly influence the wear on the cutting tools and the surface finish on workpiece materials. This is only possible, when the cutting fluid provides better penetration into the cutting zone, thereby providing a better cooling and lubricating effect. Therefore, this study aims to show the effect of various cutting fluid cooling conditions and machining parameters on tool flank wear (VB) and surface roughness (Ra) of the work piece while turning AISID2 steel with coated CBN tools. By adopting RSM technique with FCCD and using analysis of variance, the effect of cutting parameters on desired outputs was explored. The results showed that machining time was the most dominant parameter influencing both tool wear and surface roughness. Moreover, cutting fluid conditions also showed considerable contribution towards decreasing tool wear rate and increasing surface finish. In addition, the cutting tools were examined under scanning electron microscope (SEM) together with EDS. It was observed that abrasion along with BUE formation were the most dominant wear mechanism modes at low cutting speeds. However, at higher cutting speed and feed combinations, abrasions followed by diffusion and adhesion were the dominant form of wear mechanisms. Suppression of BUE was observed at higher cutting speeds of CBN tools. Finally, desirability function approach (DFA) was used to find out the optimal cutting parameters for minimum tool wear with the maximum surface finish .

Aluminium hybrid metal matrix composites have significantly increased in many advanced applications owing to their unique properties. However, these hybrid composites are difficult to machine because of hard reinforcements in the matrix. After identifying the optimum cutting conditions, it's crucial to identify the tool wear mechanisms to keep surface roughness within the desired range, especially with hybrid composites. Hence, the present work mainly focuses on identifying the wear mechanism of PVD-coated TiAlN cutting tool inserts during the end milling of Al 6082 hybrid metal matrix composites. MQL, CO2, and a combination of MQL + CO2 cooling conditions were employed over the cutting zone to decrease tool wear. In the context of 300 mm of cutting length, employing CO2, MQL, and MQL + CO2 conditions led to a significant reduction in tool wear by 34.5 %, 50.1 %, and 74.7 %, respectively, compared to dry cutting. The results showed that the MQL + CO2 condition produced better results than the other cooling conditions, improving tool life and surface quality.

One of the problem in metal cutting is tool wear. Its close relation to the expected tool life and quality of product, make tool wear become object of investigation. Eventough tool manufacturers always give recommendation on machining parameter, but they don't share the information on tool wear estimation. Some theory on tool wear has been proposed, but still need verification thru experimental works to determine parameters and constants of tool life typical to machine tool condition and machining process. In this research, experimental work and analysis of the influence of spindle speed (n, rpm), feeding speed (Vf, mm/min) and depth of cut (ap, mm)to surface roughness and tool wear on milling process using carbide ballnose with diameter of 10 mm on Al-7075 had been conducted. From the experiment, it can be concluded that tool wear has influence on surface roughness of product. Tool wear could be minimized by choosing optimum spindle speed and feeding speed.

This research investigates the impact of high-speed and thermal-assisted machining (HS-TAM) on tool wear and surface roughness during the milling of SKD11 steel. The goal is to identify high-speed and elevated temperature zones that can improve machining efficiency, enhance surface quality, minimize costs, and extend tool life. The study involves the high-speed milling of SKD11 steel at various temperature conditions to evaluate the effect of temperature on tool wear and surface roughness. Additionally, experiments are conducted at the highest allowable support temperature with increased high-speed cutting to examine the effect of high speed on tool wear and surface roughness. The study demonstrates the correlation between cutting-tool wear and surface roughness at various high-speed cutting conditions and TAM environments and provides recommendations for cutting speeds and heating temperatures for different quality and productivity objectives. The findings indicate that high-speed milling of SKD11 at 600 m/min and 500 °C can decrease cutting tool-wear height (wear volume) and surface roughness by 82.47% (95.74%) and 91.08%, respectively, compared to machining at room temperature. Furthermore, the higher-speed modes at 500 °C result in a slight increase in wear height and surface roughness for high-speed cutting below 800 m/min, but reduces surface roughness for high-speed cutting beyond 800 m/min, reaching a value of 0.158 µm at the high-speed cutting limit of 1000 m/min.

This article focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems, and tool wear model based on LS-SVM for ball-end milling cutter is established by considering the joint effect of machining conditions. In the established model, machining parameters and position parameter of ball-end cutter are considered as input and the output of the proposed model is tool wear of cutting edge position. The experimental measured tool wear is used to train the established model, and the interconnection relationship between input and output parameters is determined after training. The analysis and comparison of predicted performance are given by taking different tuning parameters and data regularisation. Some interesting analysis results are deduced from the established LS-SVM-based tool wear model. In order to further show the effectiveness of LS-SVM-based tool wear model, the verified comparison between LS-SVM-based and ANN-based model is given. Finally, the discussion of interactional effect of machining parameters on tool wear estimation is used to evaluate prediction performance of LS-SVM-based model. The verification shows that the LS-SVM-based tool wear model is suitable to predict tool wear at certain range of cutting conditions in milling operation.

Superalloys are generally considered to be difficult to machinability. In recent attempts to facilitate their machinability, minimum quantity lubrication (MQL) has been used due to its positive influence on cutting tool life, the environment and human health. This study focused on the tool life, wear behavior and surface roughness in the milling of nickel-based superalloy Waspaloy. Uncoated carbide tools, PVD (TiAlN)-coated tools and CVD (TiCN + Al2O3 + TiN)-coated tools were used. Experiments were performed with different cooling methods which included dry, wet and MQL. SEM and EDX were used for tool wear behavior and mechanism analyses. A portable measuring instrument and a 3D optical profilometer were employed for surface roughness analyses. Results obtained from the tests showed that during the milling of the Waspaloy, adhesion and abrasion were effective wear mechanisms for the cutting tools. The main wear behaviors were flank wear, crater wear and BUE (built up edge). In addition to these results, reduction in the tool wear, the wet machining and machining with the MQL system provided improvement at the rates of 37.54% and 29.01%, respectively, when compared to the dry machining.

Effect of tool wear on surface roughness in machining of AA7075/ 10 wt.% SiC composite

Titanium is a material that has a good strength to weight ratio, excellent corrosion resistance, and resistant to high or low temperatures Titanium alloys that are used in various industrial fields, one of them is the Ti6Al4V alloy. Micro drilling has a high rotational speed which results in tool wear, high temperatures, and surface roughness. This research uses The Finite Element Method (FEM) simulation. The application used is DEFORM 3D based on parameters from previous studies. The purpose of this research is to identify the progress of tool wear in a simulated manner on the Ti6Al4V alloy micro-dilling and analyze the tool wear on the Ti6Al4V alloy micro-drilling under dry machining conditions. Machining trials used CNC micro-drilling at various of cutting parameters such as cutting speed of 10.000 and 15.000 rpm. The results showed that the tool wear progress at the 5th hole was 0.00346 mm, the tool wear progress at the 10th hole was 0.00462 mm and the tool wear progress at the 15th hole was 0.00525 mm. Analyzing tool wear in simulated machining and experimental machining in dry conditions has a high wear value. Tool wear is simulated by measuring the radius of the tool in the 15th hole valued at 0.09973 mm. Tool wear is experimentally measured from the tool radius in the 15th hole valued at 0.0101 mm. The tool mesh is changed due to the tool wear. The tool mesh in the 5th hole is worth 17372 mesh. The tool mesh in the 10th hole is 15662 mesh. The tool mesh in the 15th hole was highly reduced by 13021 mesh. Tool wear affects the change in tool mesh. With increasing tool wear then tool mesh is reduced. The next cause of tool wear is the tool temperature in the machining process.

The study attempts to investigate tool wear (flank wear) and surface roughness during finish hard turning of AISI D3 steel (58HRC) with coated carbide (TiSiN-TiAlN coated) cutting tool. Taguchi L9 (3)3 orthogonal array has been applied for experimental design. S/N ratio and ANOVA analyses were performed to identify significant parameters influencing tool wear and surface roughness. The cutting speed and feed were the most significant factors influencing tool wear (flank wear), and feed is the most significant factor influencing surface roughness (Ra). Mathematical models for both response parameters i.e. tool wear and surface roughness were obtained through regression analysis. The confirmation experiments carried out at optimal combination of parameters given by Taguchi’s analysis, predicted the response factors with less than 5% error. In addition, Desirability function module in RSM was applied to arrive at the optimal setting of input parameters to minimize tool wear and surface roughness. The optimal solution provided by desirability function optimization was compared with the optimal setting of parameters given by Taguchi analysis. The optimization results provided by both techniques are in close proximity.

Identification and estimation of cutting tool wear and surface roughness of the machined surface are important in the milling process. This paper presents the correlation analysis of cutting force, acoustic emission signals, tool life, and surface roughness. We present the details of the dominant features discovery, which have a high correlation with tool wear and surface roughness. The best compound features found by the correlation analysis are verified by multiple regression models and are used to construct fault estimation models. A case study of tool wear and surface roughness estimation is presented. The good agreement between the estimation results of real tool wear and surface roughness data demonstrates the usability of acoustic emission signals in tool condition monitoring.

Tool wear and surface roughness of Al 2O 3 particle-reinforced aluminium alloy composites

The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ1 = 64.5), tool wear (Ψ2 = 22.3) and metal removal rate (Ψ3 = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm3/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

The effect of application of cutting fluid with solid lubricant in suspension during cutting of Ti-6Al-4V alloy

Objective: For a dental material to be machinable for CAD/CAM technology, it must offer convenient machining, under a given set of cutting conditions. Quantitative evaluation of machinability has been assessed in literature through various parameters such as tool wear, penetration rates, surface roughness, cutting force and power. A machinable ceramic will typically demonstrate a higher tool penetration rate with signs of reduced diamond tool wear and edge chipping. The purpose of this in vitro study was to evaluate the feasibility of machining an experimental ceramic, 20 wt.% zirconia reinforced mica glass ceramics (G20Z) for indirect dental restorations and compare the tool penetration rates of G20Z to commercially available dental ceramics, Presintered Zirconia (PSZ) and IPS emax CAD. Material and Methods: Precursors of base glass (SiO2 -Al2O3 -K2O -MgO-B2O3 -F) were melted at 15000C for 2 h in a platinum crucible and quenched in deionised water. The glass frit was ball milled with 20 wt. % YSZ (G20Z) and subject to two stage heat treatment in a muffle furnace. Specimens of G20Z (12 X 2 mm) were evaluated for their feasibility of machining under varying spindle speed, depth of cut, and feed rates. Influence of depth of cut, spindle speed and feed rate (vc=8000-16000 rpm, d=0.4-0.8 mm, f=0.1- 0.3 mm/tooth) on cutting forces, material response, surface roughness and tool wear were investigated. Tool penetration rates, tool wear and margin chipping were also evaluated and compared with Pre-sintered Zirconia (PSZ) and e.max CAD in a custom dental milling surveyor at 30,000 rpm with a load of 0.98 N under water lubrication for 6 min. Tool penetration rates were calculated as the ratio of length of cut and milling time with a measuring microscope and scanning electron microscope was used for tool wear and edge chipping. ANOVA and Tukey Kramer tests were used for statistically comparing the means of each group. Results: Spindle speed and feed rate play a significant role in influencing surface roughness, thrust force, cutting forces and tool wear. Penetration rates of G20Z (0.32 ±0.12 mm/min) was significantly greater than PSZ (0.26 ±0.06 mm/min) and IPS e.max CAD (0.21 ±0.05 mm/min). SEM observations reveal tool abrasion and edge chipping regardless of the ceramic type. Conclusion: High spindle speeds delivers low cutting forces with an average surface roughness of 1.61 µm, with abrasive wear of the tool insert and brittle fracture of zirconia mica glass ceramic composites. G20Z with its machinable nature demonstrates greater tool penetration rates than PSZ and IPS e.max CAD. Tool wear and edge chipping is seen in all the investigated ceramics. Keywords Machinability, Dental Ceramics, Mica Glass-Ceramics, Dental Zirconia, Tool penetration rates.

Optimization of Process Parameters during EDM of SS304 using Taguchi-Grey Relational Analysis