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This research aims to examine flank wear and material removal rates (MRRs) while finish hard turning work material of Inconel 718 with physical vapor deposition involving the process of cathodic arc evaporation TiAlN/TiCN-coated cermet CNC cutting tool insert. The L${}_{27}$Taguchi orthogonal design array is used for designing the experiments. The aim of this research is to optimize the process key parameters, such as cutting speed, rate of feed, depth of cut, and tool tip radius, in order to reduce flank wear and significantly improve MRR during the dry turning processes. This study examines the critical conditions of cutting parameters that were investigated by using analysis of variance (ANOVA), while the parameters which affect the flank wear and MRRs were optimized using response surface methodology according to the Design of the Taguchi orthogonal test. Mathematical models for both response parameters were derived using regression analysis, namely flank wear and MRR. The generated models achieved an accuracy of roughly 92% and 93% for estimating the flank wear and MRR values, respectively. The study revealed that cutting speed accounted for 34.28% of the most effective parameters in reducing flank wear, subsequently, the depth of cut reached 17.71%. In terms of MRR, the depth of cut accounted for 68.43% of the effectiveness, with a cutting speed of 12.94%. The optimal values for cutting speed, feed, cut depth and tool tip radius, in order to minimize wear, were determined to be 700m/min, 0.15mm/rev, 1.0mm, and 0.4mm, respectively. The optimal values for achieving the maximum MRR are 800m/min for cutting speed, 0.15m/min for rate feed, 2mm for cut depth, and 1.2mm for tool tip radius. Regression theory is used to construct correlation models, which have been shown to be statistically significant at the 0.05 level. An experimental approach is studied to investigate the coating defects and flaws of worn TiAlN/TiCN-coated cermet inserts using atomic force microscopy (AFM), optical and scanning electron microscope (SEM). Lastly, the creation of chips under optimal conditions has been shown.

This research focuses on the sustainable optimization of the milling process for Titanium Alloy Ti-6Al-4V using Minimum Quantity Lubrication (MQL) assistance. This experimental research investigates the effects of cutting parameters and lubrication conditions on the surface roughness ($R_a$) and flank wear ($V_b$) in the milling process of Titanium Alloy Ti-6Al-4V. The study aims to evaluate the influence of cutting speed ($V_c$), feed rate ($f_z$) and depth of cut ($a_p$) on the selected machining performance indicators. The experiments were carried out using a DMG Mori Seiki DMU50 CNC center, and the workpiece samples were measured at $100\mathrm{\text{mm}}\times 50\mathrm{\text{mm}}\times 15\mathrm{\text{mm}}$. Three different lubrication conditions were employed, including dry machining, conventional flood cooling and Minimum Quantity Lubrication (MQL) using peanut oil. The cutting parameters were varied based on a Taguchi L9 orthogonal array design to explore the main effects of each parameter on $R_a$ and $V_b$.

The results reveal that cutting speed ($V_c$) has the most significant influence on surface roughness, followed by feed rate ($f_z$), while the impact of depth of cut ($a_p)$ is negligible in comparison. Conversely, the coolant mode has the most significant impact on flank wear ($V_b$), followed by feed rate ($f_z$) and depth of cut ($a_p$), while the influence of cutting speed ($V_c)$ is relatively minor. Under the same cutting parameters, transitioning from dry machining to MQL led to a sharp decrease in $V_b$ from 170 to 100$\mu$m, and it increased to approximately 145$\mu$m when switching to flood cooling. Additionally, increasing the feed rate from 0.02 to 0.06mm/tooth reduced $V_b$ from around 135 to 120$\mu$m, but it significantly rose to approximately 165$\mu$m when the feed rate was further increased. Furthermore, the application of MQL with peanut oil as a lubricant and coolant demonstrated improvements in surface finish and reduced tool wear compared to dry and conventional flood cooling. The MQL approach led to better machining performance with lower $R_a$ and $V_b$ values. Overall, this study provides valuable insights into the effects of cutting parameters and lubrication conditions on surface roughness and flank wear in the milling process of Titanium Alloy Ti-6Al-4V. The findings offer practical guidelines for optimizing machining conditions to achieve desired surface quality and extend tool life while considering the environmental benefits of employing MQL in the manufacturing process.

Long tool life and high material removal rate (MRR) are the two essential requirements in rough cutting of materials. The rapid rate of the flank wear propagation in machining of nickel-based superalloys has induced the utilization of low cutting parameters when the goal was set to maximize the tool life based on the machining time or cutting length. However, this method may not provide an effective rate for the material being cut. This work presents two mathematical models to find the optimum cutting parameters results for the minimum flank wear and maximum MRR. Experimental tests were carried out based on the central composite design (CCD) in rough cutting of Inconel 625 by using TiAlN-coated insert. Maximum flank wear was measured to determine the tool wear propagation. The wear mechanisms which contribute in the tool wear were analyzed by using scanning electron microscope (SEM) to evaluate the effects of cutting parameters on the flank wear propagation. The results showed that cutting speed and depth of cut had the most significant effect on the tool wear. However, optimum cutting condition was achieved by reducing the cutting speed when feed rate and depth of cut maintained at the highest level. This was associated to the interaction of cutting speed and depth of cut, and predominant of abrasion and notching at their highest levels, respectively.

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L₉ orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the $z$-axis by 4.85%. The coconut oil with 0.5wt.% of nanoboric acid performs better than coconut oil with 0.9wt.% of nano boric acid and base oil.

Using Taguchi design of experiments (DoE), experiments were conducted with 3 factors and 3 levels. The factors were the depth of cut, spindle speed, and feed. The responses were surface roughness, flank wear, material removal rate, and spindle vibration along $x$ ($V_x$), $y$ ($V_y$), and $z$ ($V_z$) axis. To convert the multi-response optimization problem into a single response optimization problem, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied. The S/N of the closeness coefficients from TOPSIS was calculated and optimum machining conditions were obtained. Further, analysis of variance (ANOVA) was performed to verify which input parameter significantly affects the output responses. From TOPSIS optimization, the responses like surface roughness and flank wear were decreased by 0.99% and 2.55%. The vibration in $x$, $y$, and $z$-axis decreased by 3.84%, 16.87% and 12.48% respectively. This optimization significantly enhances the machining characteristics.

This study emphasizes the comparative cutting performance evaluation of CVD- and PVD-coated carbide inserts in hard turning of AISI D2 steel. The cutting factors, namely cutting speed (100m/min), tool feed rate (0.08mm/rev), and depth of cut 0.2mm, have been fixed for the entire study. The comparative study is based on the analysis of the obtained test results of flank wear, tool life, auxiliary flank wear, surface roughness, and surface texture. Both tools are catastrophically failed when tool wear reached limiting flank wear ($\mathrm{\text{VBc}}=0.3$mm) criteria. SEM and EDS analysis of both the tools (at their end of tool life) are carried. Diffusion followed by adhesion is found to be the prime mechanism at the end of tool life. Based on limiting flank wear criteria, the tool life of CVD and PVD tools was estimated as 65 and 57min, respectively, i.e. the tool life of the CVD tool is 14% longer than that of the PVD tool. Considering the limiting criteria of surface roughness ($\mathrm{\text{Ra}}=1.6\mu$m), the tool life of the CVD tool was estimated as 63min while for the PVD tool, it was found as 54min i.e. about 14.3% higher tool life was found for the CVD tool relative to the PVD tool. The auxiliary flank wear was observed to be lower for the CVD tool relative to the PVD tool. Surface roughness for both the tools increased with cutting time and the relatively larger rough surface was obtained with the PVD tool. The machining cost of one pass of the CVD tool is 2.5% less than that of the PVD tool. However, for mass production, the CVD tool is more efficient than the PVD tool for machining hard D2 steel ($57\pm 1$ HRC).

Ti–6Al–4V ELI alloy is one of the most familiar materials for orthopedic implants, aeronautical parts, marine components, oil and gas production equipment, and cryogenic vessel applications. Therefore, its appropriate quality of finishing is highly essential for these applications. But the characteristics like lower modulus of elasticity, lesser thermal conductivity, and high chemical sensitivity placed it in the categories of difficult-to-cut metal alloys. Also, tooling cost is one of the prime issues in the machining of this alloy. Therefore, this research is more inclined to use a low-budget uncoated carbide tool in turning the Ti–6Al–4V ELI alloy. Also, the selection of suitable levels of machining parameters is highly indispensable to get the appropriate surface finish with a low tooling cost. So, the $L_{16}$ experimental design is utilized to check the performances of the uncoated carbide tool in the turning tests. The performance indexes like surface roughness (Ra), flank wear of tool (VBc), and material removal rate (MRR) are measured and studied with the help of surface plots and interaction plots. Further, the Firefly Algorithm optimization is employed to find the optimal cutting parameters and cutting response values. The local optimal values of the input parameters a, f, and V_c are estimated as 0.3241mm, 0.0893mm/rev, and 82.41m/min, respectively. Similarly, the global optimal values for the responses Ra, VBc, and MRR are reported as 0.6321μm, 0.09253mm, and 24.61g/min, individually. Additionally, to predict the responses, Generalized Regression Neural Network (GRNN) modeling is employed and the average absolute error for each response is noticed to be less than 1%. Therefore, the GRNN modeling tool is strongly recommended for various machining applications.

This research emphasizes the machinability investigation on CNC turning of 7068 aluminum alloys. CVD-coated carbide tool was implemented for the $L_{27}$ full-factorial-based turning experiments in dry conditions. Machinability study includes the assessment of flank wear, cutting tool vibration, surface roughness, cutting temperature, chip reduction coefficient, and chip morphology. The selected tool performed well as very low wear (0.030–0.045mm) and low surface roughness (0.28–1.14$\mu$m) were found. All the input variables have significant impact on the flank wear, cutting tool vibration, cutting temperature, and chip reduction coefficient while for surface roughness, the effects of cutting speed and feed were significant at the 95% confidence level. Further, a novel optimization tool namely the spotted hyena optimizer (SHO) algorithm was utilized to get the optimal levels of input variables. Additionally, two different modeling tools namely multiple adaptive neuro-fuzzy inference system (MANFIS) and radial basis function neural network (RBFNN) were utilized for formulating the cutting response models. Further, the average of the absolute error was estimated for each model and compared. The MANFIS modeling tool exhibited a more close prediction of outputs as compared to RBFNN, as the obtained average absolute error for each response was lower with MANFIS.

In recent times, mechanical and production industries are facing increasing challenges related to the shift towards sustainable manufacturing. In this work, the machining was performed in dry cutting condition with the newly developed AlTiSiN-coated carbide inserts coated through scalable pulsed power plasma technique, and a dataset was generated for different machining parameters and output responses. The machining parameters are speed, feed and depth of cut, while the output responses are surface roughness, cutting force, crater wear length, crater wear width and flank wear. Abrasion and adhesion were found to be the two dominant wear mechanisms. With speed, the tool wear was found to increase. Cutting force was found to increase rapidly for higher speed ranges (70, 80 and 90m/min). But reduction of cutting force was observed in both low and medium ranges of cutting speed (40, 50, 55 and 60m/min). With higher values of feed and depth of cut, higher cutting force was also noticed. With the variation of depth of cut, the machined surface morphology and machined surface roughness were found to deteriorate. With higher feed and depth of cut values, more surface damages were observed compared to low values of feed and depth of cut. Both the feed rate and tool–chip contact length contributed significantly to the formation of crater on the tool rake surface. At high speed, continuous chips were observed. Both chip sliding and sticking were observed on the tool rake face. The data collected from the machining operation was used for the development of machine learning (ML)-based surrogate models to test, evaluate and optimize various input machining parameters. Different ML approaches such as polynomial regression (PR), random forests (RF) regression, gradient boosted (GB) trees and adaptive boosting (AB)-based regression were used to model different output responses in the hard machining of AISI D6 steel. Out of the four ML methodologies, RF and PR performed better in comparison to the other two algorithms in terms of the $R^2$ values of predictions. The surrogate models for different output responses were used to prepare a complex objective function for the germinal center algorithm-based optimization of the machining parameters of the hard turning operation.

This research focuses on wrought Ti-6Al-4V machining using coated carbide inserts under flood cooling to study the machinability characteristics. Machining parameters are optimized, and mathematical models are developed for correlations. Surface roughness lies between 0.215$\mu$m and 0.830$\mu$m and even below 1$\mu$m during machining. Flank wear lies within 0.033–0.16mm which is below the 0.2mm criteria of wear. Cutting temperature lies between 31^∘C and 158^∘C. The reduction of cutting temperature and chip serration under flood cooling and the subsequent transfer of heat from the shear zones help to generate good surface finish and may be due to the evolution of a lower wear rate. Abrasion, chipping, adhesion and built-up-edge are seen as major mechanisms of wear. The optimal conditions are found to be a depth of cut of 0.1mm, 0.1mm/rev feed rate and 70m/min cutting speed. There is an improvement in results at optimal conditions of 38.42% for Ra, 60.86% for VBc and 27% for $T$, respectively, than initial parametric conditions. Further, grey relational grade has been improved by 0.263. Machinability models developed through quadratic regression are observed to be significant.

This research investigates the impact of dry machining AA4015/B4C MMCs (metal matrix composites) utilizing an uncoated B4C insert on Flank wear (VBc) and surface roughness (Ra). This work attempts to close the knowledge gap on the effects of cutting parameters (feed, depth of cut, and speed) on tool wear and surface quality in machining technology. The primary rationale lies in optimizing machining processes for MMCs, known for their challenging machinability due to their tough metallic matrix and hard ceramic reinforcement. The study’s significance is underscored by the exploration of optimal processing parameters to minimize Flank wear and improve surface roughness, crucial factors influencing component quality and lifespan. Specifically, the research identifies v1-f1-d3 (VBc) and v3-f1-d3 (Ra) as the best process parameter combinations, significantly reducing both VBc and Ra. The obtained mathematical models for VBc and Ra provide statistically significant insights into the relationships between cutting variables and performance characteristics. The employment of Taguchi’s L9 orthogonal array proves invaluable in achieving these optimized process parameters efficiently. The Taguchi method’s advantage lies in its ability to systematically explore numerous variables and their interactions with minimal experiments. By reducing the number of trials required, this methodology streamlines the optimization process, saving time, resources, and costs while delivering enhanced machining performance for MMCs. This research, through its systematic approach and emphasis on optimized parameters, contributes to the advancement of machining techniques for MMCs, holding implications for various industrial applications demanding high-performance materials.

This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.

The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.

The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.