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This paper addresses an approach based on the Taguchi method with gray relational analysis for optimizing the turning parameters of hardened DIN 1.2344 hot work tool steel (54 HRC) with multiple performance characteristics. A gray relational grade obtained from the gray relational analysis was used for the performance characteristic in the Taguchi method L${}_{18}$ (2${}^1\times 3^2)$. The optimal turning parameters for surface roughness and tool wear were determined using the parameter design proposed by the Taguchi method. Dry turning tests were carried out using cryogenically treated and untreated uncoated carbide cutting tools. The cutting tool (Untreated and Deep Cryogenic Treated), cutting speed (200, 250 and 300m/min) and feed rate (0.09, 0.12 and 0.15mm/rev) were selected as experiment parameters. The analysis results revealed that the feed rate (72.84%) was the dominant factor affecting surface roughness and the cutting speed (93.93%) was the dominant factor affecting flank wear. The optimum turning parameters for the lowest Ra values were A₂B₁C₂ and for the lowest Vb values were A₁B₃C₂. According to the results of gray relational analysis, the optimum parameters for minimum average surface roughness and minimum flank wear were A₁B₂C₂.

In order to reduce the adverse effects on the environment and economy and to avoid health problems caused by the excessively used cutting lubrications, cryogenic machining is drawing more and more attention. In this work, a novel cryogenic machining approach was applied for drilling of carbon fiber-reinforced polymers (CFRPs). According to this approach, CFRP was dipped into the liquid nitrogen (LN₂) and it was machined within the cryogenic coolant directly. Various machinability characteristics on thrust force, delamination damage, tool wear, surface roughness, and topography were compared with those obtained with dry condition. This experimental study revealed that the novel method of machining with cryogenic dipping significantly reduced tool wear and surface roughness but increased thrust force. Overall results showed that the cryogenic machining approach in this study improved the machinability of CFRP.

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

This paper presents the surface modification of aluminium-6061 by electric discharge machining (EDM). Si–Cu powder metallurgical green compact tool is used to deposit its material on to the work surface under reverse polarity of EDM. Compact load, current and pulse on-time are selected control parameters. Material deposition rate (MDR), tool wear rate (TWR) and surface roughness ($R_a)$ are considered as process outputs. Scanning electron microscopic (SEM) analysis and energy dispersive X-ray (EDX) analysis show the presence of tool materials in the deposit of work surface. Olympus optical micrograph shows an average thickness of the deposited layer to be 18.73$\mu$m. The hardness of the deposited layer is found to be 268HV. Analysis of variance (ANOVA) shows the compact load to be the most effective parameter on surface modification followed by pulse on-time and current, respectively.

Electrical discharge machining (EDM) is one of the most explored nonconventional machining processes due to its ability to machine intricate shapes on conductive materials. However, tool wear is one of the major challenges in the EDM process as it directly affects the accuracy of machining, surface roughness, reproduction of geometrical characteristics on the workpiece and cost of the process. Lots of work have been done to minimize the tool wear by improving the discharge conditions by controlling the EDM process parameters, varying the dielectric characteristics, powder-mixed dielectric methods and ultrasonic-assisted methods. However, minimizing the tool wear by the above approaches also constrains the material removal rate from the workpiece and accuracy of the process. This review highlights the efforts done by the researchers to improve tool wear by recently developed techniques or modifications. Researches available in the field of using treated tool electrode, cooled tool electrode, coated tool electrode, noble tool materials and other techniques are highlighted.

In the present work, the performances of TiAlN-, AlCrN- and AlCrN/TiAlN-coated and uncoated tungsten carbide cutting tool inserts are evaluated from the turning studies conducted on EN24 alloy steel workpiece. The output parameters such as cutting forces, surface roughness and tool wear for TiAlN-, AlCrN- and AlCrN/TiAlN-coated carbide cutting tools are compared with uncoated carbide cutting tools (K10). The design of experiment based on Taguchi’s approach is used to obtain the best turning parameters, namely cutting speed ($V$), feed rate ($f$) and depth of cut ($d$), in order to have a better surface finish and minimum tool flank wear. An orthogonal array (L${}_9)$ was used to conduct the experiments. The results show that the AlCrN/TiAlN-coated cutting tool provided a much better surface finish and minimum tool flank wear. The minimum tool flank wear and minimum surface roughness were obtained using AlCrN/TiAlN-coated tools, when $V=160$m/min, $f=0.318$mm/rev and $d=0.3$mm.

Surface finish of machined workpiece is one of the factors to evaluate the performance of machining operations. There are different factors such as machining parameters that affect the surface finish of machined workpiece. Tool wear is an unwanted machining issue that highly affects the surface finish of machined workpiece. In a similar way, different parameters (e.g. cutting speed, feed rate and depth of cut) also affect tool wear. In this research, we investigated how the complex structure of machined workpiece is related to the complex structure of tool wear. For this purpose, we benefited from the fractal analysis. The experiments were conducted based on the variations of machining parameters (depth of cut, feed rate and spindle speed), and accordingly the fractal dimension of machined surface was analyzed versus the fractal dimension of tool wear. Based on the obtained results, the complexity of machined surface is related to the complexity of tool wear. Fractal analysis could be applied to other machining operations to analyze the complex structures of machined surface and tool and potentially make a relationship between them.

In this work, an attempt has been made to optimize the process parameters on turning operation of INCOLOY 800H, with the aid of cryogenically treated (24h, 12h and untreated) multi-layer chemical vapor deposition (CVD) coated tools. The influencing factors like cutting speed, feed rate, depth of cut and cryogenic treatment were selected as input parameters. Surface roughness, microhardness and material removal rate (MRR) were considered as output responses. The experimentation was planned and conducted based on Taguchi L₂₇ standard orthogonal array (OA) with three levels and four factors. Multi-criteria decision making (MCDM) methods like grey relational analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS) have been used to optimize the turning parameters in this work. Similar results were obtained from these MCDM techniques. Analysis of variance (ANOVA) was employed to identify the significance of the process parameters on the responses. Experimental research proved that machining performance could be improved efficiently at cutting speed is 55m/min, feed rate is 0.06mm/rev, depth of cut is 1mm and 24h cryogenically treated tool. Tool wear was analyzed for the cutting tool machined at the optimum cutting condition with the help of scanning electron microscope (SEM) and energy dispersion spectroscopy (EDS). Dry sliding wear test was also conducted for the optimal condition. The percentage improvement in machining performances is 12.70%.

This study has been carried out to understand and reveal the efficacy of using surface-textured tools in the dry turning of Ti grade-23 alloys. The textures have been fabricated nearer to the primary cutting edge (in the rake face) and the secondary cutting edge (in the flank face) of the tool. The method used for texture fabrication is a nonconventional machining technique, Wire Electro-discharge Machining (WEDM). Micro-grooves/channels have been fabricated in the tools (rake face and flank face). The study also aimed to reveal the influence of texture pattern as well as its position on the cutting edges. The rake face-textured tools had four varieties: vertically textured (VT-R), diagonally textured (DT-R), horizontally textured (HT-R) and cross-textured (CT-R). On the other hand, the flank face had three varieties: cross-textured (CT-F), horizontally textured (HT-F) and vertically textured (VT-F). The cutting performance was measured and compared among the aforementioned different textured tools and nontextured (NT) ones in terms of the forces ($F_x$, $F_y$ and $F_z$), temperature, tool wear and chip morphology. The study revealed that the textured tools performed better and the VT-R tool performed the best followed by the DT-R and VT-F tools as compared to NT ones for the dry turning of Ti grade-23 alloys.

A series of experiments were conducted to study the performance of a coated cemented carbide tool in high speed milling of Ti-6Al-4V alloy. Experimental measurements of three components of the cutting forces were performed by using a three-component dynamometer. The cutting temperature was measured by using an infrared thermal imager. The variation of cutting forces and cutting temperature with the cutting parameters are investigated. The influence of cutting speed, axial depth of cut, and feed rate on the cutting forces and cutting temperature are analyzed and discussed. The wear patterns of the tool were investigated using scanning electron microscope (SEM) and analysis of energy spectrum, and the wear mechanism is discussed. It is found that abrasive wear and adhesive wear are the dominant wear mechanism of the tool.

Tool wear of a cutting tool has a significant impact on the tool life and surface quality of the finished product. Tool wear is influenced by many factors such as cutting parameters, tool geometry, coating type, work piece material, chatter, and cutting condition. In the present work, the design of experiments (DOE) technique has been used for four factors at five levels to conduct experiments. Tool wear is taken as the response variable measured during end milling, while helix angle, spindle speed, feed and depth of cut are taken as the input parameters. The material and tool selected for this study are AISI 304 stainless steel and uncoated solid carbide end mill cutter respectively. The tool wear was measured using tool maker's microscope. The experimental values are used in six sigma software for finding the coefficients to develop the regression model. The direct and interaction effect of the machining parameter with tool wear were analyzed using contour graphs, which helped to select process parameters for reducing tool wear and also ensure quality of milling.

Tool wear is one of the unwanted phenomena in machining operations where tool has direct contact with the workpiece. Tool wear is an important issue in milling operation that is caused due to different parameters such as machine vibration. Tool wear shows complex structure, and machine vibration is a chaotic signal that also is complex. In this research, we analyze the correlation between tool wear and machine vibration using fractal theory. We run the experiments in which machining parameters, namely depth of cut, feed rate and spindle speed change, and accordingly analyze the variations of fractal dimension of tool wear versus the fractal dimension of machine vibration signal. Based on the obtained results, variations of complexity of tool wear are reversely correlated with the variations of complexity of vibration signal. Fractal analysis could potentially be applied to other machining operations in order to investigate the relation between tool wear and machine vibration.

Researchers are always trying to explore, use resources so that the machining performances become more efficient, economically viable, and environment-friendly which are in general, conflicting in nature. In this investigation, surface finish, material removal rate, and tool wear are considered as responses, and cutting speed, feed, and depth of cut are considered as input factors for optimization of AISI 4140 alloy steel during CNC dry turning. In most of the previous researches, weights of the responses are considered arbitrarily. But, in this investigation, weighted ratio analysis is considered as an optimizing tool whose weights are determined by the Eigenvectors obtained from the principal component analysis of the experimental data. This weighted ratio analysis technique requires very few computations compared to others, and even it can be performed without a computer. Surface texture of the tool wear is measured using image processing of the wear surface. First, a metallurgical microscope captures an image of the sharp tool (unworn), and RGB values of the gray scale image are noted, which acts as a reference. After machining, RGB values of the wear surface from the gray scale image are noted. Comparing these two RGB values, the amount of flank wear of the tool is determined. It is observed that the error is within 10% and therefore it is accepted.

Obtaining the optimum surface finish is one of the key factors in machining operations. For this purpose, engineers apply a set of machining parameters to obtain the desired surface quality. On the other hand, tool faces wear during machining operation that itself affects the surface quality of machined surface. Therefore, tool wear and surface finish of machined workpiece should be related to each other. In this research, we employ fractal analysis in order to investigate the correlation between variations of complex structure of machined surface and tool wear in turning operation. In fact, we changed the machining parameters between different experiments and investigated how the machined surface is correlated with the tool wear. Based on the obtained results, we can see the correlation between the complexity of machined surface and tool wear by increasing the depth of cut, spindle speed and feed rate in different experiments. The method of analysis employed in this research can be widely applied to other machining operations in order to find the correlation between the surface quality of machined surface and tool wear.

Tool wear is an important issue that happens in all machining operations when the tool exerts forces on the workpiece. Therefore, engineers should choose the optimum values for machining parameters and conditions to reduce the amount of tool wear and increase its life. Machine vibration is one of the factors that highly affects tool wear. Since both tool wear and machine vibration signal have complex structures, in this research we employ fractal theory to find out their relation. In this paper, we analyze the relation between tool wear and machine vibration signal in different experiments where the depth of cut, feed rate and spindle speed change. The obtained results showed that tool wear and machine vibration signal are related to each other in case of variations of depth of cut and feed rate in different experiments, where both fractal structures get more complex by the increment of these machining parameters. The obtained method of analysis in this research can be potentially applied to other machining operations in order to link the machine vibration to the structure of tool wear.

The present work aims at numerical approximation in combination with experimental validation of some of the important performance measures in turning of commercially pure titanium (CP-Ti) with uncoated carbide inserts. A three-dimensional (3D) finite element model was developed based on Lagrangian criterion. Simulation of the turning operation was performed using DEFORM 3D software in order to approximate the responses viz. feed force ($F_x)$, radial force ($F_y)$, tangential force ($F_z)$, flank wear (Vb) and machining temperature ($T_m)$. Usui’s tool wear model was used to predict the flank wear. Morphology of the free and back surfaces of the chips was examined under a field emission electron microscope (FESEM). Turning experiments were carried out on a heavy duty lathe equipped with a 3D dynamometer. Secondly, a quadratic model was acquired for all the aforementioned quality characteristics using response surface methodology (RSM). Analysis of variance (ANOVA) test was performed to confirm the adequacy of the developed quadratic model. The results obtained from simulation and quadratic model were compared with the experimental data sets. Finally, an error analysis was done to determine the percentage inaccuracy of both the models. The percentage error for all the turning responses, was observed within 6% which showed the satisfactoriness of the proposed approximation tools. However, the simulation model exhibited lower prediction error when compared with the quadratic counterpart.

This work investigates the influence of tool surface area (TSA) on the average surface roughness ($R_a)$, tool wear rate (TWR) and material removal rate (MRR) in the micro-electrical discharge machining ($\mu$EDM). The effects of three different TSAs were investigated at three different discharge energy settings. It was observed that the TSA had substantial influence on $\mu$EDM performance owing to scaling effect. Therefore, the low-frequency workpiece vibration was applied to improve the $\mu$EDM performance. The surface topography of machined surfaces was examined using scanning electron microscopy to disclose the effect of TSA as well as vibration frequency on $\mu$EDMed surfaces.

The monitoring of tool wear state by the texture of the turning surfaces is studied in this paper. A microscopic image acquisition system was established to capture the images from turning surface and tool flank, then the relation between turning surface texture and tool flank wear state was studied based on Fractional Brown motion model. Specifically, logarithm power spectrum (LPS) of each surface texture image was earned by using the Fourier transform, five sets of data was selected from the LPS on five different directions (namely, X-axis direction, 30° direction, 45°direction, 60° direction and Y-axis direction), fit-slope and fractal dimension of earned data were calculated by linear fitting method. Finally, fractal dimension on X-axis direction, found to be highly correlated with the trend of flank wear, can be regarded as the texture feature parameter of tool wear state monitoring.

In this paper, the effect of cutting parameters during micromilling on surface finish and material removal rate is presented. Inconel 718 alloy and high-speed steel micro end mill are used as work material and cutting tool, respectively. High-speed steel end mill of 1 mm diameter is subjected to cryogenic treatment. Machining studies are performed on Inconel alloy using untreated and cryogenic treated cutters. The milling tests are conducted at three different values of feed rate, cutting speed and depth of cut. Also, tool wear, microstructure and microhardness of different treated and untreated end mill are investigated and discussed in detail. The results showed that cryogenic treatment significantly improved the tool wear. The surface finish produced on machining the work-piece is better with the cryogenic treated tools than when compared with the untreated tools. The material removal rate is better with the cryogenic treated tools than when compared with the untreated tools. Improvement in tool life was up to 53.16% for Inconel 718 material when machined with cryogenically treated micro end mill.

The surface roughness is a crucial factor in machining methods. The most effective factors on surface roughness are feed rate and tool nose radius. Due to the many advantages of wiper (multi-nose radius) inserts, their importance and use has been increasing recently. The purpose of this paper is to investigate the effect of wiper inserts on surface roughness and tool wear. In this study, conventional inserts and wiper inserts were experimentally compared separately in milling and turning operations. Compared to conventional inserts, the surface roughness values obtained using wiper inserts improved by 33% in turning operations and approximately 40% in milling operations. It was observed that the production time in the turning process was reduced by about 25% in the case of using wiper inserts compared to the use of conventional inserts. In milling, this ratio was determined to be approximately 43% due to the fact that it has multiple cutting edge. It has been observed that the use of wiper inserts in machining methods creates a significant time and cost saving advantage.