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This study investigates the tool wear for turning Inconel-718 using the titanium nitride carbide tool inserts. This research work aims to compare the performance of processed images and acoustic waves used as an indirect technique for evaluating tool wear. The work targets to capture the tool wear and tool image after turning and acoustic wave during turning for each experimental run. The pixel area of the processed picture, the root-mean-square (RMS) of the acoustic wave, and the microscope tool wear of the tool maker were taken into consideration as output parameters for the change of operational parameters including feed, speed, and depth of cut. The performance of wear, pixel area, and RMS was compared using the Box Behnken method. Further, the correlation between the performance of tool wear, image processed pixel area, and RMS for the variation in input variable was obtained from interaction and main effects plots. The results demonstrated that at lower speeds (280rpm), lower feed rates (0.04mm/rev), and medium depth of cut (0.2mm), there was less wear, pixel area, and RMS. Wear, pixel area, and RMS have all decreased as a result of the tool and workpiece having less surface friction due to the reduced speed, feed, and medium depth of cut. From the analysis, it was also clear that the indirect evaluation of the wear can be successfully carried out using digital image pixel area and acoustic wave RMS for turning Inconel-718 using a titanium nitride-coated carbide tool.

In this paper, an experimental investigation of the machinability of Titanium grade 23 has been carried out using hybrid micro-textures on the cutting tool. The different types of micro-textures have been fabricated on the rake surface as well as on the flank surface of the cutting tool. The performance of textured tools with the presence of a chip breaker has been investigated. The selected patterns were vertical grooved rake face (VT-R), diagonal grooved rake face (DT-R), vertical grooved flank face (VT-F) and hybrid textured tools namely, vertical rake and flank (V+V), diagonal rake and vertical flank (D+V). A comparative study has been carried out to acknowledge the performance of textured tools while dry turning Titanium Grade 23 alloy. The machinability has been made to acknowledge the performance of textured tools with the variation of machining time in case of dry turning operation. The machinability criteria are investigated based on cutting forces, cutting temperature and tool wear mechanisms involved while machining. The performance of the cutting tool with DT-R and VT-F micro-texture is observed to be better in comparison to others. Innovative applications of micro-textured tools are for dry green machining without any environmental hazards and proper chip-flow control.

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.

The physical health of human beings and the environment will be negatively influenced by the improper use of cutting fluid, which is an important factor for achieving sustainable machining. Turning is a basic process of all machining operations. Using a minimum quantity lubrication (MQL) system is the best way for achieving sustainable turning process. Improvements made in the MQL system to achieve better performance of the turning process have been discussed in this paper. Search categories that have been used to carry out the study are MQL system with the conventional cutting fluid, MQL system with nano-fluid, MQL system with cryogenics and restructured MQL system. The advancements in MQL turning process published over a span of four most recent years from 2018 to 2021 have been chosen for this review. In this study, the details of experimentation such as materials used for the workpiece, cutting tool materials, environments for lubrication, machining parameters, MQL fluids and performance parameters such as surface roughness, tool wear, cutting force, cutting temperature, etc. have been critically considered. Results investigated from various articles mostly show that advanced MQL systems such as MQL system with nano-fluid, MQL system with cryogenics and restructured MQL system are better than the conventional MQL system in terms of producing better output performance.

The machining of Ti–6Al–4V alloy faces several confronts like generation of higher cutting temperature, fast tool wear, poor surface finish, higher tool vibration and chattering. Therefore, this research presents the detailed analysis of the surface roughness, tool flank wear, and amplitude of vibration and chip morphology under MQL enabled Ti–6Al–4V CNC machining. The experimental scheme is chosen as Taguchi L${}_{18}$ orthogonal array (OA) with cutting speed, feed and cutting depth considered as the input processing parameters. Further, WPCA optimization is implemented to evaluate the best combinations of input factors to get the optimal values of outputs.

The aluminum-based composites (AMCs) are known for a variety of functions like building, aerospace, automotive, marine, and aeronautical applications. In this research, Al-4032 alloy-based 6% SiC (by weight) composite has been fabricated using stir casting and the effects of prominent machining parameters on energy consumption and surface finish have been examined using carbide inserts in turning. Microstructures of as-cast specimens has been analyzed using the optical microscope, scanning electron microscopy, and energy-dispersive spectroscopy. The CNC turning has been performed at varying machining parameters like cutting speed, feed rate, and depth of cut, following an RSM-based design matrix. The desirability function approach has been employed to obtain the best combination of parameters for achieving the desired objectives. The experimental outcome demonstrates that the machined composite is considerably influenced by built-up edge (BUE) formation and interfacial bonding of particles. The result establishes that the inclusion of SiC in the Al-4032 matrix demonstrates improved mechanical properties and superior machined surface with the optimized turning operation.

This paper deals with experimental investigation on chip formation of machining of Monel K500 and its characteristics are studied. Two different nanocutting fluids namely nanoCuO and nanographenes are used for turning operation and the performance of the two cutting fluids is evaluated and the corresponding responses are recorded for the selection of better cutting fluid. This experiment is carried out with the help of SEM which picturizes the microstructure and surface distribution of the chips. The various output responses like chip thickness, chip compression ratio, and surface roughness are portrayed. The desirable effects on the properties of the machined material obtained by the variation of process parameters like cutting speed, cutting depth, and feed rate are analyzed. Results show that the thickness of chips (corresponding metal removal rate) is reduced considerably by 10–15% and the surface roughness is reduced by 20–30% when nanographene cutting fluids are used.

Computer simulation of industrial processes is an important alternative that may be used either to complement or to replace expensive experimental procedures associated with developing new parts or modifying existing process. For a metal cutting process, numerical simulations provide vital information about cutting forces, cutting temperatures, tooling and part distortion, etc. Since the early 1970s, FEA has been applied to simulate machining process. The development of this approach, its assumptions and techniques has been widely accepted. Nowadays, the manufacturing productivity even drives the community to the next level innovation through computer utilizations. A kinematic simulation of machining processes is one of many innovative CAE applications, especially beneficial to high volume production of automotive powertrain parts. In this paper, a generic force calculation method is introduced with a modified horsepower correction factor. An example of sizing milling force, milling paths and proper milling parameters is provided by utilizing the methodology. This paper will also discuss and propose how the manufacturing industry uses this resourceful tool. Applications of the methodology would empower product and manufacturing engineers to make intelligent and cost effective decisions.

Evolutionary computation is one of the important problems solving method frequently used by the researchers. The choice of an algorithm to optimize the problem is determined by some sort of reliability of the researcher with that technique. To overcome the limitations in individual algorithms and to achieve synergic effects, fusion or hybridization of two or more algorithms is carried out. Hybrid algorithms have gained popularity because there is no evidence that a universal optimal strategy exists for solving optimization problems. In this work, a hybrid algorithm called hybrid genetic simulated swarm (HGSS) algorithm is proposed to optimize the parameters of multi-pass turning operation. The HGSS algorithm is a fusion of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The objectives of this work are (i) to explore and exploit the problem search space through hybridization, (ii) to justify that proficient hybridization of evolutionary algorithms (EAs) will yield an efficient means to solve the optimization problems. In this work, the EAs such as GA, SA and PSO are also applied to optimize parameters and results are compared with HGSS. The results of the proposed work HGSS are very effective than other algorithms.

Multi-objective optimization method is used to simultaneously maximize and minimize the various criteria involved in complex industrial problems. In the present work, the optimum combination of cutting parameters is estimated in the turning of EN25 steel with coated carbide tools by performing desirability function analysis and utility concept. The experiments were designed as per L18 Taguchi mixed level orthogonal array with each trial performed under different conditions. These methods are employed for minimization of cutting force, surface roughness and maximization of material removal rate. The optimized results are compared and utility concept gave good combination of input and output parameters. Finally, Analysis of Variance (ANOVA) on overall desirability and utility value was employed to identify the relative significance of factors in terms of their percentage contribution to the responses.

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.

Nowadays, industrialists, especially those in the automobile and aeronautical transport fields, seek to lighten the weight of different product components by developing new materials lighter than those usually used or by replacing some massive parts with thin-walled hollow parts. This lightening operation is carried out in order to reduce the energy consumption of the manufactured products while guaranteeing optimal mechanical properties of the components and increasing quality and productivity. To achieve these objectives, some research centers have focused their work on the development and characterization of new light materials and some other centers have focused their work on the analysis and understanding of the encountered problems during the machining operation of thin-walled parts. Indeed, various studies have shown that the machining process of thin-walled parts differs from that of rigid parts. This difference comes from the dynamic behavior of the thin-walled parts which is different from that of the massive parts. Therefore, the purpose of this paper is to first highlight some of these problems through the measurement and analysis of the cutting forces and vibrations of tubular parts with different thicknesses in AU4G1T351 aluminum alloy during the turning process. The experimental results highlight that the dynamic behavior of turning process is governed by large radial deformations of the thin-walled workpieces and the influence of this behavior on the variations of the chip thickness and cutting forces is assumed to be preponderant. The second objective is to provide manufacturers with a practical solution to the encountered vibration problems by improving the structural damping of thin-walled parts by additional damping. It is found that the additional structural damping increases the stability of the cutting process and reduces considerably the vibrations amplitudes.

Duplex turning becomes an important metal cutting process due to unique features like higher productivity with better surface finish at lower specific energy and vibration. Such process requires two-cutting tools which are mounted parallelly and fed inward to cut the material from rotating surfaces. Such complex process needs modeling and optimization to analyze the effect of factors and identify the optimal cutting condition. This paper focuses to develop two models related to statistical and intelligent techniques especially responses surface methodology (RSM) and artificial neural network (ANN) for prediction analysis of duplex turning. Based on prediction potential, the ANN model is utilized to analyze the effect of various parameters (cutting speed, feed rate, primary depth-of-cut (DOC) and secondary-DOC on the responses as surface roughness and cutting forces (primary and secondary). Further, the parameters are optimized using Taguchi Methodology (TM) and experimentally validated. The results show that ANN model predicts the data with more precision than RSM model. Further, the optimal data are experimentally validated and significantly agreed with predicted data of ANN model with percentage error as 2.24%, 1.40% and 0.75% for surface roughness, cutting forces (primary and secondary), respectively.

In the present research, measurement of residual stress induced during turning and threading operations for the fabrication of two types of pin profiled friction stir processing/welding (FSP/FSW) tools, i.e. cylindrical profiled pin tool and cylindrical threaded profiled pin tool, is being dealt with. Workpiece was chosen to be H13 tool steel with a diameter of 22mm and 110mm length. Turning and threading was done on CNC machine tools using CNMG 12404-THM uncoated tungsten carbide cutting tool. For residual stress measurement of the workpieces, an XRD-based Pulsetec$\mu$-X360n portable residual stress analyzer setup was used. The experimental results show that the cylindrical pin profile tool had a compressive residual stress of $\sigma (x)=301$MPa and compressive residual shear stress of $\tau (xy)=60$MPa, while the cylindrical threaded pin profile tool had a compressive residual stress of $\sigma (x)=457$MPa (51.8% more) and compressive residual shear stress of $\tau (xy)=36$MPa (40% less). It has been concluded that due to threading operation on the cylindrical threaded pin profile, the value of residual stress is more in it, and since the stress is compressive in nature, it would have a better positive impact while doing FSP/FSW than that of the cylindrical profiled pin tool.

This paper presents an approach of empirical modeling of the machining process physical quantity with measurement uncertainty parameter included in the resulting power mathematical model coefficients. The proposed approach is presented through an example of modeling of the average temperature in turning like a methodology that is under the influence of various sources of measurement errors. The uncertainty budget accounts for the sources of errors of the experimental measuring system and the cutting process itself. This approach enables estimation of the reliability of the gained mathematical models and gives the possibility of identification and lowering of the main sources of errors.

Duplex turning (DT) is a novel concept of metal cutting where two tools are employed to cut the objects in lieu of single tool. It shows many benefits over conventional turning in terms of superior dynamic balancing, lower cutting forces and tool wears, better surface finish, reduction in vibration with additional support for workpiece. It is a complex method and the resulting experimental analysis becomes difficult and expensive. In such conditions, modeling techniques show their potential for parametric study, prediction of data for optimization and selection of optimal condition. Generally, soft computing-based Artificial Neural Network (ANN) is applied for modeling and prediction for complicated processes while Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows their potential for optimization of complex problems over Genetic Algorithm. Therefore, ANN and NSGA-II techniques are employed for modeling and optimization of DT process to minimize the surface roughness and cutting forces (primary and secondary). Finally, results reflect that ANN efficiently predicts the responses at different input combinations within training data set with absolute percentage errors as 2.55% for roughness, while 3.05% and 3.14% for cutting forces (primary and secondary), respectively. In the same way, optimized results also found within the range of acceptability with percentage errors as 2.57% for roughness, while 3.25% and 3.15% for primary and secondary forces, respectively.

The mechanical properties of Aluminum metal matrix composites (AMMCs) have made them a popular choice in various industries such as automotive, aerospace, and marine. However, their composition, which includes abrasive reinforcements, makes them difficult to machine using conventional techniques. So, unconventional machining processes play a vital role in the machining of these materials. Electrical discharge turning (EDT) is one of the configurations of electrical discharge machining (EDM) that is utilized to machine cylindrical-shaped workpieces. In this study, AMMC Al 7075/ZrO₂ was fabricated using the stir casting method. Further, investigated the effect of machining parameters of EDT, namely pulse Current (I), pulse on time (T-ON), and rotating speed (RPM), on Al 7075/ZrO₂ in terms of material removal rate (MRR), tool wear rate (TWR), and overcut (OC) using one parameter at a time (OPAT) method. The outcome of the experiment reveals that MRR, TWR, and OC are more impacted by pulse current than by rotational speed. A decrease in TWR was also found with an increase in pulse on time for all rotational speeds. The size of the craters and globules observed lower with 1400 RPM in the micrograph image.

Production engineering focuses on designing, optimizing, and managing manufacturing processes to produce goods efficiently. Turning is a machining process where a cutting tool removes material from a rotating workpiece to create cylindrical shapes. Key parameters include cutting speed, feed rate, and depth of cut. Surface roughness is a key challenge in turning, impacting product quality. Achieving the desired finish is crucial for tight tolerance and performance. Engineers use optimization techniques to minimize roughness. Advancements in tool materials and technology help address roughness challenges for improved efficiency in turning. Predictive models for surface roughness are vital for optimizing machining processes, ensuring quality, and enhancing performance. They guide decision-making, improve efficiency, and drive innovation in manufacturing. In this paper, 25 machine learning models have been used and optimized to accurately predict the surface roughness of the turning process of five previous studies on Titanium alloy. Resulting in the best performance with the lowest MSE was for the artificial neural network with 3 hidden layers: the first has 5 neurons, the second has 10 neurons and the last one has 5 neurons. The MSEs are 0.053072, 0.555763, 0.059667, 0.051867, and 0.554829 for the five studies, respectively.

The tunability of electrical switching behaviors in WO_x thin films were investigated in this paper. Electrical responses of the WO_x films were observed to be highly sensitive to the film thickness. As the film thickness increases from 50 to 100nm, the switching behavior changes from complementary resistive switching (CRS) to threshold switching (TS). A defect-related dynamic evolution of filament is responsible for the switching behavior. Such a controllable electrical switching can well broaden the application of the WO_x thin film.

This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350rpm, 0.37mm/rev feed rate, and 1mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452g/sec.