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Aluminium is a noncorrosive, lightweight material used to fabricate parts for the aerospace, automobile, and construction industries. Due to the low-temperature resistance, more heat is generated. At the same time, in machining, tremendous efforts are taken to keep friction and chatter to a minimum and to attain better quality and perfect output, and also more attention is required while selecting the machining process parameters. Spindle speed, rate of feed, radial and axial depth of cut, and radial rake angle of the tool are the parameters utilized to machine aluminium 6063 using the HSS tool on CNC milling to estimate spindle and worktable vibration using a prediction model. In this study, the design of the experiment of the response surface methodology approach is used to create a second-order statistical equation for experimentation with the Design-Expert v12 software. The performance characteristics are analyzed using the ANOVA method. The spindle speed achieved the lowest vibration between 2000 and 3000 rpm. Next, axial and radial depths were the most vibration-affecting parameter compared to the rate of feed and radial rake angle of the tool. To find the best feasible response, the nondominant sorting genetic algorithm II (NSGA II) approach was trained and tested using MATLAB software. Using a Pareto-optimal technique, the optimum worktable vibration ranged from 0.00284 to 0.00165 mm/s2, whereas the spindle vibration ranged from 0.02404 to 0.01336 mm/s2. The predicted values were found to be in an excellent argument when Pareto-optimal solutions are used. [ABSTRACT FROM AUTHOR]

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design-Expert V12 software. The second-order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software's ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R-squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable. [ABSTRACT FROM AUTHOR]

This study's objective is to review the literature on the environmental impact of the additive manufacturing process. When this new manufacturing technology is employed, it aims to create a healthy environment free of pollutants. The work is motivated by the lack of universal guidelines on new design approaches, the classification of manufacturing materials, and processes that address environmental concerns. Using additive manufacturing over traditional subtractive technologies may result in considerable material and energy resource savings, especially if the component is appropriately designed for manufacture. In this scenario, additive manufacturing, regarded as a potential breakthrough innovation, has grown in popularity in producing parts with complex geometry. AM encourages constant product development and flexible modifications that enable stakeholders to create better products faster. This study examines the state-of-the-art essentials of the fast-expanding manufacturing technique known as additive manufacturing (or 3D printing) and compares the environmental impact caused due to environmental issues. With increasing pressure on firms to provide transparency in their product sourcing and manufacturing processes, sustainability is no longer a distant goal but a strategic requirement. Manufacturers must also pay particular attention to their products' total energy usage and overall environmental impact. [ABSTRACT FROM AUTHOR]

Aluminum alloy is the second most abundant metal on Earth, known for its wide range of utilization in commercial goods due to its heat capacity and tensile strength. This study examines the effect of nose radius on the turning process. Further, it explores the implications of cutting parameters such as the cutting speed, the rate of feed, the cutting depth, and the nose radius of the tool. The trials were carried out with an Al 6061 workpiece and an Al2O3-coated carbide tool as the cutter, utilizing the response surface methodology. A mathematical model was developed to investigate the performance characteristics of the turning operation using the analysis of variance method. The multi-response desirability function analysis combines individual desirability values to create a composite desirability value. The ideal parameter levels were determined using the composite desirability value, and the significant influence of parameters was assessed. The obtained optimum surface roughness and temperature parameters are at a cutting speed of 116.37 m/min, a rate of feed of 0.408 mm/rev, a cutting depth of 0.538 mm, and a tool nose radius of 0.20 mm. The related ideal surface roughness and temperature values are 0.374 µm and 27.439 °C. The optimal overall desirability value is 0.829, close to the target response. [ABSTRACT FROM AUTHOR]

Nanoparticles of titanium diboride (TiB2) coated with a conductive polymer and subjected to an oxidizing agent was carried out in this research. TiB2 nanoparticles coated with polypyrrole (PPy) were studied using XRD and TEM techniques. Nitrogen adsorption and desorption properties of nanoparticles are covered with modified polypyrrole to understand better the surface zone, structural features, and pore geometries of the nanoparticles. The water-based hazardous anionic Congo red (CR) dye was removed using polypyrrole-coated titanium diboride (PPy@TiB2) nanoparticles. Numerous cutting-edge experimental techniques, including FTIR, FE-SEM, EDXS, and element mapping analysis, were used to confirm the CR color's adhesion to the PPy@TiB2 nanoadsorbent study. While conducting batch experiments, coated TiB2 nanoadsorbents with polypyrrole enhanced the adsorption behaviour. One of the factors evaluated in the adsorption tests was pH; another was contact time, and a third was dose. At pH = 4 , 98.75% of Congo red dye was detached using 60 mg of PPy-coated TiB2 nanoadsorbent. Several sorption-desorption cycles were performed on this nanoadsorbent to determine its reusability. An excellent adsorption capacity for water treatment is reported in PPy-coated TiB2 nanoadsorbent. [ABSTRACT FROM AUTHOR]

Because aluminium is a lightweight and low-density material, its alloys, such as Al 6061 alloy, are extensively used in numerous automobile, defense, and aviation components. This study aims to develop a predictive model to investigate the impact of tool nose radius on the CNC turning process of Al 6061 alloy and better recognize the implications of operating machining considering cutting speed, rate of feed, cutting depth, and tool nose radius. The trials were carried out by using the response surface methodology (RSM), with an Al2O3 coated carbide tool as the cutter and an Al 6061 workpiece as the material. A mathematical model of the second-order was created. The analysis of variance (ANOVA) approach was used to analyze the performance characteristics of the turning operation. Individual desirability values from the desirability function analysis for the multi-responses are used to construct a composite desirability value. The ideal parameter levels were determined by using the composite desirability value, and the significant impact of parameters was assessed by using the analysis of variance. The minimum temperature attained at the machining parameters are 98.0 m/min cutting speed, 0.26 mm/rev rate of feed, 0.893 mm cutting depth, and 0.84 mm tool nose radius. The best total desirability value is 23.615 °C, indicating that the experimental results are close to the predicted values. [ABSTRACT FROM AUTHOR]

The composite was made using the stir cast manufacturing method. Many parameters, like stirring speed, stirring time, ZrO2% reinforcement, and cast temperature, are evaluated in a Taguchi experimental design to see how they affected the composite properties. In terms of composite properties, ZrO2% reinforcement and the stir speed have the most significant impact. There were 25.02% gains in ultimate tensile strength and hardness, as well as a decrease in composite wear loss, when the optimal stir casting parameters were used compared to the initial stir casting settings. To get insight into the process and the qualities of the composite, the hot-pressing parameters were studied. Pressure, followed by temperature, is the most critical factor in determining the properties of composites. When a hot-pressing setting was determined to reduce the wear loss by a significant 39.3%, it was deemed perfect by the superranking concept. Under ideal conditions, hot-pressing procedures reduced wear loss by 40.8% while boosting ultimate tensile strength and hardness by 19.83% and 9.6%, respectively. The resulting microstructures and worn surface morphologies from stir casting and hot pressing show vastly different properties. [ABSTRACT FROM AUTHOR]

The microstructure and mechanical properties of an MMC based on AA 7075 and strengthened through silicon carbide (SiC) as well as boron carbide (B4C) elements were studied. The (SiC + B4C) combination was used in various weight percentages of 4, 8, 12, and 16% to create the hybrid composites utilizing the traditional stir casting procedure. XRD and SEM measurements were used to investigate the dispersion of the reinforced particles. For example, microhardness, impact strength, and ultimate tensile strength were measured on hybrid composites at room temperature. The density and porosity of the materials were also studied. The researchers found that increasing the weight percentage of the (SiC + B4C) mixture resulted in a small drop in % elongation. However, hybrid composites comprising 16% (SiC + B4C) weight reduction showed some decrease in hardness and tensile strength. Equated to unreinforced alloys, the hardness and tensile strength of hybrid composites rise by 8% and 21%, respectively. Reinforcement also resulted in a decrease in impact strength and density, as well as an increase in porosity. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the effect of the machining parameters and tool geometry. The turning operation is carried out as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise of aluminium-6061 as a cutting material and Al2O3 coated carbide tool is used as a cutting tool for turning operation. The ANOVA analysis is used to measure the performance quality and mathematical model is developed. The values of probability >(F) is less than 0.05 indicates, the model conditions are significant. The cutting speed is the most influencing parameters compared to other parameters. For the optimum machining parameters leading to temperature rise, the Artificial Neural Network (ANN) model is trained and tested using MAT Lab software. The ANN recommends best minimum predicted value of temperature rise. The confirmatory analysis results, the predicted values were found to be in commendable agreement with the experimental values. [ABSTRACT FROM AUTHOR]

Due to exclusive universal value and usefulness, the combination of good mechanical properties and manufacturing characteristics, Stainless steel is an indispensable tool for design engineers to design components. In oil and gas companies, power plants such as nuclear and thermal, equipment's used in chemical processing industries: such as heat exchangers, seawater processing industries, Pipeline systems, face an incredible and exceptional challenge and the most important one is the reduction of thickness due to corrosion. In order to overcome this complexity, researchers developed a metal called duplex stainless steel (DSS). DSS is a mixture of Chromium - Nickel - Molybdenum - Ferric alloys that consists of an equal quantity of Face Centred Cubic (FCC) - austenite and Body Centred Cubic (BCC) - ferrite grains. DSS is designed to provide improved corrosion resistance, primarily stress corrosion and chloride pitting corrosion and superior resistance to other standard austenitic stainless steels. The DSS material is very difficult to perform machining operations due to high austenite, nitrogen content, alloy composition, high strength, work hardening rate and toughness. High hardness requires high cutting force which tends to reduce machinability characteristics such as tool wear, surface finish, low MRR, etc. This review article provides an overview of the research conducted during last one decade by the researchers and the optimization methods used to examine the machinability characteristics of DSS to predict surface unevenness wear in tool, machinability, MRR and chip volume ratio. Furthermore, this article indicates an efficient means of machining behavior, future scope and the fruitful methodology for the successful machining of duplex stainless steel. [ABSTRACT FROM AUTHOR]