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Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi's L27 orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use. [ABSTRACT FROM AUTHOR]

From last two decades, plant fiber reinforced polymer/polyester composites have been effectively used in structural and automotive applications. Researchers and manufacturers are looking forward for an effective utilization of these composites. However, despite the outstanding properties in terms of load bearing capacity and environmental sustainability of plant fibers the uptake of these composites are limited due to its poor machinability characteristics. Hence in this paper, Taguchi based fuzzy logic model for the optimization and prediction of process output variable such as surface roughness during Abrasive Water Jet Machining (AWJM) of new class of plant fiber reinforced polyester composites i.e., Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites has been explored. Initially machining experiments has been carried out using L27 orthogonal array obtained from Taguchi Design of Experiments (TDOE). Finally, Taguchi based fuzzy logic model has been developed for optimisation and prediction of surface roughness. From the extensive experimentation using TDOE it was observed that the optimum cutting conditions for obtaining minimum surface roughness value, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and Abrasive Size:200 microns. Further from FLM, it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size:100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size:200 microns the surface roughness values (3.25 microns).

Metal matrix composites (MMCs) have achieved significant attention in engineering applications because of their exceptional properties, like increased strength-to-weight ratiosand resistance to wear. However, their manufacturing processes pose challenges for industries, such as oxidation, porosity, and chemical reactions. To address these challenges, this study investigates the processing and sintering (500 °C) of Ti-6Al-4V-SiCp composites and their mechanical properties, particularly hardness, wear and frictional force using a statistical approach. The main objective of this research is to identify optimal processing conditions for Ti-6Al-4V-SiCp composites that yield maximum hardness, minimal wear and frictional force. Thisstudy varies three key parameters, namely compaction pressure (Ton/sq.inch), SiC (wt.%), and PVA binder (wt.%) using Taguchi's design of experiments (TDOE). Further, the response surface methodology (RSM) is used to develop second-order models to predict the output values under different processing conditions, by correlating with the values obtained from TDOE. The results indicate that the most significant influence on the output is exerted by SiC (wt.%), followed by PVA binder (wt.%) and compaction pressure (Ton/sq.inch). To achieve higher hardness with minimal wear and frictional force during processing, SiCp (15 wt.%), compaction pressure (4 Ton/sq.inch), and PVA binder (3 wt.%) arerecommended. Finally, microstructural analysis using (SEM) scanning electron microscope images, optical macrographs and (AFM) atomic force microscopy revealed that the inclusion of 15 wt.% SiCp resulted in improved hardness, wear and frictional force compared to 20 wt.% SiCp. In conclusion, this study provides valuable insights into optimizing the processing parameters of Ti-6Al-4V-SiCp samples, enabling the production of materials with enhanced hardness and wear resistance. [ABSTRACT FROM AUTHOR]

Elastomers are the class of materials that are widely used in a variety of industrial, commercial, and consumer applications due to their unique mechanical properties, including high elasticity, high flexibility, and high resilience. However, there are many challenges in machining of elastomers such as poor surface finish, build up of heat, degradation of elastomers, etc. To overcome these challenges, cryogenic cooling assistance has been introduced as a means of improving the machinability of elastomers. This paper presents a soft computing approach for optimizing the surface roughness and cutting force during turning of elastomers under different lubrication conditions. Three types of elastomers, namely Nitrile Rubber (NBR), Polyurethane Rubber (PU), and Neoprene Rubber (CR), are studied, and a cryogenic fluid delivery system is employed to improve the machining process. Taguchi's L27 array is used to vary the input parameters, and a Back-Propagation Artificial Neural Network (BPANN) model is developed to predict the cutting force and surface roughness. The cutting force and surface roughness are analyzed under different cooling conditions, cutting speeds, feeds, and depths of cut for various elastomers. The results show that changes in cutting conditions significantly affect the cutting force and that the type of lubrication used affects the cutting force by altering the material's physical properties. Cutting force is significantly influenced by cutting conditions, and NBR requires the highest cutting force compared to PU and CR. Further, at a cutting speed of 55 m/min, a feed of 0.11 mm/rev, and a depth of cut of 0.25 mm, the cutting force for NBR (85.1 N), while for PU (75.1 N) and CR (80.3 N), respectively. Finally, with LN2 lubrication conditions, the Cutting Force decreased by 45% and Surface Roughness decreased by 16.9%. This study provides insights into the factors affecting the elastomer machining process, which can be useful for optimizing the machining process parameters and improving machining efficiency. [ABSTRACT FROM AUTHOR]

Titanium silicon carbide (Ti3SiC2) is a novel composite material that has found a multitude of uses in the aerodynamics, automobile, and marine industries due to its excellent properties such as high strength and modulus, high thermal and electrical conductivity, high melting point, excellent corrosion resistance, and high-temperature oxidation resistance. These properties are strongly associated with physical properties and microstructural features. Due to difficulties in the synthesis of this material, there have been very few investigations on the relationship between microstructure and physical characteristics of titanium silicon carbide composites processed through powder metallurgical process. However, the importance of thermal conductivity and electrical conductivity of titanium silicon carbide composites in various potential applications has led to keen attention from several researchers. Hence, in this paper, optimization, and prediction of process input parameters during processing under vacuum sintering for achieving maximum electrical and thermal conductivity of Ti-6Al-4V-SiC(15 Wt.%) has been presented. Using Taguchi's L9 Orthogonal Array, it has been observed that aging temperature (1150 ° +°C), aging time (four hours), heating rate (25 °C/min), and cooling rate (5 °C/min) result in optimum input parameters for achieving the highest electrical conductivity values during the processing of Ti-6Al-4V-SiCp composites. Further, for maximum thermal conductivity values during the processing of Ti-6Al-4V-SiCp composites, aging temperature (1150 °C), aging time (four hours), heating rate (5 °C/min), and cooling rate (5 °C/min) are preferred. A second-order response surface model generated can be effectively used for predicting the electrical conductivity and thermal conductivity during the processing of Ti-6Al-4V-SiCp composites with an accuracy of 99.28% (electrical conductivity) and 99.14% (thermal conductivity). By comparing the experimental results along with the results of the mathematical model and the BPANN model results for nine trials, it was observed that the estimated value is accurate for all tests with an error of 0.39% (electrical conductivity) and 0.48% (thermal conductivity). Further, from X-ray diffraction studies and microstructural analysis, it has been observed that aging at 1150 °C for four hours resulted in the formation of a ternary carbide phase of titanium silicon carbide (Ti3SiC2), which resulted in maximum electrical conductivity (4,260,000 Ω-1 m-1 conductivity (36.42 W/m·K) of the Ti-6Al-4V-SiC (15 Wt.%) composite specimen. [ABSTRACT FROM AUTHOR]

High performance lightweight structures made of metal matrix composites (MMCs) are in demand for application in variety of industries such as aircraft, spacecraft, automobile, marine, sports equipment, etc. However, uniform distribution of the reinforcement phase to improve the mechanical properties and quality of MMCs has been the challenge for the manufacturing industries. Hence, researchers are focusing on the development of traditional low-cost method of producing metal matrix composites. In the view of above facts, an attempt is made to study the processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites in this paper. Hence, this paper concentrates on experimentally identifying the effect of stir cast and spray forming processing techniques followed by hot pressing on micro hardness, compressive strength, and tensile strength using Taguchi's design of experiments for aluminum silicon matrix alloy reinforced with zirconium dioxide particulates. From the extensive experimentation on aluminum and silicon reinforced with the ZrO2 powder particulates, it was observed that there was an improvement in selected mechanical properties as the percentage of ZrO2 increased with 13 wt.% of silicon under spray forming processing technique compared to stir cast composites. This may be due to uniform distribution homogenous dispersion, larger work hardening rate, and structure of dislocation tangles around the ZrO2 particulates that occurred during spray forming processing technique. Further, results obtained from the interaction plot, contour plot, main effects plot, and analysis of variance (ANOVA) proved to be successful for identifying the optimum processing parameters for Si-Al alloy reinforced with zirconium dioxide particulate composites. Further, this paper also discusses wear study using pin on disc wear testing apparatus on spray forming processed aluminum and silicon (13.0 wt.%) alloy reinforced with the ZrO2 powder particulates based on Taguchi's design of experiments followed by second order model generation for wear using response surface methodology. Finally, electrode wear study of spray forming processed aluminum and silicon alloy reinforced with the ZrO2 powder particulates using electric discharge machining by varying peak current (A), pulse on time (μs), and pulse off time (μs) using brass, copper, and graphite as electrode material based on L27orthogonal array. The understanding gained from the design of experiments in this paper can be used to develop future guidelines for processing and characterization of Si-Al alloy reinforced with zirconium dioxide particulate composites. [ABSTRACT FROM AUTHOR]