Ti-6Al-4V is an alloy that has a high strength-to-weight ratio. It is known as an alpha-beta titanium alloy with excellent corrosion resistance. This alloy has a wide range of applications, e.g., in the aerospace and biomedical industries. Examples of alpha stabilizers are aluminum, oxygen, nitrogen, and carbon, which are added to titanium. Examples of beta stabilizers are titanium–iron, titanium–chromium, and titanium–manganese. Despite the exceptional properties, the processing of this titanium alloy is challenging when using conventional methods as it is quite a hard and tough material. Nonconventional methods are required to create intricate and complex geometries, which are difficult with the traditional methods. The present study focused on machining Ti-6Al-4V using wire electrical discharge machining (WEDM) and conducting numerous experiments to establish the machining parameters. The optimal setting of the machining parameters was predicted using a multiresponse optimization technique. Experiments were planned using the response surface methodology (RSM) technique and analysis of variance (ANOVA) was used to determine the significance and contribution of the input parameters to changes in the output characteristics (cutting speed and surface roughness). The cutting speed obtained during the processing of the annealed titanium alloy using WEDM was quite large as compared to the cutting speed obtained in the case of processing the pure, quenched, and hardened titanium alloys using WEDM. The maximum cutting speed obtained while processing the annealed titanium alloy was 1.75 mm/min.

The use of polymer composite has been implemented since 3400 B.C, the very first known composite’s application is attributed to the Mesopotamians. These ancient people fabricated plywood with glued wood strips placed at various distinct angles and in the late 1930s glass fiber thin strands have been developed. Glass fibre polymer composites have a wide scope in various engineering structures submarines, spacecraft, airplanes, automobiles, sports, and many more, over traditional materials because of their superior properties including lightweight, high fracture toughness, corrosion, fatigue, wear & fire resistance, high strength to weight ratio, high modulus and low coefficient of expansion. Various technologies have been developed so far to create different types of polymer composites in accordance with their properties and applications. Glass fiber possesses better properties as great strength, better flexibility, stiffness, and chemical corrosion resistance. Glass fibers are generally in the form of cut-up strand, fabrics and mats. Every kind of glass fibers has different properties and has various applications as in polymer composites. The aim of this review paper is to provide updated technological insights regarding the evolution of composite, classification of gass fibre polymer composites, development methodology in contrast with various applications, advantages and limitations and their behavioral properties.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

The present study has investigated the role of the addition of TiC nanoparticles on the microstructural, mechanical, physical, and metallurgical properties of the Cu-TiC nanocomposites. A Powder metallurgy route was used to fabricate the samples. Cold compaction of powders was done at 480 MPa which was followed by sintering at 950°C. Wt.% of TiC was varied from 0% to 20%. A Pin-On-Disc tribometer was used to carry out the wear and friction tests. SEM and EDS techniques were used to explicate the morphology and microstructures of worn surfaces and to comprehend the underlying wear mechanism. The fabricated samples were investigated against the applied normal load (10, 20, and 30 N), sliding speed (0.5, 1.0, and 1.5 m/s), and sliding distance (900, 1800, and 2700 m). The results revealed that the Hardness and tensile strength were improved by 88.76% and 37.26% respectively due to the addition of TiC and were maximum for Cu-20%TiC while the relative density shows the reverse trend. Further, it was found that wear resistance and coefficient of friction were improved by 87.18% and 51.85% respectively as a function of nano-TiC content. The presence of oxide layers and mechanically mixed layers are detected from worn surface analysis which modulates the tribological behavior of the contact.