This work is focused on the fabrication and machining of Aluminium Alloy (AA) 7075, AA 7075/B4C composite and AA 7075/B4C/RHA hybrid composite. The composites were fabricated using stir casting. The highest hardness value was reported on AA 7075-5 B4C-5 RHA hybrid composite. It is 72% higher than pure AA 7075. Scanning electron microscopy was used to study the surfaces of fabricated and machined surfaces of the composites. The theoretical density was calculated by rule of mixing and actual density was calculated by Archimedes principle. Energy dispersive spectroscopy analysis was used to confirm the distribution of reinforcements in AA7075. Turning of AA 7075, AA 7075/B4C composite and AA 7075/B4C/RHA hybrid composites were studied using by plain carbide inserts. The various responses such as Surface Roughness (SR), Material Removal Rate (MRR) and Tool Wear (TW) were studied. The analysis and optimization of the experimental results were carried out using response surface methodology. The feed rate is considered as most influencing parameter for SR and MRR. Speed is considered as most influencing factor for tool wear and the Wt. % of reinforcement is considered as least influencing factor for MRR.

Defects in the welds degrade the quality of the weld. Weld defect identification is a challenging task in the industry because of the wide range of weld imperfections. Weld defect detection using radiographic images is an effective technique for achieving good weld quality in shipbuilding and aerospace applications. Foreign inclusions, cracks and pores are examples of welding joint imperfections. Several appropriate computer-based image processing techniques have made the detection of weld defects possible. It is challenging because weld imperfection can show various sizes, shapes, contrasts and locations in radiography images. The accuracy of this inspection process is more dependent on various external factors and is also time-consuming. Automatic weld defect detection is needed by analyzing the images obtained directly from digital radiographic systems. This paper uses a unique image-based approach to a small batch of x-ray imaging datasets to investigate a potential solution for weld defect identification. This article compares a deep learning network’s performance for various parameter and hyper-parameter combinations. Also it compares the traditional approaches of defect detection using manual inspection method, feature-based defect identification, and finally deep - learning based approach on several types of weld defects in various industrial applications. This comparative analysis concludes that deep learning-based approaches have achieved more accuracy as compared to conventional techniques. This research paper also highlights a few challenges and future directions in welding area.

Squeeze casting was used to prepare aluminium alloy-6061/titanium diboride (TiB₂) to create a composite with varied TiB₂ quantities. The composite's metallographic structure, tensile strength, and hardness have been explored. The tensile strength and hardness of produced metal matrix composites increased when a particle of TiB₂ was increased from 5 to 10 vol.%. This study uses electrical discharge machining (EDM) to investigate the output response tool wear rate (TWR). Variables in EDM operation were investigated, such as current, pulse on time, and voltage gap. The experiments were designed using the Box–Behnken strategy. Statistical approaches were used to analyse the experiments. At ideal settings for TiB₂ concentrations of 5 and 10 vol.%, TWR was 0.2146 and 0.1749 mm³ min^-1 and surface roughness was 2.47 and 3.03 µm, respectively. TiB₂ is utilized in automobile disc brakes, an industry where components slide against each other. The alu­minium alloy-6061/titanium diboride has many applications as a biomaterial and is a good prospect.