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The present work investigates the method of depositing a ceramic coating on the surface of aluminium by means of electrical discharge coating (EDC) in EDM. The present study makes use of powder metallurgy (P/M) green compacts made of titanium, boron carbide and aluminum (Ti+B 4 C+Al) powder as the EDM tool for surface modification of aluminum workpieces. EDM process was carried out with different tool parameters like composition of the electrode material, compaction pressure of the green compacts and different settings of the process variables like peak current and pulse duration setting.Responses observed were material deposition rate (MDR), tool wear rate (TWR) and average layer thickness (LT). Experiments were designed and carried out using Taguchi L 18 orthogonal array. The most influential parameter for responses MDR, TWR and LT was found to be peak current (I p ) with a percentage contribution of 60.72%, 59.52% and 42.09%, respectively. In addition, various other characterization techniques such as optical microscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectrum (EDX) analysis were performed in order to judge different attributes of the deposited coating.

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Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.