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.

Recently, the urgency of improved machining performance and environmental sustainability has forced the manufacturer to seek for alternative cooling and lubricating agent/technique such as nano-fluid (NF)-assisted minimum quantity lubrication (MQL). In this context, the performances of aluminum oxide (Al₂O₃), molybdenum disulfide (MoS₂) and graphite (C) NF-impinged MQL in turning of Ti alloy (grade II) using CBN tool were evaluated regarding the cutting force, cutting temperature and surface roughness. The cutting speed, feed rate, approaching angle and cutting conditions (i.e., NFs) were oriented following the Box–Behnken design-of-experiment. The experimental results showed that the graphite NF, compared to Al₂O₃ and MoS₂, revealed the lowest cutting force, temperature and roughness. Moreover, it is evident from SEM images that graphite NF revealed a smoother machined surface and tool profile. This smooth tool and workpiece surface profile can be accredited to graphite’s role as a nano-lubricant and its breaking ability into smaller NFs under pressure. To make the study complete, the adaptive neuro-fuzzy inference system (ANFIS) was employed to predict, the response surface methodology (RSM) was used to mathematically model, and the composite desirability approach (CDA) was used to optimize the responses. A good agreement between the experimental and modeled observations was found; however, the ANFIS outperformed the RSM. Moreover, the analysis of variance exhibited that the cutting force and temperature were primarily influenced by the cutting speed and the surface roughness was afflicted mostly by the feed.