Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same.

Artificial intelligence has played a potential role in present technological advancements. In terms of additive manufacturing or 3D printing techniques, computational AI models and algorithms such as artificial neural network, genetic algorithms, evolutionary algorithms, conventional machine learning techniques like decision tree, Naïve Bayes, K nearest neighbours, support vector machine, and ensemble methods including random forest, etc., has shown incredible results in the past few years. The applications of artificial intelligence in manufacturing are rapidly influencing most of the factors such as process optimization, material property prediction, determining the probability of product failure, real-time monitoring of processes, secure remote customer interactions, feature automation, material tuning, design feature recommendation, precise analysis, quality control/enhancement, or dynamic system modelling. Recent research in the field of VAT photopolymerization indicates that the creation of complex, versatile material systems with adaptable mechanical, chemical, and optical properties via the high-resolution processes includes a variety of 3D printing technologies, like stereolithography, digital illumination processing, and continuous liquid interface production. It has a compelling future in the last industrial revolution, Industry 4.0. This review compiles the evolution, current trends, open issues, and future computational AI models in 3D-printing VAT photopolymerization. Possibilities, prospects, and projects are well discussed to understand the significance of this technology.

Any metal surface’s usefulness is essential in various applications such as machining and welding and aerospace and aerodynamic applications. There is a great deal of wear in metals, used widely in machines and appliances. The gradual loss of the upper metal layers in all metal parts is inevitable over the machine or component’s lifetime. Artificial intelligence implementations and computational models are being studied to evaluate different metals’ tribological behavior, as technological progress has been made in this field. Different neural networks were used for different metals. They are classified in this paper, together with a description of their benefits and inconveniences and an overview and use of the different types of wear. Artificial intelligence is a relatively new term that uses mechanical engineering. There is still no scientific progress to examine various metal wear cases and compare AI and computational models’ accuracy in wear behavior.

Develop different nutritive recipes using chikoo and kiwi peel powder and performe sensory evaluation