The results show that AI can greatly decrease the time spent on design iterations, increase the accuracy of predictions of process parameters, and allow in-situ defect detection with high accuracy.
Abstract
: The rapid adoption of artificial intelligence (AI) in the production sector has triggered a revolutionary change in the design, manufacturing, monitoring, and optimization. The paper is a critical analysis of AI-based additive manufacturing (AM) and smart manufacturing systems with an emphasis on design intelligence and process optimization, real-time quality control, and sustainable production. Based on recent literature, this paper considers the major AI methods, such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and physics-informed neural networks (PINNs) throughout the manufacturing lifecycle. The results show that AI can greatly decrease the time spent on design iterations, increase the accuracy of predictions of process parameters, and allow in-situ defect detection with high accuracy. Moreover, AI
A new hybrid artificial intelligence system of smart manufacturing is suggested, combining Long Short-Term Memory networks, Convolutional Neural Networks, Convolutional Neural Networks, ensemble tree-based classifiers, and a Proximal Policy Optimization-based Reinforcement Learning agent in a four-layer system that inc...
C. T· Materials Research Proceedin...· 0 citations
The convergence of additive manufacturing (AM) and artificial intelligence (AI) is poised to redefine the landscape of modern production; however, the literature remains fragmented across isolated applications, lacking a unified perspective on the engineering impact and practical deployment of these technologies. This...
The development of AI-enabled advisory systems for casting processes are presented, integrating singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) and hybrid simulation frameworks to support real-time process prediction and optimization.
Sofija Milicic, A. Horr, S. Elgeti et al.· Processes· 0 citations
Quality control is a fundamental function of manufacturing because product conformity, process stability, customer satisfaction and operational efficiency depend on the ability of manufacturers to detect and prevent deviations from specified requirements. Conventional quality-control practices, although effective in ma...
Olusegun Olukayode Olaleye, Emmanuel Okon Wilson· International Journal of Afr...· 0 citations
Smart manufacturing is undergoing a transformation due to the development of Generative Artificial Intelligence (GenAI), which introduces a new level of autonomy and data-driven decision-making in industrial settings. Unlike conventional prediction and classification type AI, GenAI can also be applied to develop optimi...
E. Fischer, Viktor Gulyás-Oldal, I. Gálóczi et al.· Veredas do Direito· 0 citations
This paper introduces an extended outline of the AI-based decision-making in manufacturing facilities with the application of real-time sensor data, machine learning, and adaptive control and puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
Davinder Singh· Materials Research Proceedin...· 0 citations
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