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.
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, 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. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative Horizontal Direct Chill (HDC) continuous casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications.
: 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
It is argued that first-principles models (FPMs) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations and hybrid intelligence integrates mechanistic rigor with data-driven insights to deliver smarter design, safer operations, and more sustainable processes.
Hongzhi Zhao, Shu Wang, Salvador I. Pérez-Uresti et al.· Industrial & Engineering...· 0 citations
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 includes data acquisition, AI processing, decision control, and feedback actuation.
C. T· Materials Research Proceedin...· 0 citations
The article concludes with a forward-looking roadmap for the future of AI in chemical and materials engineering, defined by a paradigm shift from simply finding new materials to creating viable, economical and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation.
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 optimized production plans, adjust production plans to changes, generate synthetic engineering data, intelligent design options, and real-time production recommendations. The review provides an in-depth overview of the use of GenAI in contemporary manufacturing systems such as process optimization, predictive maintenance, quality assurance, digital twins, intelligent robotics, supply chain resilience, and autonomous production control. It covers the most recent developments in generative models, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models, as well as how these models are used in manufacturing process planning, defect detection, scheduling optimization and human-machine collaboration. This also encompasses the relationship between GenAI and Industrial Internet of Things (IIoT), cyber-physical systems, cloud-edge computing, and Industry 5.0, which will be used to create a self-adaptive manufacturing environment. Technical, ethical, and organizational issues, including data quality, model interpretable, cybersecurity, computational burden, workforce adaptation, and regulatory compliance issues, are explored. Lastly, the review spills out research gaps in this rapidly evolving technology field and provides directions for developing trustworthy, explainable and sustainable GenAI-enabled manufacturing systems.
Fischer Erik Krisztián, Viktor Gulyás-Oldal, Imre Gálóczi et al.· Veredas do Direito· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
