Aug 2026· Industrial & Engineering
Chemistry Research· 0 citations· 56 references
TL;DR
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.
Abstract
The growing influence of artificial intelligence (AI) is reshaping process systems engineering (PSE) and industrial modeling and optimization. While data-driven methods excel in predictive maintenance, anomaly detection, and pattern recognition, they still face challenges in safety-critical, data-scarce, and extrapolation-prone environments. This paper argues that first-principles models (FPMs) (rooted in fundamental physics, chemistry, and engineering) remain essential for mission- and business-critical workflows in industrial automation, both in process design and operations. We highlight the enduring strengths of first-principles and examine hybrid paradigms that combine mechanistic rigor with data-driven machine learning (ML) and large language models (LLMs) to enhance adaptability and efficiency. Case studies across process design, advanced process control (APC), real-time optimization (RTO), production planning, production scheduling, and supply chain management illustrate the value of retaining first principles as a backbone for modeling and optimization. We conclude that the future lies in AI-enabled systems grounded in first principles and mathematical optimization, where hybrid intelligence integrates mechanistic rigor with data-driven insights to deliver smarter design, safer operations, and more sustainable processes.
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.
Sofija Milicic, A. Horr, S. Elgeti et al.· Processes· 0 citations
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
Baotong Chen, Lu Dai, Chuangjian Wang et al.· IEEE Access· 0 citations
This article delves deep into the confluence of simulation, ML, and statistics, showcasing how they synergize to improve engineering workflows and emphasizes that DCE is not just a technological advancement but a foundational strategy for next-generation engineering solutions.
Benjamin Scott· International Journal of Dat...· 0 citations
Abstract. Introduction of Artificial Intelligence (AI) to smart manufacturing has transformed conventional production systems as it allows optimization of processes in real-time. In this paper, the author 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. The offered system will help improve productivity, minimize the downtime, and optimize the product quality with the help of predictive analytics and dynamic optimization. It is experimentally proven that the efficiency, accuracy and operational performance greatly improve when using industrial datasets. The paper puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
Davinder Singh· Materials Research Proceedin...· 0 citations
The rapid evolution of electric vehicle (EV) manufacturing has intensified the need for highly
adaptive, efficient, and intelligent material handling systems capable of operating in dynamic
production environments. This review examines recent advances in the integration of Artificial
Intelligence (AI) with Autonomous Mobile Robots (AMRs) for real-time material flow optimization
in EV manufacturing ecosystems. It explores how AI-driven perception, decision-making, and
predictive analytics enhance the operational capabilities of AMRs, enabling them to respond
autonomously to fluctuating production demands, layout constraints, and supply chain
uncertainties. The study synthesizes developments in machine learning algorithms, reinforcement
learning, computer vision, and digital twin technologies that collectively enable real-time route
optimization, task allocation, congestion avoidance, and energy-efficient navigation within smart
factories. Particular attention is given to the role of edge computing and Industrial Internet of
Things (IIoT) architectures in facilitating low-latency communication and decentralized
intelligence, which are critical for real-time responsiveness. The review also evaluates system
level integration challenges, including interoperability with Manufacturing Execution Systems
(MES), scalability, cybersecurity risks, and safety compliance in human-robot collaborative
environments. Furthermore, it highlights emerging trends such as swarm intelligence, multi-agent
coordination, and adaptive scheduling frameworks that are redefining material flow strategies in
EV production lines. By consolidating current research and industrial practices, this paper
identifies key performance improvements in throughput, operational efficiency, and cost reduction
attributed to AI-AMR integration. It also outlines future research directions, including the
development of explainable AI models, resilient control architectures, and sustainable energy
aware robotic systems. Overall, this review provides a comprehensive foundation for
understanding how AI-enabled AMRs are transforming material flow optimization and shaping
the next generation of intelligent EV manufacturing systems.
Olasubomi Akanbi· International Journal of Eng...· 0 citations
The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming DevOps, enabling it to become a predictive, intelligent, and adaptive process across the software delivery lifecycle. This study aims to create an AI/ML based DevOps automation framework for deployment risk prediction, deployment optimization, rollback management, and continuous monitoring in a single CI/CD workflow. The framework’s data components are the build logs, deployment history, infrastructure monitoring, configuration repositories, incident records, and user feedback, which are then combined with DevOps data into a machine learning pipeline. The data is then preprocessed, combined, and processed by feature engineering to be split into training, validation, and testing sets. A Random Forest model is employed for deployment risk prediction, while an AI-driven decision engine uses predicted risk and operational conditions to support deployment optimization, resource allocation, automated rollback, and continuous monitoring. Experimental evaluation demonstrates a 50% reduction in average build time, a 60% reduction in deployment failure rate, a 50% reduction in rollback frequency, a 34% improvement in deployment risk prediction accuracy, and a 68% reduction in false positive rate. The findings demonstrate improved software delivery efficiency, reliability, operational stability, and decision-making.
Basheer Ahmedu Basha· The American Journal of Engi...· 0 citations
