2026· Materials Research Proceedings· Vol 71, pp. 49-55· 0 citations
TL;DR
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.
Abstract
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.
Industry 4.0 integrates Artificial Intelligence (AI), Industrial Internet of Things (IIoT), cloud computing, edge computing, and cyber-physical systems to enable intelligent and automated manufacturing. Unlike traditional rule-based automation, AI-driven process optimization enables predictive decision-making, adaptive control, and continuous learning in dynamic production environments. This paper proposes an AI-enabled process optimization framework that combines real-time sensor data, predictive analytics, machine learning, reinforcement learning, optimization algorithms, and closed-loop feedback to improve manufacturing performance. The framework predicts equipment failures, detects process anomalies, optimizes production schedules, enhances resource utilization, and reduces energy consumption. Performance is evaluated using metrics such as production efficiency, cycle time, defect rate, machine utilization, predictive maintenance accuracy, throughput, energy efficiency, and operational cost. The proposed framework provides a scalable and intelligent solution for Industry 4.0 and Industry 5.0 manufacturing, improving productivity, sustainability, operational resilience, and decision-making in automated production systems.
N. Wirth· International Journal of Int...· 0 citations
Abstract. The fact that operational costs and the environmental impact are increasing is what has made energy consumption in manufacturing systems a serious issue. In this paper, a machine learning (ML)-based predictive control model is introduced to enhance energy efficiency in the contemporary manufacturing settings. The offered solution combines predictive models based on data and Model Predictive Control (MPC) to optimize the performance of the systems in real time. Machine learning algorithms are used to predict the energy demand, process dynamics, and disturbances, as well as to make decisions proactively. Industrial case studies confirm the validity of the framework, showing great progress in terms of energy efficiency, productivity, and stability of operations.
Prabhakara Rao Kapula· Materials Research Proceedin...· 0 citations
Smart manufacturing analytics (sma) is a key component of industry 4.0 that combines the industrial internet of things (iiot), artificial intelligence (ai), machine learning (ml), cloud and edge computing, and big data analytics to improve manufacturing processes. It continuously collects and analyzes real-time data from sensors, machines, robots, and production systems to support intelligent decision-making.sma enables predictive maintenance, fault detection, quality control, energy optimization, and production forecasting, leading to higher productivity, reduced downtime, improved product quality, and lower operational costs. By integrating iiot with advanced analytics, smart manufacturing analytics supports the development of intelligent, autonomous, and sustainable manufacturing systems for the next generation of smart factories.
Iyengar P.K· International Journal of Int...· 0 citations
Abstract. The fast development of Industry 4.0 has also resulted in the introduction of new digital technologies into manufacturing systems, which allow intelligent and autonomous work. One of these technologies is edge computing which has become a major enforcer of real-time data processing and low-latency decision-making. This paper is a complete discussion of intelligent control of industrial manufacturing processes with the help of edge-computing. The suggested framework combines edge devices, artificial intelligence (AI), and Industrial Internet of Things (IIoT) to facilitate the real-time monitoring, predictive control, and fault detection. This paper addresses system architecture, control strategies, applications, benefits and challenges. The findings emphasize that edge-enabled intelligent control can be used to greatly increase the responsiveness of a system, decrease the latency, and increase the overall operational efficiency.
K. Vijayakumar· Materials Research Proceedin...· 0 citations
The evolution of Industry 4.0 has brought forth an increasing demand for flexibility, adaptability, and intelligence in manufacturing systems. Modular smart manufacturing cells, with their inherent reconfigurability, are becoming essential components in modern production environments. This paper presents an AI-based framework for dynamic reconfiguration of such cells, enabling rapid adaptation to changing production demands, equipment failures, and optimization goals. Leveraging machine learning and real-time data analytics, the proposed system autonomously identifies optimal reconfiguration strategies with minimal human intervention. A case study is presented to validate the framework, demonstrating significant improvements in operational efficiency and system responsiveness. The results highlight the transformative potential of AI in achieving truly autonomous and resilient manufacturing systems.
Mohammed Asif Khan, Shalini Gupta· International Journal of Mac...· 0 citations
: 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
