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.
Abstract
Abstract. The proliferation of interlinked industrial devices at an exponential rate in the Industry 4.0 paradigm has produced volumes of real-time manufacturing information not seen before, creating a need and opportunity to establish intelligent process control. This article suggests a new hybrid artificial intelligence system of smart manufacturing, combining Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), ensemble tree-based classifiers, and a Proximal Policy Optimization (PPO)-based Reinforcement Learning (RL) agent in a four-layer system that includes data acquisition, AI processing, decision control, and feedback actuation. The framework is tested on the SECOM semiconductor manufacturing data with added synthetic CNC machining data, where a fault detection accuracy of 96.7, an Overall Equipment Effectiveness (OEE) of 91.8 and a defect rate is reduced by 6.8 to 1.1 compared to traditional Statistical Process Control (SPC) baselines. Latency of inference 44 ms meets hard real-time requirements in manufacturing. The superiority and generalizability of the proposed approach are supported by the results of comparative analysis against six state-of-the-art methods. The findings indicate that predictive modeling, computer vision and adaptive closed-loop control in a synergistic combination is a viable scalable route to intelligent manufacturing excellence.
: 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
Abstract. The intersection of artificial intelligence (AI) and sustainable manufacturing offers an innovative possibility to decrease industrial waste and, at the same time, improve the production yield. The current paper suggests a new multi-layer AI architecture including a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) neural network and a Multi-Objective Genetic Algorithm (MOGA) optimizer to control real-time process in green manufacturing setting. The architecture acts on the heterogeneous sensor streams, energy metering information, and process logs in a five-stage pipeline including data acquisition, preprocessing, AI inference, decision support and closed-loop feedback. Two industrial datasets (n = 18,400 samples and n = 14,200 samples) of semiconductor fabrication and automotive body stamping data (20192023) are experimentally validated. The proposed system attains a waste reduction of 34.7, a yield improvement of 17.1 and prediction RMSE of 1.72 as compared to the traditional machine learning baselines. The explainability analysis using SHAP reveals that the dominant process variables are the coolant temperature, spindle speed and feed rate. The findings affirm that the proposed hybrid AI architecture is more effective than the current approaches and provides a deployable and scalable system towards smart green manufacturing.
Irshadullah Asim Mohammed· Materials Research Proceedin...· 0 citations
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 many applications, frequently depend on manual inspection, statistical process control and rule-based decision-making, which may be inadequate for highly customised, high-speed and data-intensive production environments. The emergence of artificial intelligence (AI), machine learning (ML), deep learning (DL), computer vision, industrial Internet of Things (IIoT), edge computing and digital twins has created new opportunities for transforming quality control from a predominantly reactive activity into a predictive and autonomous manufacturing function. This paper examines the application of AI-driven quality control in modern manufacturing systems through a critical review of recent literature. The paper discusses AI-enabled visual inspection, predictive quality, process monitoring, anomaly detection, defect classification, intelligent metrology, predictive maintenance and closed-loop quality control. Particular attention is given to the integration of AI with Industry 4.0 technologies and the implications for production efficiency, waste reduction, process capability and decision-making. The paper also identifies major challenges, including data scarcity, model explainability, algorithmic generalisation, cybersecurity, computational requirements, system integration and workforce readiness. A conceptual framework for AI-driven quality control is proposed, linking data acquisition, intelligent analytics, quality prediction, decision support and corrective action. The paper concludes that AI should not be viewed simply as a replacement for human inspection but as an intelligent layer that augments engineering knowledge and enables manufacturing systems to anticipate, diagnose and correct quality problems. Future research should emphasise explainable, robust, resource-efficient and human-centred AI systems capable of operating reliably in real industrial environments.
Olusegun Olukayode Olaleye, E.J. Wilson· International Journal of Afr...· 0 citations
The transition from localised control to an AI-driven autonomous framework, supported by Digital Twins and Explainable AI, provides a transparent approach to modernising glass manufacturing and may reduce operational risks and environmental impacts, thereby supporting intelligent and sustainable industrial automation.
Omar Musazade, Stanislav Aghamatov· Journal of Engineering Resea...· 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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