This study proposes an AI-driven adaptive control strategy to enhance the learning, adaptability, and autonomous performance of robotic ‎manipulators in dynamic and unstructured industrial environments. Moving beyond the limitations of conventional model-based controllers, ‎the research introduces a self-learning framework that integrates real-time sensor data from LiDAR and stereo vision cameras. This data ‎continuously informs and optimizes the robot’s motion trajectories in both simulated and real-world tasks. The system’s core innovation lies ‎in combining Reinforcement Learning (RL) with Deep Neural Networks (DNNs) for adaptive trajectory planning and error compensation. ‎Specifically, the Proximal Policy Optimization (PPO) algorithm is employed to fine-tune control strategies based on real-time sensory ‎feedback, allowing the robotic system to autonomously adapt to variations in object positions and unexpected disturbances. An Edge-AI ‎module is embedded into the architecture to enhance decision-making speed and reduce latency during task execution. Experimental ‎validation, including scenarios like arc welding and sealant dispensing, shows the proposed system outperforms traditional PID-based ‎adaptive controllers. The AI-driven solution demonstrated improved precision, faster convergence, and superior adaptability under complex ‎and fluctuating manufacturing conditions. The study also opens pathways for future integration of hybrid AI techniques—such as fuzzy ‎logic and genetic algorithms—for even more intelligent and responsive robotic systems‎.

Thermal systems are fundamental to a wide range of industrial applications, where performance and efficiency critically depend on precise and reliable modeling techniques. Traditional Artificial Neural Network (ANN)-based models, although widely used, often struggle with overfitting, limited generalization, and inadequate representation of the complex, nonlinear behavior inherent to thermal processes. These limitations restrict their deployment in real-time and dynamic operational settings. This study aims to enhance the predictive accuracy and robustness of thermal system modeling by integrating advanced machine learning (ML) techniques with hybrid optimization strategies. The research focuses on complex systems such as heat exchangers, gas-solid fluidized beds, and thermal energy storage units. A comprehensive methodology involving industrial data collection, preprocessing via normalization and feature selection, and model training using individual and hybrid ML algorithms is proposed. Performance is benchmarked using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² metrics. Advanced methods like Deep Learning (DL), Support Vector Machines (SVM), Genetic Algorithms (GA), Ensemble Learning, Transfer Learning, and Evolutionary Optimization are employed to address shortcomings of conventional approaches. Results demonstrate that hybrid models outperform standalone ANN-based techniques in prediction accuracy and generalization.

Additive Manufacturing (AM) has transformed modern production by enabling the fabrication of complex geometries with enhanced material ‎efficiency. However, traditional 3D printing techniques often face challenges such as incomplete fusion, material inconsistencies, and thermal ‎warping, which affect overall quality and productivity. This study introduces an intelligent 3D printing framework that integrates Artificial ‎Intelligence (AI) to enable real-time monitoring, defect detection, and adaptive process control, thereby addressing these limitations. The ‎proposed system utilizes Convolutional Neural Networks (CNNs) for computer vision-based quality inspection, enabling the detection of ‎structural anomalies during the printing process. Reinforcement Learning (RL) is employed for dynamic adjustment of parameters like nozzle ‎temperature, deposition speed, and material feed rate in response to real-time feedback, significantly reducing defect occurrence. Adaptive ‎machine learning algorithms like Random Forests and Gradient Boosting also facilitate process optimization and predictive maintenance. ‎Stereolithography (SLA), Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM) are among the AM platforms that use this AI-‎AI-enhanced closed-loop control approach. Material use, energy efficiency, production time, print quality and defect mitigation have all significantly ‎improved, as confirmed by experimental validation. With its ability to guarantee accuracy and dependability in contemporary 3D printing ‎processes, the framework shows great promise for developing industrial and biomedical applications‎.

The rapid advancement of IoT technologies has emerged as a key driver of sustainable development, reshaping industries and societal structures. This study critically examines the intersection of IoT and sustainability by analyzing contemporary literature on the subject. A comprehensive review of IoT-driven innovations highlights their transformative impact across sectors such as agriculture, smart cities, and resource management. The research investigates how digitalization, particularly within supply chains, redefines operational strategies and enhances sustainability metrics. With the integration of technologies like RFID, blockchain, and IoT under Industry 4.0, organizations are revolutionizing process efficiency, transparency, and environmental responsibility. To assess these implications, the study conducts two comparative simulation experiments involving a three-party supply chain in cheese production—one utilizing traditional methods and the other leveraging IoT-based innovations. Results reveal significant improvements in order management efficiency and compliance handling, underscoring the critical role of emerging technologies in fostering sustainable practices. The proposed framework provides valuable insights into the broader management implications of IoT adoption, reinforcing its potential as a catalyst for global sustainability initiatives.

The performance of polymer-based nano iron composites reinforced with natural fibers and nanoparticles is investigated in this work with the aim of enhancing their mechanical electrical and water-absorbing properties for a variety of applications. System 1 (PLA with nano iron particles) System 2 (PLA with natural fillers) and System 3 (PLA with both natural fillers and nano iron particles) are the three composite systems that were developed. Mechanical performance assessment tests including tensile compression and bending tests as well as electrical conductivity and water absorption morphological analysis using SEM and EDAX were all conducted. According to the results System 3 which combines natural fillers with nano iron showed superior tensile and flexural strength because of improved filler dispersion and improved filler-matrix bonding. The creation of a conductive network by nano iron was responsible for System 2s highest electrical conductivity (340 µS/cm). Compression testing showed that Systems 2 and 3 were stronger because there were fewer voids and cracks spreading. System 2 did however exhibit a high water absorption rate of 20% which may indicate durability problems. According to this study adding natural fibers and nanoparticles to PLA composites may produce lightweight incredibly durable multifunctional materials with exciting potential uses in the electronics automotive and construction sectors.