AI is used in manufacturing to optimize production processes, improve quality control, and enhance automation. It employs machine learning algorithms, robotics, and real-time data analysis to increase efficiency, reduce defects, and enable predictive maintenance, leading to improved productivity and cost savings in manufacturing operations.
This article discusses the electricity consumption of artificial intelligence (AI) technologies, focusing on the training and inference phases of AI models. With AI's rapid growth and increasing demand for AI chips, the study examines the potential impact of AI on global data center energy use and the need for a balanced approach to address environmental concerns while harnessing AI's potential.
Researchers introduce the e3-skin, a versatile electronic skin created using semisolid extrusion 3D printing. This innovative technology combines various sensors for biomolecular data, vital signs, and behavioral responses, making it a powerful tool for real-time health monitoring. Machine learning enhances its capabilities, particularly in predicting behavioral responses to factors like alcohol consumption.
Researchers explore how AI chatbots can improve supply chain sustainability in small and medium manufacturing enterprises (SMEs) in India. The research shows that chatbots enhance supply chain visibility and innovation capability, leading to improved sustainability performance, and offers practical recommendations for SMEs to leverage this technology for sustainable practices.
This study delves into the significant impact of artificial intelligence (AI) on reducing carbon emissions in the manufacturing sector. The research explores the correlation between AI adoption and carbon intensity, highlighting the role of green technological, management, and product innovation in strengthening AI's carbon reduction effect.
This research presents FL-LoRaMAC, a cutting-edge framework that combines federated learning and LoRaWAN technology to optimize IoT anomaly detection in wearable sensor data while preserving data privacy and minimizing communication costs. The results demonstrate that FL-LoRaMAC significantly reduces data volume and computational overhead compared to traditional centralized ML methods.
Researchers from Bosch Center for Artificial Intelligence have unveiled a groundbreaking framework for flexible robotic manipulation. This system empowers robots to learn complex object-centric skills from human demonstrations and sequence them for intricate industrial assembly tasks, demonstrated effectively in the assembly of critical components of an electric bicycle (e-bike) motor.
This comprehensive review explores the applications of Explainable Machine Learning (XML) in the realm of lithium-ion batteries. It sheds light on how XML techniques enhance transparency, inform decision-making, and drive innovation across battery production, state estimation, and management systems.
Researchers delve into the world of logistics automation, employing RL to enhance storage devices and logistics systems, with real-world implications for manufacturing efficiency. In this groundbreaking approach, using innovative reward signal calculations and AI-driven algorithms, they showcase efficiency gains of 30-100% and pave the way for a new era of unmanned factories and optimized production processes.
Researchers delve into the realm of Citizen-Centric Digital Twins (CCDT), exploring cutting-edge technologies that enable predictive, simulative, and visualizing capabilities to address city-level issues through citizen engagement. The study highlights data acquisition methods, machine learning algorithms, and APIs, offering insights into enhancing urban management while fostering public participation.
Researchers present a novel approach utilizing a residual network (ResNet-18) combined with AI to classify cooling system faults in hydraulic test rigs with 95% accuracy. As hydraulic systems gain prominence in various industries, this innovative method offers a robust solution for preventing costly breakdowns, paving the way for improved reliability and efficiency.
The study delves into the integration of deep learning, discusses the dataset, and showcases the potential of AI-driven fault detection in enhancing sustainable operations within hydraulic systems.
The paper delves into recent advancements in facial emotion recognition (FER) through neural networks, highlighting the prominence of convolutional neural networks (CNNs), and addressing challenges like authenticity and diversity in datasets, with a focus on integrating emotional intelligence into AI systems for improved human interaction.
This article introduces cutting-edge deep learning techniques as a solution to combat evolving web-based attacks in the context of Industry 5.0. By merging human expertise and advanced models, the study proposes a comprehensive approach to fortify cybersecurity, ensuring a safer and more resilient future for transformative technologies.
Researchers propose an intelligent Digital Twin framework enhanced with deep learning to detect and classify human operators and robots in human-robot collaborative manufacturing. The framework improves reliability and safety by enabling autonomous decision-making and maintaining a safe distance between humans and robots, offering a promising solution for advanced manufacturing systems.
A groundbreaking study presents a framework that leverages computer vision and artificial intelligence to automate the inspection process in the food industry, specifically for grading and sorting carrots. By incorporating RGB and depth information from a depth sensor, the system accurately identifies the geometric properties of carrots in real-time, revolutionizing traditional grading methods.
This article reviews the transformative impact of artificial intelligence (AI) techniques such as deep learning and machine learning in the field of superconductivity. From condition monitoring and design optimization to intelligent modeling and estimation, AI offers innovative solutions to overcome challenges, accelerate commercialization, and unlock new opportunities in the realm of superconducting technologies and materials.
This groundbreaking study explores the transformative potential of artificial intelligence, machine learning, deep learning, and big data in revolutionizing the field of superconductivity. The integration of these cutting-edge technologies promises to enhance the development, production, operation, fault identification, and condition monitoring of superconducting devices and systems.
The integration of artificial intelligence (AI) is transforming the battle against food waste and propelling the transition towards a circular economy. By leveraging AI technologies, such as advanced analytics and machine learning, various applications are being developed to optimize food manufacturing, distribution networks, and waste management processes. These AI-driven solutions enhance decision-making, enable efficient resource utilization, and support recycling and upcycling initiatives.
The study demonstrates the use of text mining to identify emerging ML/AI technologies in the Korean semiconductor industry, enabling SMEs to establish an R&D roadmap and enhance competitiveness. Deep neural networks and AI technology applications in semiconductor R&D and manufacturing processes were found to be crucial, with potential for improved reasoning, learning abilities, and process optimization.
Manufacturing companies are embracing AI and IoT technologies to revolutionize their operations, achieve sustainability goals, and remain competitive. The implementation of smart configurations, such as digital twins, sensors, 5G networks, and advanced ML models, enables real-time monitoring, analysis, and optimization of production processes.
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