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.
The paper published in the journal Electronics explores the crucial role of Artificial Intelligence (AI) and Explainable AI (XAI) in Visual Quality Assurance (VQA) within manufacturing. While AI-based Visual Quality Control (VQC) systems are prevalent in defect detection, the study advocates for broader applications of VQA practices and increased utilization of XAI to enhance transparency and interpretability, ultimately improving decision-making and quality assurance in the industry.
This article explores the challenges of deep-sea exploration and how bioinspired soft robots have emerged as a transformative solution. Drawing inspiration from deep-sea organisms, these robots combine innovative actuation, pressure resilience, and sensing strategies to navigate and interact with the extreme conditions of the deep ocean.
This article explores the impact of industrial robot adoption on corporate green innovation in China. The study uses data from Chinese manufacturing companies and analyzes the role of industrial robots in improving green innovation by promoting environmental management and enhancing productivity.
Researchers have introduced the All-Analog Chip for Combined Electronic and Light Computing (ACCEL), a groundbreaking technology that significantly improves energy efficiency and computing speed in vision tasks. ACCEL's innovative approach combines diffractive optical analog computing and electronic analog computing, eliminating the need for Analog-to-Digital Converters (ADCs) and achieving low latency.
Researchers have improved inkjet print head monitoring in digital manufacturing by employing machine learning algorithms to classify nozzle jetting conditions based on self-sensing signals, achieving over 99.6% accuracy. This approach offers real-time detection of faulty nozzle behavior, ensuring the quality of printed products and contributing to the efficiency of digital manufacturing processes.
Researchers examined the impact of visual information and the perceived intelligence of voice assistants on consumers' purchasing behavior in online sustainable clothing shopping. Their findings highlight the importance of positive attitudes toward sustainable fashion and the role of AI-driven voice assistants.
This review explores the landscape of social robotics research, addressing knowledge gaps and implications for business and management. It highlights the need for more studies on social robotic interactions in organizations, trust in human-robot relationships, and the impact of virtual social robots in the metaverse, emphasizing the importance of balancing technology integration with societal well-being.
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.
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