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.
University of Toronto engineers have developed a machine learning framework, AIDED, to rapidly optimize 3D metal printing settings, reducing trial and error.
Researchers from Jülich have developed a robust new memristor that mimics brain-like learning and memory. Its novel switching mechanism could overcome the “catastrophic forgetting” problem in neural networks.
inait partners with Microsoft to scale its digital brain AI, leveraging neuroscience-driven intelligence for fintech and robotics transformation.
Scientists have developed an AI-powered Intelligent Acting Digital Twin (IADT) that can autonomously control real-world machines in real-time, marking a shift from passive monitoring to active decision-making.
Researchers developed a deep learning model that integrates economic theory to improve price-demand predictions, outperforming standard AI models in unprecedented scenarios like the COVID-19 pandemic.
Scientists at ORNL and leading institutions have mapped a vision for AI-driven autonomous research labs, aiming to revolutionize scientific discovery through automation and interconnected systems.
Columbia Engineering researchers have enabled robots to develop "kinematic self-awareness" by watching their own movements through a single camera, allowing them to self-model, adapt to damage, and learn new skills without human intervention.
AI job postings in the U.S. surged by 68% from Q4 2022 to Q4 2024, despite an overall 17% decline in job postings. A new study from the University of Maryland highlights the "ChatGPT effect" driving this trend, with AI roles expanding across multiple economic sectors.
Researchers at Binghamton University and the University at Buffalo are using AI and computational modeling to refine electrospray deposition, a technique for producing ultra-thin polymer films with applications in electronics and healthcare.
Researchers have developed ProtET, a cutting-edge AI model that uses multi-modal learning to enable precise, text-guided protein editing, paving the way for breakthroughs in biotechnology and medicine.
A new optical chip-connection system aims to break the “memory wall” that limits AI model growth and computing speed by replacing traditional electrical connections with reconfigurable light pathways, promising data transfer speeds 100 times faster than current technology.
Study introduces a robust RGB-D dataset for 6D pose estimation, enabling robots to perform industrial pick-and-place tasks with greater precision. The dataset's evaluation with cutting-edge models highlights its potential for advancing robotic automation.
Researchers developed a machine learning model to predict defects in semi-solid die casting, enhancing quality control and paving the way for broader industrial applications.
Researchers developed TLE-PINN, a transfer learning-enhanced physics-informed neural network, to predict melt pool morphology in selective laser melting (SLM), achieving faster training, higher accuracy, and reduced computational costs. This breakthrough offers a scalable solution for real-time process control in manufacturing.
Researchers reimagine microelectronics with cutting-edge materials and AI to create energy-efficient systems that could transform computing, sensing, and scientific discovery worldwide.
Researchers have developed an AI-powered system that rapidly designs complex electromagnetic structures and circuits, slashing development time from weeks to minutes. This breakthrough opens new frontiers in wireless chip technology, enabling unprecedented performance and efficiency.
Research paper reviews 55 green AI initiatives aimed at reducing energy consumption and carbon emissions while identifying the challenges of adopting sustainable AI technologies across industries. The study emphasizes collaboration, model efficiency, and ethical practices to advance green AI development.
This study compares four computer vision algorithms on a Raspberry Pi 4 platform for depalletizing applications. The analysis highlights pattern matching, SIFT, ORB, and Haar cascade methods, emphasizing low-cost, efficient object detection suitable for industrial and small-scale automation environments.
A machine learning framework optimized dry cooling system designs for supercritical CO2 Brayton cycle solar power plants, reducing lifetime cooling costs by 67%. The study highlights the potential for ML to accelerate cost-effective, sustainable energy solutions.
A comprehensive machine learning framework was developed to predict mechanical properties in metal additive manufacturing. By leveraging a vast dataset and advanced featurization techniques, the framework achieved high accuracy, offering a standardized platform for optimizing additive manufacturing processes.
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