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 perspective maps how AI can transform water treatment across technology, engineering, and industry by organising current advances into a structured tri-axis roadmap. It highlights how AI can drive smarter materials, microbial regulation, autonomous ecosystems, and next-generation industrial management.
Researchers from the University of Naples and the University of Wollongong have developed an AI-based method that boosts defect detection accuracy in wire arc additive manufacturing from 57% to 85.3%. The approach processes high-frequency welding data to identify anomalies in real time, reducing production costs and improving quality assurance.
Researchers at Chung-Ang University in South Korea have developed DiffectNet, an AI-powered diffusion model that reconstructs hidden internal defects in structures with high precision, overcoming the physical limits of traditional non-destructive testing. The breakthrough enables real-time defect imaging for critical sectors such as energy, aerospace, and semiconductors.
Cornell researchers have quantified AI’s rapidly growing environmental footprint, projecting that U.S. data centers could emit up to 44 million tons of CO₂ and consume over a billion cubic meters of water annually by 2030. Their Nature Sustainability study also outlines strategies that could cut these impacts by up to 86%.
Researchers at the University of New Hampshire used artificial intelligence to accelerate the discovery of new functional magnetic materials, compiling a searchable database of 67,573 compounds. The system identified 25 previously unknown materials that remain magnetic at high temperatures, potentially reducing reliance on rare earth elements.
Rowan University’s Digital Engineering Hub (DEHub) is pioneering the fusion of AI and advanced manufacturing, using a cutting-edge lab equipped with 3D metal printing and supercomputing capabilities to transform real-world production. By integrating intelligent systems with live data processing, DEHub enables real-time flaw detection and design optimization, laying the foundation for industrial-scale smart manufacturing.
Korean researchers have unveiled an AI-powered roadmap for sustainable perovskite solar cells that halves production costs and cuts climate impact by over 80%. Their bio-based process replaces toxic solvents, making solar power safer, cheaper, and greener.
Researchers at Cornell University have created Double Duty, a new Field-Programmable Gate Array (FPGA) chip architecture that allows logic blocks to perform arithmetic and logical operations simultaneously. This innovation cuts energy use, shrinks chip space by over 20%, and boosts performance by nearly 10% for AI tasks.
Researchers at University of Florida have created a silicon chip that uses laser light and microscopic Fresnel lenses to perform convolution operations, a power-intensive step in AI. This optical approach greatly reduces energy use while maintaining near 98% accuracy in machine learning tasks.
Researchers at Penn and Duke developed AMP-Diffusion, a generative AI tool that designed 50,000 novel antimicrobial peptides. Two lead candidates successfully treated infections in mice, performing on par with FDA-approved antibiotics and showing no toxicity.
Researchers at NC State have developed Rainbow, the world’s first multi-robot self-driving laboratory that autonomously discovers high-performance quantum dots. Powered by AI and advanced robotics, Rainbow can run up to 1,000 experiments daily, accelerating discovery and scaling seamlessly from lab to manufacturing.
A Tsinghua University team reviewed the progress of inverse lithography technology (ILT) in semiconductor manufacturing, highlighting how artificial intelligence improves lithography modeling, mask optimization, and computational efficiency. The paper also discusses challenges in scalability, interpretability, and mask fabrication.
Researchers at Nanjing University of Science and Technology and Warsaw University of Technology developed DFAMFPP, a deep learning-enabled 3D imaging system that breaks sensor frame-rate limits. The method reconstructs multiple high-precision 3D frames from a single image, enabling ultrafast visualization of high-speed phenomena.
MIT chemical engineers have developed a machine learning model that predicts molecular solubility in organic solvents with high accuracy, streamlining pharmaceutical synthesis. The freely available tool could accelerate drug development and help identify safer, more sustainable solvents.
Researchers at Northwestern University and Toyota Research Institute used an AI-driven "megalibrary" of millions of nanoparticles to rapidly discover a low-cost hydrogen catalyst. The material matches or exceeds iridium’s performance but is far cheaper and more abundant, boosting clean energy prospects.
Researchers from Zhejiang University have developed Deep3DSketch-im, a deep learning model that transforms a single freehand sketch into a high-quality 3D mesh. The system uses signed distance fields and a pose-estimation network to generate detailed, editable 3D models with minimal user input.
This review from Southeast University outlines the evolution, technologies, applications, and future directions of AI-driven text generation, from rule-based systems to large language models.
It categorizes text generation into utility, creative, and emotive types, and discusses the challenges of quality, ethics, and computational costs.
A new AI tool developed by Chongqing University and Zhejiang University has created 7,245 novel proteins entirely by computer design, dramatically speeding up and lowering the cost of protein engineering. These AI-designed proteins are optimized for stability and target binding, promising advances in drug development, diagnostics, and research.
Viam has partnered with Viking Yachts to automate the labor-intensive fiberglass sanding process in yacht manufacturing using adaptive robotics. This collaboration aims to improve efficiency, consistency, and worker safety, signaling a major step forward in marine industry automation.
Researchers at Empa have developed machine learning algorithms that significantly streamline and enhance laser-based metal manufacturing processes, such as powder bed fusion and welding. By integrating real-time sensor data and adaptive AI, they reduce experimental workload and optimize quality and efficiency, making advanced metalworking more accessible.
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