Articles tagged with Manufacturing Equipment
Engineers at the University of Pennsylvania have developed LIBRIS, an automated microfluidic platform capable of generating lipid nanoparticle formulations at high speed and scale. This enables the creation of large, systematic datasets needed to train predictive AI models, accelerating the design of lipid nanoparticles for mRNA delivery.
A University of Houston professor has found that tree-like thin films release heat at least three times better than traditional methods, enabling more efficient cooling in AI data centers. The discovery demonstrates the power of physics-aware AI design for validating high-impact cooling solutions.
Researchers have developed a strong, defect-free composite material that can phase-shift under stress to dissipate energy. The material, created using additive friction stir deposition, has potential applications in defense, infrastructure, aerospace, and sporting equipment.
A new GaN-based e-beam technology has been developed through joint research between Photo electron Soul and Nagoya University, enabling non-contact electrical inspection and metrology during semiconductor manufacturing. The technology is expected to improve yield and defect detection, leading to increased efficiency in the industry.
International Journal of Extreme Manufacturing (IJEM) achieves a new Impact Factor of 21.3, surpassing 20 for the first time and maintaining its position as top journal in the field. IJEM has attracted submissions from 853 institutions in 81 countries.
A team of researchers at Texas A&M University has received a $1.6 million grant to develop a system for rapidly accelerating the certification process of 3D-printed critical components used in military applications.
Researchers from Empa developed machine learning algorithms to optimize laser-based manufacturing techniques, reducing preliminary experiments by two-thirds. They also implemented real-time optimization using field-programmable gate arrays (FPGAs) for improved welding processes.
The University of Texas at Arlington's (UTA) Texas Manufacturing Assistance Center (TMAC) has developed a real-time sensor data system to track environmental impact. The program helps Texas manufacturers reduce pollution, lower emissions, and save costs by identifying energy waste, water consumption, and air leaks.
The MASTER project invites educational institutions to develop innovative XR educational content and validate cutting-edge XR technologies in real settings. Selected applicants will receive financial support, access to advanced XR tools, and expert mentorship.
Researchers at the University of Sydney have proposed a new way to reduce industrial emissions by utilizing liquid metals in chemical reactions. This approach aims to decrease energy requirements and lower greenhouse gas emissions.
Lynne McLandsborough's research offers a solution to the sticky sanitation issue in peanut butter and chocolate industries, improving food safety and reducing bacterial illness outbreaks. Her patent-pending method uses a water-in-oil emulsion to kill 99.9999% of Salmonella bacteria.
The University of Texas at San Antonio has been selected to establish a Secure Manufacturing Tech Hub with a $500,000 grant from the US Economic Development Administration. The consortium aims to grow a skilled workforce, enhance business competitiveness, and promote secure manufacturing strategies across South Texas.
Researchers at Rice University have created a new type of storage container that effectively prevents surface contamination for at least six weeks. The technology relies on an ultraclean wall with tiny bumps and divots, which attracts VOCs in air inside the containers.
A new cybersecurity framework uses digital twin technology, machine learning, and human expertise to detect cyberattacks in manufacturing processes. The framework analyzes continuous data streams from physical machines and their digital twins to identify irregularities and flag potential threats.
Scientists from Harvard John A. Paulson School of Engineering and Applied Sciences have created a machine that uses surface tension of water to grab and manipulate microscopic objects, enabling nanoscopic manufacturing. The device can braid micrometer-scale fibers of synthetic material Kevlar, opening doors for high-frequency conductors.
A recent study published in Engineering Construction & Architectural Management identified the main obstacles preventing digital transformation in the engineering and construction industry. The three main problems are a lack of laws and regulations, a lack of support and leadership, and a lack of resources and professionals.
Researchers at Aarhus University have developed a simple analytical model to predict chip formation and optimize surface finish in manufacturing processes. The study reveals the critical cutting depth for almost every material, tool geometry, and running conditions, minimizing tool wear and improving product quality.
Researchers at the University of South Florida have invented a new technology that enables the rapid printing of wearable and electronic sensors without the need for polymer binders. This breakthrough can significantly reduce production time, making it suitable for applications such as health monitoring, prosthetics, and robotics.
A team of engineers at the University of Arizona is using machine learning methods to monitor and mitigate defects in additive manufactured metal parts designed for use in extreme environments. The system combines data processing, process optimization, materials analysis, and machine learning to predict defects.
The AI optimization improves the motor's power factor, reducing disruptions to the power grid. The optimized motor shows excellent performance, with improved efficiency and increased torque while drawing less current.
A new food freezing method called isochoric freezing preserves food without turning it to solid ice, killing microbial contaminants during processing. This technology could save up to 6.5 billion kilowatt-hours of energy and reduce carbon emissions by 4.6 billion kg annually.