Fig. 1. Trade-off between fidelity/accuracy and simulation time for different solutions providing thermo-chemical analysis of composite parts during processing. Photo Credit: Convergent Manufacturing ...
In the pharmaceutical discovery process, understanding a drug’s residence time—the duration a molecule remains bound to its ...
Regression failure debug is usually a manual process wherein verification engineers debug hundreds, if not thousands of failing tests. Machine learning (ML) technologies have enabled an automated ...
FLO, offers practical guidance on leveraging artificial intelligence, digital twins and streamlined workflows to improve ...
The “composites for sustainable mobility” (CosiMo) project was launched in 2018 by Faurecia Clean Mobility (Nanterre, France) to develop a smart thermoplastic composite resin transfer molding (RTM) ...
Research, development, and production of novel materials depend heavily on the availability of fast and at the same time accurate simulation methods. Machine learning, in which artificial intelligence ...
In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
This third installment in the golf putter design series moves the process from reverse engineering and additive manufacturing to CAD/CAM software. Turning attention to the role of CAD/CAM software in ...
Forbes contributors publish independent expert analyses and insights. Gary Drenik is a writer covering AI, analytics and innovation. Every day we hear about new ways automation is transforming ...
CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for cancer treatment. Operating significantly faster than traditional all ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback