Merging technologies with color to avoid design failures
Various software packages can be used to evaluate products and predict failure; however, these packages are extremely computationally intensive and take a significant amount of time to produce a solution. Quicker solutions mean less accurate results.
To combat this issue, a team of Penn State researchers studied the use of machine learning and image colorization algorithms to ease computational load, maintain accuracy, reduce time and predict strain fields for porous materials. They published their work in the Journal of Computational Materials Science with accompanying presentations and proceedings in Procedia Engineering.
"There is always a human side to design," said Chris McComb, assistant professor of engineering design in the School of Engineering Design, ...

















