For metallic alloys in aerospace, machine learning can make the development of additive manufacturing (AM) processes both faster and cheaper. In collaboration with the University of Sheffield Advanced ...

Machine learning is a rapidly growing field with endless potential applications. In the next few years, we will see machine learning transform many industries, including manufacturing, retail and ...

In recent years, significant advancements in ML have influenced several fields beyond computer science, including autonomous driving, structural color design, medicine, and face recognition. The ...

The importance of digital tools and simulation for successful composite parts design is well established, whether for aircraft wings, automotive bumper beams or bicycle frames. Over the past decade, ...

Researchers at Empa have developed machine learning algorithms that reduce preliminary testing in laser-based metal additive manufacturing by about two-thirds without compromising quality. Using ...

Sustainability metrics for additive manufacturing are evolving, with a focus on enterprise-wide impact rather than just part-to-part comparisons, emphasizing resource efficiency and environmental ROI.

The quest to shape our understanding of AI is occurring at the same time that we’re experimenting with its potential applications, from designing through integration of products and processes.

Laser-based metal manufacturing, including powder bed fusion (PBF) 3D printing, requires precise parameter settings that can change even between batches of identical material. Empa researchers Giulio ...