Manufacturing Trade and Connectivity (MTC)

A novel 3D printing method developed by A*STAR researchers fabricates soft support for tissue growth, with embedded channels that ensure nutrient supply.

Deep learning algorithms for predicting machine failures in industrial settings can be compressed without compromising their performance, say A*STAR researchers.

Compact nanochain waveguides that can efficiently transmit infrared light and even slow light down to a fraction of its usual speed could take photonics mainstream.

By giving algorithms the ability to generalize, researchers are expanding the range of problems that can be tackled with artificial intelligence.

A machine learning method that finds defects or dimensional deviation on 3D-printed surfaces ‘on-the-fly’ is paving the way for smart, fully automated systems.

New research shows that titanium alloys joined by 3D-printed curved interlayers are stronger and less likely to crack.

Artificial neural networks are now being used to make 3D-printed metal structures more accurately—and stronger—than ever before.

An integrated simulation model provides a clearer picture of what happens when a water droplet comes in contact with a hydrophobic surface.

Horizontal Technology Programme Offices will bring A*STAR’s deep capabilities to bear on real-world issues facing Singapore and the wider world, says Deputy Chief Executive (Research), Andy Hor.

Porous metallic bones designed to be 3D-printed in unusual shapes could be the future of permanent orthopedic implants.

A*STAR is poised to contribute towards Singapore’s Research, Innovation and Enterprise 2025 plan across all four strategic domains.

Leveraging both artificial intelligence and field knowledge may prove to be a superior strategy for predicting machine health.