Addiguru Expands Metal AM Monitoring Partnerships With Renishaw, LISI Aerospace, and UK Research Centers

Addiguru, a company developing multi-sensor monitoring systems for metal additive manufacturing, has announced a series of collaborations with manufacturers and research organisations aimed at improving defect detection during 3D printing builds.

The Partners

New partners include Apex Additive Technologies, Renishaw, Additive Manufacturing Solutions (AMS), LISI AEROSPACE Additive Manufacturing, The Manufacturing Technology Centre (MTC), and the Centre for Advanced Manufacturing at the University of Bolton.

Each collaboration examines how optical, thermal, and machine control data collected during builds can be analysed together to improve confidence in Probability of Defect (PoD) signals and potentially reduce reliance on post-build inspection methods such as computed tomography (CT) scanning.

The Technology

Metal additive manufacturing applications in aerospace, defence, and other critical sectors often require extensive inspection to confirm internal part quality. CT scanning remains a common verification method but increases both cost and production time.

Addiguru's monitoring platform integrates optical imaging, near-infrared sensing, long-wave infrared thermal data, and machine control signals to analyse build conditions during printing. During the 2025 ASTRO America In-Situ Monitoring Challenge, Addiguru was named a co-winner. According to the company, its monitoring approach achieved 96% accuracy in detecting swelling and layer distortion signals and identified those signals approximately 50 to 100 layers earlier than optical monitoring alone.

Renishaw Integration

One collaboration involves Apex Additive Technologies and Renishaw. Engineers from all three organisations are integrating Addiguru's monitoring software with Renishaw's RenAM 500Q metal additive manufacturing system. API connections to Renishaw Central and DataHUB allow the monitoring platform to correlate photodiode intensity data, optical imagery, and machine control signals with thermal maps produced during the build process.

Combining these signals allows earlier detection of defects, potentially reducing the need for expensive post-build CT scanning and enabling more confident acceptance of parts based on in-process data.