Authentise, Addiguru Partner on Integrated In-Process AM Monitoring

Collaboration enables workflow monitoring for real-time actions to repair defects or stop failed prints.

Authentise, a manufacturer of data-driven workflow tools for additive manufacturing (AM), and Addiguru, a provider of real-time AM monitoring solutions, are extending the Authentise Manufacturing Execution System (AMES) to include real-time process monitoring powered by Addiguru with a computer vision and artificial intelligence (AI)-based solution. AMES is a workflow management engine using machine data for automation.

The company says the integration of Authentise with Addiguru will provide a seamless experience for users who are seeking to gain practical use from their AM machine monitoring systems. Alerts from the Addiguru algorithms create real-time notifications within the Authentise web interface and app, display images highlighting potential issues, and visually spotlight the alert within the full workflow view. The full data suite of images and findings is automatically added to the real-time traceability alert and in a new analytics section for each machine and build. This includes the ability to overlay detected anomalies with sensor data taken from Authentise’s unique access to machine data. Each user can also use this data to create custom alerts, reports and dashboards.

The combination of Addiguru’s AI-driven insight and Authentise’s workflow tools enables users to gain practical benefit in a system by having all data and notifications in one place, according to the company. Authentise provides coherent control of the digital thread and access to machine data, to which Addiguru can add visual inspection and intelligent analysis. This monitoring technology detects defects using computer vision and AI algorithms during the build process, which then inform the user via notifications who can make changes to repair defects or stop failed prints. Addiguru says its monitoring platform is machine-brand agnostic, can easily incorporate different kinds of sensors and data, and can be easily incorporated into existing and newly developed powder bed AM equipment.