AR Training for Metal Additive Manufacturing

AR Training for Metal Additive Manufacturing

AR Training

Augmented Reality AI powered Text Recognition Object Detection Personalized Feedback


Simplification of the training process, reduce associated costs and timeframe and Versatile to work with multiple metal 3D printers and AR headsets


To create a system for standardizing the workflow in Metal Additive Manufacturing (AM) in order to efficiently train new operators.



A system was created that was capable of standardizing the metal AM workflow in a comprehensive format to quickly and efficiently train new operators. This was accomplished by implementing an AI-powered AR training simulation for metal AM that:

An AI algorithm was developed to simulate the same.

Key Features


CAD software was used to create the Renishaw AM400 digital twin; Solidworks was used for its full configuration and Fusion 360 was utilized for its RBV configuration.

Precise measurements of the most commonly interactive parts were taken like doors, buttons and build areas. External equipment like chiller, dryer and argon tank were modeled as well.

Implementation & Integration

The digital twin was uploaded into the virtual environment of HyperSkill and a step-by-step training procedure was created which was capable of including pictures/videos to facilitate the learning process. Computer Vision was used to detect the button pressed by the user.

For the AR visualization, Hololens 2 was used and HyperSkill was downloaded into the headset, which allowed the trainee to experience the simulation. The aim was for the trainee to first scale and fix the Renishaw AM400 digital twin to the room and this was done using Hololens 2 finger and hand gestures.

Process flow of AI system

The Computer Vision AI system interfaced with the HyperSkill app to provide immediate user feedback. At a given prompt, the Hololens captured an image from the Hololens camera which was sent to the web app for AI processing. The AI prediction result was then sent back to Hololens for user feedback.

Training Procedure

A high-level flowchart of the standardized process was developed for the full and RBV configuration. This was reviewed by an experienced operator to ensure that all steps are correct and in order. A detailed, low-level flowchart was also developed for implementation into HyperSkill.


Professor Bingbing Li, Department of Manufacturing Systems Engineering & Management, California State University Northridge, said “We received excellent support from the SimInsights Team! The CEO Raj, and his engineering team members Erick and Chris helped us a lot in developing the AR system. We used HyperSkill to create the workflows and integrated our machine learning models for finger detection and text recognition. SimInsights provided support and training to our students and also visited our lab to test the overall system on the 3D printers in our lab. Thank you!”.

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