The Commonwealth Center for Advanced Manufacturing (CCAM) team in Virginia.
AI startup
Bonsai and Siemens have deployed AI on a real-world machine in a test environment that marks the first time deep reinforcement learning has been successfully applied to auto-calibrate real-world Computer Numerical Control (CNC) machines. Siemens experts trained an AI model, using Bonsai’s AI Platform, to auto-calibrate a CNC machine more than 30 times faster than an expert human operator based on estimation performed by Commonwealth Center for Advanced Manufacturing (CCAM) in Virginia.
According to Bonsai, the value that CNC machines provide global manufacturers is constrained by high maintenance costs and to achieve the highest possible quality of production, CNC machines need to be recalibrated frequently, as even minor friction leads to errors that result in costly manufacturing imperfections. Usually, manufacturers will have to bring specialists, which can take hours. That downtime wastes time and money for many manufactyurers.
Siemens partnered with Bonsai, which created an AI platform based on deep reinforcement learning. The platform is based around what Bonsai calls ‘Machine Teaching’ technique, which enables subject matter experts such as specialist engineers to train machines to efficiently perform complex task. Using a simple scripting language, experts can design the lessons and rewards required to train each task. Bonsai’s AI Engine supports a range of deep reinforcement learning algorithms, along with the logic for choosing the best-fit algorithms and guiding the training. In this way, the experts are able to leverage AI without themselves having to gain a deep understanding of machine learning.
