Abstract
This work focuses on a novel human‐centered digital assistant combining Mixed Reality (MR), Computer Vision, and Machine Learning regression to guide professionals and students on how to operate and correctly parameterize battery manufacturing machinery. This article aims to provide a proof of concept of the digital assistant, for a process involving an operator interacting with a semi‐manual electrode calendering machine and examining how the intended calendering parameters will impact the electrode properties after calendering. The operator performs his/her actions while the digital assistant automatically detects the parameters entered by him/her on the machinery. Then, the digital assistant provides real‐time predictions to the operator, helping him/her in decision‐making through a minimalistic holographic interface minimizing visual hindrance. The ergonomics of the solution is optimized by acquiring feedback from users with various experience levels and evaluating their performance. As the necessity for advanced energy storage solutions rises, there is a strong need for modern training and guidance tools that aid in complex battery manufacturing processes at the prototyping stage, involving both semi‐manual and automatic activities. Thanks to MR, the digital assistant has strong potential to improve manufactured electrode and cell qualities by emphasizing the egocentric perspective along the battery cell prototyping process.