Keywords: Human in the loop, Language processing, Spec documents, Manufacturing, Task guidance
TL;DR: A scalable approach to automate manufacturing process involves spec documents understanding. We show the data collection & model operations.
Abstract: We introduce a task guidance framework for manufacturing settings aiming to improve the well-being and productivity of manufacturing workers completing a given task. The assistive technology proposed in this work centers on a dialogue system built upon semantic frame extraction of process specifications detailing a given manufacturing process. The dialogue system interacts with the technician performing the task by capturing their actions and assisting them in performing relevant steps. Specifically, we develop components to parse expert-authored natural language documents called specs and utilize the parse for task guidance and continual learning. While still in the early stages, we believe that an interactive, assistive AI framework similar to the one we are exploring will become an important component of high-volume manufacturing in the future. Such a system could increase the quality and scalability of next-generation materials and materials-related products, such as batteries or fuel cells, produced by automated materials synthesis techniques and analyzed by automated materials characterization techniques.
Paper Track: Behind the Scenes
Submission Category: Automated Chemical Synthesis, Automated Material Characterization, Other