tinyML Summit 2023: Using tinyML and Sound Event Detection for weld anomaly detection in Manu... скачать в хорошем качестве

tinyML Summit 2023: Using tinyML and Sound Event Detection for weld anomaly detection in Manu...

Joint Application end user Shiloh Industries with imagimob Using tinyML and Sound Event Detection for weld anomaly detection in Manufacturing Jeffrey MOORE, Senior Controls Engineer, Shiloh/Dura Alexander SAMUELSSON, CTO/Co-Founder, Imagimob In an increasingly competitive global manufacturing industry, innovation has never been more essential. In today’s presentation we will show an example of innovative effort. tinyML has wide ranging potential to improve manufacturing processes including robotic operations, quality control and efficiency. Working with Imagimob we have initiated a project for the acoustic detection of robotic gas metal arcwelding anomalies. These include the detection of the weld defects porosity, burn through,and low deposition. Conventional electronic detection methods have significant limitations and as such human visual inspection is heavily relied upon. Our project aims to improve current processes and gain a potential competitive advantage. We will discuss the challenge, the data, data collection, the tinyML model building, testing and implementation and the concept of continuous learning. We will also discuss the hardware, open-source software used, PLC/ SPS integration, and workflows developed for this project. Additionally, in this presentation will be examples of how Large Language Models (LLM )were used to expedite our efforts.