A data-driven framework for integrating visual inspection into injection moulding pipeline
Recent advances in machine vision has led to new opportunities for automating that entire manufacturing pipeline. Consider, for example, the situation where an unattended computer vision system inspects the widget and decides whether or not to discard it. Even this little amount of automation can save many hundreds of person-hours on a typical factory floor. While for simple designs, we now have automated inspection methods relying upon lasers, 3D scanning or other imaging modalities that can decide if a widget has any defect. For complex designs, this ability remains elusive. More importantly, however, automated inspection schemes can only decide if a widget deviates from its intended design, say available in the form of a CAD drawing, it cannot decide what changes should be made down the manufacturing pipeline to prevent similar defects in the future. This project aims to explore machine learning techniques that integrate automated inspection with manufacturing process. Specifically, we will focus on injection moulding process in this project. We will develop new theory and methods for characterizing the injection moulding process in terms of quantities measured via an automated inspection system.