Abstract
Automated real time quality monitoring is one of the key enablers for future high-speed production. In this research, an in-process monitoring procedure based on computer vision inspection and deep learning is proposed to indicate the tool and part quality during soft tooling injection moulding. Multiple types of injection moulding defects can be detected by the proposed method. Geometrical dimensions of the part can be measured simultaneously and the uncertainty can be quantified. Based on the obtained data, automated quality evaluation can be achieved in-process and a decision signal can be sent back to the injection moulding system for process adjustment.
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