Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method

The study published in Scientific Reports evaluates various deep learning-based automatic optical inspection (AOI) setups for detecting complex surface defects in injection-molded parts. Three inspection strategies were compared: static frontal imaging, belt conveyor inspection, and robotic-assisted inspection. The robotic-assisted method, which allows for flexibility in optimizing camera angles and positions, demonstrated superior performance in terms of defect detection accuracy.

The research provides a comprehensive methodology for systematically evaluating and optimizing inspection setups, enabling informed decisions about AOI system design. This contributes to narrowing the gap between advanced detection algorithms and their practical application in quality control processes, ultimately enhancing the automatic detection of challenging defects in the injection molding industry.

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