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<p>In the injection molding industry, the shift toward small-batch production has led to a greater variety of products and smaller batch sizes, necessitating frequent mold changes and efficient quality control, which still largely relies on human operators. This study proposes a comprehensive methodology for evaluating and comparing deep learning-based automatic optical inspection (AOI) strategies to detect complex surface defects in injection-molded parts. Three inspection setups were assessed: static frontal imaging, belt conveyor inspection, and robotic-assisted inspection. The findings reveal clear differences in defect detection capabilities among the methods, with the robotic-assisted approach demonstrating superior performance, achieving higher defect detection accuracy due to its flexibility in optimizing camera angles and positions. The proposed methodology serves as a workflow to systematically evaluate and optimize inspection setups across different parameters, enabling informed decisions about AOI systems design. This research contributes to narrowing the gap between the development of advanced detection algorithms and their industrial application, offering insights into the strategic implementation of AI technologies in quality control processes and enhancing the automatic detection of challenging defects.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Enrico Bovo,
  • Xi Vincent Wang,
  • Giovanni Lucchetta,
  • Lihui Wang

摘要

In the injection molding industry, the shift toward small-batch production has led to a greater variety of products and smaller batch sizes, necessitating frequent mold changes and efficient quality control, which still largely relies on human operators. This study proposes a comprehensive methodology for evaluating and comparing deep learning-based automatic optical inspection (AOI) strategies to detect complex surface defects in injection-molded parts. Three inspection setups were assessed: static frontal imaging, belt conveyor inspection, and robotic-assisted inspection. The findings reveal clear differences in defect detection capabilities among the methods, with the robotic-assisted approach demonstrating superior performance, achieving higher defect detection accuracy due to its flexibility in optimizing camera angles and positions. The proposed methodology serves as a workflow to systematically evaluate and optimize inspection setups across different parameters, enabling informed decisions about AOI systems design. This research contributes to narrowing the gap between the development of advanced detection algorithms and their industrial application, offering insights into the strategic implementation of AI technologies in quality control processes and enhancing the automatic detection of challenging defects.