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In particular, injection molding enables rapid and cost\u2010effective production, making it well\u2010suited for mass production. Thus, rapid and precise measurement is essential for inspecting the quality of all injection\u2010molded products. However, complex 3D geometry and easily deformable property of axial fan hinder rapid and accurate measurement, thereby reducing quality control efficiency. This study introduces a convolutional neural network\u2010based vision inspection system that can enhance the productivity and quality of injection\u2010molded products by overcoming the limitations of traditional physical measurement methods. Consequently, the proposed model shows high performance (R\u2010squared\u2009=\u2009\u22480.9987) for predicting both edge heights. Compared to a conventional manual measurement method, the proposed model reduces the measurement time per blade by \u224899%, and the total inspection time by \u224893.61%. Moreover, by utilizing explainable artificial intelligence, key features for prediction are identified, providing insight into why the model is capable of robust and precise measurements even in the presence of noise. The developed vision\u2010based deflection measurement system is expected to contribute significantly to the automation of quality control of axial fans to realize the future smart injection\u2010molding plants.<\/jats:p>","DOI":"10.1002\/aisy.202500364","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:53:33Z","timestamp":1754506413000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Rapid and Precise Geometric Measurement of Injection\u2010Molded Axial Fans Using Convolutional Neural Network Regression"],"prefix":"10.1002","volume":"8","author":[{"given":"Keuntae","family":"Baek","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering Hanyang University  Seoul 04763 South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanghun","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Hanyang University  Seoul 04763 South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhyeok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Hanyang University  Seoul 04763 South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaemin","family":"Oh","sequence":"additional","affiliation":[{"name":"HS Metal\/Plastic Forming &amp; 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