Rapid and Precise Geometric Measurement of Injection-Molded Axial Fans Using Convolutional Neural Network Regression

Abstract

Rapid and precise product dimension measurement is essential for enabling complete enumeration inspection, ensuring product reliability, and ultimately achieving factory automation. In particular, injection molding enables rapid and cost-effective production, making it well-suited for mass production. Thus, rapid and precise measurement is essential for inspecting the quality of all injection-molded 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-based vision inspection system that can enhance the productivity and quality of injection-molded products by overcoming the limitations of traditional physical measurement methods. Consequently, the proposed model shows high performance (R-squared = approximate to 0.9987) for predicting both edge heights. Compared to a conventional manual measurement method, the proposed model reduces the measurement time per blade by approximate to 99%, and the total inspection time by approximate to 93.61%. 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-based deflection measurement system is expected to contribute significantly to the automation of quality control of axial fans to realize the future smart injection-molding plants.