Generative Adversarial Networks for geometric surfaces prediction in injection molding
This addresses quality control challenges in industrial injection molding by enabling early geometry prediction, though it is incremental as it applies existing GAN methods to a new domain-specific dataset.
The paper tackles the problem of predicting final part geometry in injection molding by using Generative Adversarial Networks (GANs) with pix2pix to translate thermographic images of hot parts into geometry predictions, achieving performance evaluated through image similarity algorithms and Discrete Modal Decomposition (DMD) analysis.
Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding. To guarantee the product's quality, it is necessary to adjust the process settings in a closed loop. Those adjustments cannot rely on the final quality because a part takes days to be geometrically stable. Thus, the final part geometry must be predicted from measurements on hot parts. In this paper, we use recent success of Generative Adversarial Networks (GAN) with the pix2pix network architecture to predict the final part geometry, using only hot parts thermographic images, measured right after production. Our dataset is really small, and the GAN learns to translate thermography to geometry. We firstly study prediction performances using different image similarity comparison algorithms. Moreover, we introduce the innovative use of Discrete Modal Decomposition (DMD) to analyze network predictions. The DMD is a geometrical parameterization technique using a modal space projection to geometrically describe surfaces. We study GAN performances to retrieve geometrical parameterization of surfaces.