Generative Adversarial Networks for geometric surfaces prediction in injection molding

Pierre Nagorny (1) , Thomas Lacombe (1) , Hugues Favreliere (1) , Maurice Pillet (1) , Eric Pairel (1) , Ronan Le Goff (2) , Marlene Wali (2) , Jerôme Loureaux (2) , Patrice Kiener (3)

1 SYMME - Laboratoire SYstèmes et Matériaux pour la MEcatronique
2 IPC - Centre Technique Industriel de la Plasturgie et des Composites
3 InModelia

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.

Mots clés: Discrete Modal Decomposition; Generative Adversarial Networks; Quality prediction; Thermography; Injection molding

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Systèmes et contrôle [cs.SY]; Traitement du signal et de l'image [eess.SP]

Pierre Nagorny, Thomas Lacombe, Hugues Favreliere, Maurice Pillet, Eric Pairel, et al.. Generative Adversarial Networks for geometric surfaces prediction in injection molding: Performance analysis with Discrete Modal Decomposition. 2018 IEEE International Conference on Industrial Technology (ICIT), IEEE IES, Lyon 1 University, Ampère Lab, Satie Lab, Feb 2018, Lyon, France. pp.1514-1519, ⟨10.1109/ICIT.2018.8352405⟩. ⟨hal-01995293⟩