  The prediction of upcoming events in industrial processes has been a
long-standing research goal since it enables optimization of manufacturing
parameters, planning of equipment maintenance and more importantly prediction
and eventually prevention of defects. While existing approaches have
accomplished substantial progress, they are mostly limited to processing of one
dimensional signals or require parameter tuning to model environmental
parameters. In this paper, we propose an alternative approach based on deep
neural networks that simulates changes in the 3D structure of a monitored
object in a batch based on previous 3D measurements. In particular, we propose
an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to
model the geometric variations in manufacturing parameters and predict upcoming
events related to sub-optimal performance. We validate our framework on a
microelectronics use-case using the recently published PCB scans dataset where
we simulate changes on the shape and volume of glue deposited on an Liquid
Crystal Polymer (LCP) substrate before the attachment of integrated circuits
(IC). Experimental evaluation examines the impact of different choices in the
cost function during training and shows that the proposed method can be
efficiently used for defect prediction.
