  One of the consequences of passing from mass production to mass customization
paradigm in the nowadays industrialized world is the need to increase
flexibility and responsiveness of manufacturing companies. The high-mix /
low-volume production forces constant accommodations of unknown product
variants, which ultimately leads to high periods of machine calibration. The
difficulty related with machine calibration is that experience is required
together with a set of experiments to meet the final product quality.
Unfortunately, all possible combinations of machine parameters is so high that
is difficult to build empirical knowledge. Due to this fact, normally trial and
error approaches are taken making one-of-a-kind products not viable. Therefore,
a Zero-Shot Learning (ZSL) based approach called hyper-process model (HPM) to
learn the relation among multiple tasks is used as a way to shorten the
calibration phase. Assuming each product variant is a task to solve, first, a
shape analysis on data to learn common modes of deformation between tasks is
made, and secondly, a mapping between these modes and task descriptions is
performed. Ultimately, the present work has two main contributions: 1)
Formulation of an industrial problem into a ZSL setting where new process
models can be generated for process optimization and 2) the definition of a
regression problem in the domain of ZSL. For that purpose, a 2-d deep drawing
simulated process was used based on data collected from the Abaqus simulator,
where a significant number of process models were collected to test the
effectiveness of the approach. The obtained results show that is possible to
learn new tasks without any available data (both labeled and unlabeled) by
leveraging information about already existing tasks, allowing to speed up the
calibration phase and make a quicker integration of new products into
manufacturing systems.
