Hot Stamping 4.0
With smart process monitoring, the press hardening of lightweight
parts can be seamlessly traced and documented
The process of press hardening, also referred to
as hot stamping in the metal forming industry,
depends on a number of influencing factors: the
exact temperature of the red-hot blanks when
leaving the furnace, the amount of time that
passes before they are placed in the die, the
press force applied, and many other things. All
of these parameters have a direct effect on the
quality of the parts, which is why it should be
possible to seamlessly document and, in the
case of any doubt, track and trace these parts
with pinpoint accuracy. That’s exactly what the
new solution offers that Schuler will present
for the Industrial Internet of Things (IIoT) or
the “Smart Press Shop” at the EuroBLECH trade
fair from October 23 through 26 in Hanover,
Germany.
The first system that Schuler networked with the
solution’s software was its hot stamping line in
the Hot Stamping TechCenter at the company
headquarters in Göppingen. Video and thermal
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TUBE NEWS EVENTS August 2018
imaging cameras provide a real-time overview of the
blank feed, transfer and parts exit sections, all while
the system continuously records and documents
process data (referred to as “process monitoring”).
The information is collected by the numerous
sensors installed in the press, die, furnace and
cooling units. These powerful sensors detect even the
slightest changes in temperature, pressure, vibration
characteristics and flow rate in intervals measuring
just fractions of a second.
As this is happening, the process monitoring system
from Schuler is able to not only combine the huge
quantities of data collected by the sensors at many
megabytes per second, but also to synchronize
this data across all of the different interfaces. To
ensure that the correct and necessary information is
available at any given moment, algorithms analyze
the data and keep the amount of memory used to a
sensible size. This makes it possible to visualize long-
term trends for various different types of process
data.