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The Advanced Automatic Gauge Control

We truly believe that our systems will help our Customers to produce more and better. Our measurement systems, such as thickness gauges, coating gauges, etc. are capable to improve your production quality starting from the first pass. Discover how much the thickness control is directly connected to final material's quality. A stand-alone thickness control does not automatically leads to an effective quality control. Discover more on our advanced automatic gauge control optimization program. Thickness consistency during the manufacturing process on a flat product has a great impact on final material's properties. Thickness control quality is directly proportional to final product's quality, but there are much more variable involved into the process. Measure a variable is not enough to control your final product's quality, if this variable is not cross referenced with others. The advanced automatic gauge control solution, gathering high speed data from the field while measuring, is capable to discover groups or "families" in some way or another "similar" to each other, generalizing known structure to forecast following process presets in order to minimize unwanted behaviors due to known errors.
What is the Advanced Automatic Gauge Control and how it works? It consists in 4 steps:

  • ONLINE DATA ACQUISITION

  • ONLINE / OFFLINE (BIG) DATA STORAGE

  • ONLINE DATA CROSS-REFERENCING

  • OFFLINE AND POST PROCESS DATA ANALYSIS


The AAGC performs the real time identification of a possible unusual data and identify the relationships between variables which may have been caused and evaluate the impact on the overall process result. Our AAGC solution (gathering high speed data from sensors), is capable to discover groups or "families" in some way or another "similar" to each other, generalizing known structure to forecast following process presets in order to minimize unwanted behaviors due to known errors.
Our main target is to help our Customers to produce better, faster and cheaper, by helping to identify the possible process bottle-necks. In few words, our solutions are capable of cross-checking, comparing and providing predictive suggestion and post process statistical reports data to obtain the best from the production line.

STEP-1.
ONLINE DATA ACQUISITION

Variables, such as thickness, speed and mill vibrations, are acquired online with our extremely accurate equipments.

STEP-2.
ONLINE / OFFLINE (BIG) DATA STORAGE

Big data are managed on one or more dataservers, suitable to archive more than 10 years of working reports, 24/365.

STEP-3.
ONLINE DATA CROSS-REFERENCING

Acquired data are cross referenced with saved data to extrapolate "rules" and "trends", in order to anticipate or predict events already observed with comparable data.

STEP-4.
OFFLINE AND POST PROCESS DATA ANALYSIS

Data are available to managers and operators at any time, to teach to the system new rules and trends.


AAGC- V Stand Tandem Mill

An example of advanced automatic gauge control is the full close loop with feedback interaction with the mill, in a V stands Steel Tandem Mill, reducing the strip thickness from 2.5 mm to 0.7 mm with the toerance of 0.1 microns at the last pass.

AAGC- - Hot Steel Plate Mill

AAGC applied on a hot plate mill for steel, with thickness reducition from 40 mm to 8 mm in 4 passes.

AAGC- - Aluminium Foil Mill

AAGC applied on an aluminium foil mill, producing aluminum foil with a target thickness of 7 microns.

AAGC - Hot Strip Mill - Centerline

AAGC applied on Hot Strip Mill

Thickness Control on Finishing lines

Thickness Control on Finishing lines

Working Reports

Off-line reporting

Application's Showcase

The AAGC performs the real time identification of a possible unusual data and identify the relationships between variables which may have been caused and evaluate the impact on the overall process result. Our AAGC solution (gathering high speed data from sensors), is capable to discover groups or "families" in some way or another "similar" to each other, generalizing known structure to forecast following process presets in order to minimize unwanted behaviors due to known errors.