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VERSION:2.0
METHOD:PUBLISH
BEGIN:VEVENT
ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 27
SUMMARY:Under-sampling and weak signal extraction methods for detecting faint, fine-scale Defects within online data, for offline quality assessment
DESCRIPTION:High-fidelity flatness defects can plague the surface appearance / aesthetics of cold rolled finished strip, when directly shipped, without further processing (e.g., tension leveling) or inspection.  Here, during on-line rolling activities, the high speeds and strip tensions may obscure the visually assessed presence of these defects, leading mill operators to wrongly conclude a defect-free delivered strip, that is ready for direct shipment.  It’s not uncommon for the defect pattern’s elements to have longitudinal and transverse dimensions that resides near (or beyond) the spatial sampling capabilities of the typical shapemeter rolls sensing array (leading to aliasing).  The shapemeter roll’s “effective” spatial sampling frequency may be degraded by temporal or smoothing filtration within the shape measurement’s signal conditioning.  In many respects, one could argue (correctly) that this class of flatness defect will be strongly under-sampled.  The defect elements are too fine to be reliably captured by currently available shape measurement systems, leaving no realistic opportunity to correct (in real-time, while rolling) with closed-loop shape controls.  While this may be the case, there may be weak, “ghostly”, under-sampled remnants of the defect pattern, hidden within high frequency logs of the shapemeter’s raw (but calibrated) radial force measurements.  Being able to detect the presence of these unobservable defects (from the hidden remnants) may offer quality control personnel new opportunities to make post-rolling assessments of the coil’s integrity and acceptability for direct shipment (without off-line inspection).  This paper will examine the use of under-sampling reconstruction methods coupled with weak signal extraction / enhancement techniques, to render visually apparent images indicating the presence of this type of defect.  This paper will also discuss how pattern recognition strategies can offer the ability to automatically detect these defects, with possible implications for use in on-line / real-time scenarios. 
CLASS:PUBLIC
DTSTART:20230614T145000
DTEND:20230614T151000
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