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#REDIRECT [[Weld quality assurance#Signature image processing method]]
{{Technical|date=December 2011}}
{{primary sources|date=December 2011}}
[[Image:Weldprintbox.jpg|thumb|180px|right|A ''WeldPrint'' analyzer, which uses SIP for the industrial analysis of weld quality]]
'''Signature Image Processing''' (SIP) is a technology used for [[weld quality assurance]]. Implementations of the process analyze electrical data collected from [[welding]] processes. Acceptable welding requires exact conditions; variations in conditions can cause a weld to be unacceptable. SIP allows welding faults to be identified in real time, measures the stability of welding processes, and enables welding processes to be optimized.
== Development ==
Stephen Simpson at the [[University of Sydney]] conceived of the idea of using electrical data analyzed by algorithms to assess the quality of the welds produced in robotic manufacturing in 1995. The method stemmed from research on the complex physical phenomena that occur in welding arcs. The critical realization was that a way of determining the quality of a weld could be developed without a definitive understanding of those phenomena.<ref>Simpson SW and Gillespie P (1998) "In-process monitoring of welding processes—a commercial success", ''Australasian Welding Journal'', '''43''', 16–17</ref><ref>Simpson SW, ''Weld quality measurement'', WIPO PCT WO9845078 (1998); US 6288364 (2001); Australia 741965 (2002); Europe (14 countries) 1007263 (2003); Canada 2285561 (2004); South Korea 0503778 (2005)</ref><ref>Simpson SW, Welding assessment, WIPO PCT WO0143910 (2001); Australia 763689, US 6660965 (2003); Canada 2393773 (2005); PAs: Japan 2001-545030 (2001); China 00817251.X, S. Korea 2002-7007624, India IN/PCT/2002/00740 2002), Brazil PI0016401-1, EU 00984649.4 (2002)</ref> The development involved a number of advances. The first was a method for handling sampled data blocks by treating them as phase-space portrait signatures with appropriate image processing. Typically, one second's worth of sampled welding voltage and current data are collected from GMAW pulse or short [[arc welding]] processes. The data is converted to a 2D histogram, and signal-processing operations such as image smoothing are performed.<ref>Simpson SW (2007) "Signature images for arc welding fault detection", ''Science & Technology of Welding and Joining'', '''12(6)''', 481–86</ref> The second advance was a technique for analyzing welding signatures based on statistical methods from the social sciences, such as [[principal component analysis]]. The relationship between the welding voltage and the current reflects the state of the welding process, and this information is contained in the signature image. Comparing signatures quantitatively using principal component analysis allows for the spread of signature images, enabling faults to be detected<ref>Simpson, SW (2007) "Statistics of signature images for arc welding fault detection", ''Science & Technology of Welding and Joining'', '''12(6)''', 557–64</ref> and identified<ref>Simpson SW (2008) "Fault identification in gas metal arc welding with signature images", ''Science & Technology of Welding and Joining'', '''13(1)''', 87–96</ref> The system includes algorithms and mathematics appropriate for real-time welding analysis on personal computers, and the multidimensional optimization of fault-detection performance using experimental welding data.<ref>Simpson SW, "Statistics of signature images for arc welding fault detection", ''Science & Technology of Welding and Joining'', '''12(6)''', 557–64, 2007</ref> Comparing signature images from moment to moment in a weld provides a useful estimate of how stable the welding process is.<ref>Simpson SW (2008) "Signature image stability and metal transfer in gas metal arc welding", ''Science & Technology of Welding and Joining'', '''13(2)''', 176–83</ref><ref>Simpson SW (2009) "Automated fault detection in gas metal arc welding with signature images", ''Australasian Welding Journal – Welding Research Supplement'', '''54''', 41–47</ref> "Through-the-arc" sensing, by comparing signature images when the physical parameters of the process change, leads to quantitative estimates—for example, of the position of the weld bead.<ref>Simpson SW (2008) "Through The arc sensing in gas metal arc welding with signature images", ''Science & Technology of Welding and Joining'', '''13(1)''', 80–86</ref>
Unlike systems that log information for later study or that use X-rays or ultrasound to check samples, this technology looks at the electrical signal and detects faults when they occur.<ref name = ATSsitwe1>http://ats.business.gov.au/companies-and-technologies/building-and-construction/welding-technologies-innovations </ref> Data blocks of 4,000 points of electrical data are collected four times a second and converted to signature images. After image processing operations, statistical analyses of the signatures provide quantitative assessment of the welding process, revealing its stability and reproducibility, and providing fault detection and process diagnostics.<ref>Simpson, SW (2007) "Statistics of signature images for arc welding fault detection", ''Science & Technology of Welding and Joining'', '''12(6)''', 557–64</ref> A similar approach, using voltage-current histograms and a simplified statistical measure of distance between signature images has been evaluated for tungsten inert gas (TIG) welding by researchers from [[Osaka University]].<ref>Matsubara T, Terasaki H, Otsuka H, and Komizo Y (2010) "Developments of real-time monitoring method of welding" (paper RAJU-VE1), ''Proceedings of the Visual-JW2010''</ref>
== Industrial application==
SIP is the basis for the ''WeldPrint'' system (owned by the University of Sydney). ''WeldPrint'' was developed with the assistance of an Australian government ''R&D Start'' grant (1999–2001), after support by the [[Australian Research Council]] for the fundamental research (1997–2001). The system consists of a front-end interface and software based on the SIP engine, and relies on electrical signals alone. It is designed to be non-intrusive and sufficiently robust to withstand harsh industrial welding environments. The first major purchaser of the technology, [[Holden|GM Holden]]<ref>"Holden orders award-winning weldprint welding technology", ''Techwatch'', Price Waterhouse Coopers, '''12(6)''', 2002,</ref><ref> "Holden purchases award winning weldprint welding technology", ''Australian Technology Showcase'' http://www.techshowcase.nsw.gov.au/ News and Events (2002)</ref><ref>"University weld checker to be used by Holden", ''Australian Innovation Magazine'', '''3–5/02''', 29</ref> provided feedback that allowed the system to be refined in ways that increased its industrial and commercial value. Improvements in the algorithms, including multiple parameter optimization with a server network, have led to an order-of-magnitude improvement in fault-detection performance over the past five years.{{fact|date=December 2011}}
[[Image:SIP weld analysis of driveshafts.jpg|thumb|240px|The technology in use on a shop floor.]]
The ''WeldPrint'' software received the ''Brother business software of the year award'' (2001); in 2003, the technology received the A $100,000 inaugural ''Australasian Peter Doherty Prize for Innovation'';<ref>"Bright sparks join forces to take out Doherty Prize", ''The Australian'' (national newspaper)—''Higher Education Supplement'', 2 April 2003</ref><ref name = abcsite1> *{{cite web|url=http://www.abc.net.au/ra/innovations/stories/s852363.htm|title=Weldprint Wins Award|publisher=[[Radio Australia]]|work=Innovations|date=11 May 2003|accessdate=19 January 2011}}</ref>
and WTi, the University's original spin-off company, received an AusIndustry Certificate of Achievement in recognition of the development.{{fact|date=December 2011}}
SIP has opened opportunities for researchers to use it as a measurement tool both in welding<ref>Nguyen NT, Mai Y-W, Simpson SW and Ohta A (2004) “Analytical approximate solution for double-ellipsoidal heat source in finite thick plate”, ''Welding J'', '''83''', 82s</ref> and in related disciplines, such as structural engineering.<ref>The LH and Hancock GJ (2005) "Strength of welded connections in G450 sheet steel", ''J Struct Eng'', '''131''', 1561</ref> Research opportunities have opened up in the application of biomonitoring of external [[Electroencephalography|EEGs]], where SIP offers advantages in interpreting the complex signals<ref>"Car plant technology has medical spin-off", ''UniNews'', USyd, '''34(1)''', 1 (2002)</ref>
==References==
{{reflist}}
[[Category:Welding]]
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