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A WeldPrint analyser, which uses SIP for the industrial analysis of weld quality

Signature Image Processing (SIP) is a technology for analysing electrical data collected from welding processes. Welding is used in more than 50% of manufactured products. 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 optimised. Quality monitoring of automatic welding can save production downtime, and can reduce the need for product reworking and recall. The technology is used by eight auto component manufacturers, and has significantly improving the industrial process and the quality of the products.

Development

The idea of using algorithms to assess the quality of the welds produced in robotic manufacturing emerged in 1995 from research by Associate Professor Stephen Simpson at the University of Sydney on the complex physical phenomena that occur in welding arcs.

File:Associate Professor Steve Simpson.jpg
Associate Professor Stephen Simpson, the inventor of the signature image processing technology

The critical realisation was that a way of determining the quality of a weld could be developed without a definitive understanding of those phenomena.[1][2][3] 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.[4] The second advance was a technique for analysing 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[5] and identified[6] The system includes algorithms and mathematics appropriate for real-time welding analysis on personal computers, and the multidimensional optimisation of fault-detection performance using experimental welding data.[7] Comparing signature images from moment to moment in a weld provides a useful estimate of how stable the welding process is.[8][9] "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.[10]

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. 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.[11] 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.[12]

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 non-intrusive, simple to set up, and sufficiently robust to withstand harsh industrial welding environments. The first major purchaser of the technology, GM Holden[13][14][15] 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 optimisation with a server network, have led to an order-of-magnitude improvement in fault-detection performance over the past five years.

Industrial use

The technology in use on the shop floor of Melbourne firm Unidrive, which has used WeldPrint to monitor the quality of steering-column component welds in more than half a million Australian vehicles in the period 2001–06

Globally, more than 70 million passenger vehicles are built annually, each containing typically around 50 metres of continuous arc welding in its subassemblies.[citation needed] WeldPrint for arc welding became available in mid-2001. The innovation has enabled significant improvements in the quality, durability, and safety of vehicles, with considerable cost savings in the avoidance of recalls to fix the large proportion of systemic quality problems that arise from suboptimal welding.[citation needed] About 70 units have been deployed since 2001; of these, about 90% are used on the shop floors of automotive manufacturing companies and their suppliers. The industrial users include Lear (UK), Unidrive, GM Holden, Air International and QTB Automotive (Australia). Units have been hired to Australian companies such as Rheem, Dux, and OneSteel for welding evaluation and process improvement.

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;[16] and WTi—the University's original spin-off company—received an AusIndustry Certificate of Achievement in recognition of the development.

SIP has opened opportunities for researchers to use it as a measurement tool both in welding[17] and in related disciplines, such as structural engineering.[18] Research opportunities have opened up in the application of biomonitoring of external EEGs, where SIP offers advantages in interpreting the complex signals[19]

See also

References

  • "Weldprint Wins Award". Innovations. Radio Australia. 11 May 2003. Retrieved 19 January 2011.

Notes

  1. ^ Simpson SW and Gillespie P (1998) "In-process monitoring of welding processes—a commercial success", Australasian Welding Journal, 43, 16–17
  2. ^ 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)
  3. ^ 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)
  4. ^ Simpson SW (2007) "Signature images for arc welding fault detection", Science & Technology of Welding and Joining, 12(6), 481–86
  5. ^ Simpson, SW (2007) "Statistics of signature images for arc welding fault detection", Science & Technology of Welding and Joining, 12(6), 557–64
  6. ^ Simpson SW (2008) "Fault identification in gas metal arc welding with signature images", Science & Technology of Welding and Joining, 13(1), 87–96
  7. ^ Simpson SW, "Statistics of signature images for arc welding fault detection", Science & Technology of Welding and Joining, 12(6), 557–64, 2007
  8. ^ Simpson SW (2008) "Signature image stability and metal transfer in gas metal arc welding", Science & Technology of Welding and Joining, 13(2), 176–83
  9. ^ Simpson SW (2009) "Automated fault detection in gas metal arc welding with signature images", Australasian Welding Journal – Welding Research Supplement, 54, 41–47
  10. ^ 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
  11. ^ Simpson, SW (2007) "Statistics of signature images for arc welding fault detection", Science & Technology of Welding and Joining, 12(6), 557–64
  12. ^ 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
  13. ^ "Holden orders award-winning weldprint welding technology", Techwatch, Price Waterhouse Coopers, 12(6), 2002,
  14. ^ "Holden purchases award winning weldprint welding technology", Australian Technology Showcase http://www.techshowcase.nsw.gov.au/ News and Events (2002)
  15. ^ "University weld checker to be used by Holden", Australian Innovation Magazine, 3–5/02, 29
  16. ^ "Bright sparks join forces to take out Doherty Prize", The Australian (national newspaper)—Higher Education Supplement, 2 April 2003
  17. ^ 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
  18. ^ The LH and Hancock GJ (2005) "Strength of welded connections in G450 sheet steel", J Struct Eng, 131, 1561
  19. ^ "Car plant technology has medical spin-off", UniNews, USyd, 34(1), 1 (2002)

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