Off-line scan path planning for robotic NDT
Measurement, ISSN: 0263-2241, Vol: 122, Page: 284-290
2018
- 50Citations
- 70Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
This work presents computer-aided scan path generation for robotic non-destructive testing of complex shaped test-pieces. Off-line programmed scan path was used to robotically inspect an aluminium fixed leading edge skin panel of an aircraft wing by means of swept frequency eddy currents method. Eddy currents probe was deployed by means of a six-axis robotic arm KUKA KR5 arc. Reverse engineering of the test-piece was carried out to reconstruct CAD model of its surface. Positioning accuracy of the performed continuous scan was measured with a laser tracker in accordance with ISO 9283:1998 and is reported in the paper. The positional uncertainty of the NDT scan calculated as the standard deviation of the measured path coordinates from the command path coordinates does not exceed 0.5 mm which is rather moderate taking in account uncertainties associated with the off-line robot programming.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0263224118301131; http://dx.doi.org/10.1016/j.measurement.2018.02.020; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85044122651&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0263224118301131; https://api.elsevier.com/content/article/PII:S0263224118301131?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0263224118301131?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.measurement.2018.02.020
Elsevier BV
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