Articles | Volume 6, issue 1
https://doi.org/10.5194/jsss-6-53-2017
https://doi.org/10.5194/jsss-6-53-2017
Regular research article
 | 
31 Jan 2017
Regular research article |  | 31 Jan 2017

Concept for completeness checking of joined structures exemplified on rail vehicle car body shells

Uwe Jurdeczka

Abstract. Completeness checking of constructional steel structures with several hundred weld-on parts (mountings, such as car body shells/chassis for rail vehicles, for example) still represents a great challenge. It has been performed manually so far. By the use of 3-D scanners, it is possible to obtain sufficiently comprehensive information about the actual configuration (as-is state) of the constructional steel structure to be checked. For this purpose, the laser beam geometry must be contemplated. By using an adapted algorithm for post-processing of the resulting point clouds, difference figures are superimposed over the respective 3-D model as the target state. Therefore, an algorithm was adapted and applied here. The difference figures are useful for signalling the possible deviations and the missing parts, at least, however, suspect points. Thus, the disadvantages of the exclusively manual completeness checking practiced so far, i.e. tiredness and slowness of the inspector, can largely be avoided. The 3-D model-based inspection procedure allows one to handle the variation in the target state with just a few volumes of equal configuration (e.g. less than 10 trains with equal configuration). There is a general problem that the tolerance for the structure is in the same range as the size of the mountings. It is possible to solve this problem by dividing the test item as well as the point cloud into areas. Superposing the 3-D model by point clouds and calculating the difference figures will be performed area by area.

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Short summary
Completeness checking of constructional steel structures with several hundred weld-on parts still represents a great challenge. By the use of 3-D scanners, it is possible to obtain sufficiently comprehensive information about the actual configuration (as-is state). By using an adapted algorithm for post-processing of the resulting point clouds, difference figures are superimposed over the respective 3-D model as the target state. The difference figures signal the missing parts.