The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 29–36, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-29-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-3-2021, 29–36, 2021
https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-29-2021

  10 Aug 2021

10 Aug 2021

MODELING ROLLERS USING TERRESTRIAL LIDAR POINTS IN A HOT-ROLLING STEEL MILL

S. S. Deshpande1, M. Falk2, and N. Plooster2 S. S. Deshpande et al.
  • 1Surveying Engineering, Penn State University, Wilkes-Barre, USA
  • 2Falk-PLI, Portage, Indiana, USA

Keywords: Hot rollling steel-mill, Rollers, terrestrial lidar

Abstract. Terrestrial lidar scanners are increasingly being used in numerous indoor mapping applications. This paper presents a methodology to model rollers used in hot-rolling steel mills. Hot-rolling steel mills are large facilities where steel is processed to different shapes. In a steel sheet manufacturing process, a steel slab is reheated at one end of the mill and is passed through multiple presses to achieve the desired cross-section. Hundreds of steel rollers are used to transport the steel slab from one end of the mill to the other. Over a period of use, these rollers wore out and need replacement. Manual determination of the damage to the rollers is a time-consuming task. Moreover, manual measurements can be influenced by the operator’s judgment. This paper presents a methodology to model rollers in a hot-rolling steel mill using lidar points. A terrestrial lidar scanner was used to collect lidar points over the roller surfaces. Data from several stations were merged to create a single point cloud. Using a bounding box, lidar points on all the rollers were clipped and used in this paper. The clipped data consisted of the roller as well as outlier points. Depending on the scan angles of scanner stations, partial surfaces of the rollers were scanned. A right-handed coordinate frame was used where the X-axis passed through the centers of all the rollers, Y-axis was parallel to the length of the first roller, and the Z-axis was in the plumb direction. Using a standard diameter of the roller, model roller points were created to extract the rollers. Both the lidar data and the model points were converted to rectangular prism-shaped voxels of dimensions 15.24 mm (0.05 ft) × 15.24 mm in the X and Z directions and extending over the entire width of the roller in the Y-direction. Voxels containing at least 40 lidar points were considered valid. Binary images of both the lidar points and the model points were created in the X-Z axes using the valid voxels. The roller locations in the lidar image were located by performing 2D FFT image matching using the model roller image. The roller points at the shortlisted locations were fitted with a circle equation to determine the mean roller diameters and mean center locations (roller’s rotation axis). The outlier points were filtered in this process for each roller. The elevation at the top of every roller was determined by adding their radii and Z-coordinates of its centers. Incorrectly located and/or modeled rollers were identified by implementing moving-average filters. Positively identified roller points were further analyzed to determine surface erosions and tilts. The above methodology showed that the rollers can be effectively modeled using the lidar points.