A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling

EasyChair Preprint 3573

Abstract

This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPro camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy which is in line with the rolling standards, i.e. with a root mean square error less than 2.5 mm.

Keyphrases: High temperature steel production, Manufacturing and Automation, Metrology, computer vision, uncertainty quantification

@booklet{EasyChair:3573,
  author    = {Peng Wang and Yueda Lin and Ree Muroiwa and Simon Pike and Lyudmila Mihaylova},
  title     = {A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling},
  howpublished = {EasyChair Preprint 3573},
  year      = {EasyChair, 2020}}