Alexander Zeiler

Mathematical Modeling, Process Optimization and Quality Estimation in Side-trimming of Heavy Steel Plates


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ISBN:

978-3-8440-7712-4

Series:

Modellierung und Regelung komplexer dynamischer Systeme
Herausgeber: Univ.-Prof. Dr. Andreas Kugi (TU Wien), o. Univ.-Prof. Dr. Kurt Schlacher (JKU Linz) and Prof. Dr.-Ing. Wolfgang Kemmetmüller (TU Wien)
Wien / Linz

Volume:

Keywords:

steel industry; side trimming; quality monitoring; design optimization; machine vision; model-based estimation

Type of publication:

Thesis

Language:

English

Pages:

132 pages

Figures:

54 figures

Weight:

195 g

Format:

24 x 17 cm

Binding:

Paperback

Price:

45,80 € / 57,30 SFr

Published:

May 2022

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Abstract:

This thesis deals with the mathematical modeling, the inline measurement of quality parameters, and the optimization of the process and machine design of rolling-cut trimming shears for heavy steel plates. Side and longitudinal trimming of heavy plates constitute one of the last process steps in the finishing part of rolling mills. Thus, the product quality with respect to its geometrical shape is mainly determined by this process step. Traditionally, a mechanical rolling-cut shearing technique is utilized to conduct the plate trimming due to its high process speed. However, this technique frequently provokes quality defects on the plate geometry, in particular on the trimmed edges. These defects often bring along the necessity of costly post-processing of the steel plates.
The first part of this thesis is dedicated to gain a physical understanding of the rolling-cut shearing process. For this purpose, a suitable mathematical 3D model of the trimming shear is developed and validated by plant measurements. The gained insights are utilized in the second part to optimize the geometrical design of common rolling-cut trimming shears. The third part presents an automated inline quality inspection system for trimmed steel plates. This novel system consists of a charge-coupled device camera and 2D laser sensors, which allow for a complete and accurate estimation of the product quality without interfering the production process. The final part of this thesis is concerned with the optimization of process parameters for rolling-cut trimming shears. The goal of this optimization is to achieve a high and consistent product quality. A data set generated by the proposed quality inspection system serves as a basis for machine learning applications.