Advanced Camera Detection and Measurement System in the Czochralski Process
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In this thesis the Czochralski process is introduced as well as the technological setupused in the process. The three problems this thesis aim to solve are presented: implementation of automatic cold ingot detection, automatic detection of structure loss in the body phase and automatic temperature detection in the neck stabilization phase. After theintroduction, the relevant theory is presented. This thesis references the automatic cold ingot detection algorithm developed in a previous project heavily, so this work is presentedin a separate chapter. Then each of the three main problems are described and solutionsare proposed, tested and discussed for each of them separately. At the end of this thesisis a chapter with conclusions of the proposed problem solutions. The thesis resulted inproblems being uncovered in the implemented automatic cold ingot detection which needsto be further analyzed and tuned for a satisfactory performance in the factory. For thestructure loss detection, several approaches were proposed. Most of the approaches canbe discarded, but the mean value approach and the machine learning approach both seemviable for a robust detection of the structure loss phenomenon. In the case of automatictemperature detection in the neck stabilization phase, there were problems with the cameranot having a high enough resolution to get precise results. If a new camera is acquired thisthesis proposes two separate methods which can be viable for automatically detecting thedesired temperature in this phase, a machine learning approach and a dynamic programmingapproach, where the problem is divided into smaller parts.