Observing the weld pool surface and measuring its geometrical parameters is a key to developing the next-generation intelligent welding machines that can mimic a skilled human welder who observes the weld pool to adjust welding parameters. It also provides us an effective way to improve and validate welding process modeling. Although different techniques have been applied in the past few years, the dynamic specular weld pool surface and the strong weld arc complicate these approaches and make the observation /measurement difficult. In this dissertation, a novel machine vision system to measure three-dimensional gas tungsten arc weld pool surface is proposed, which takes advantage of the specular reflection. In the designed system, a structured laser pattern is projected onto the weld pool surface and its reflection from the specular weld pool surface is imaged on an imaging plane and recorded by a high-speed camera with a narrow band-pass filter. The deformation of the molten weld pool surface distorts the reflected pattern. To derive the deformed surface of the weld pool, an image processing algorithm is firstly developed to detect the reflection points in the reflected laser pattern. The reflection points are then matched with their respective incident rays according to the findings of correspondence simulations. As a result, a set of matched incident ray and reflection point is obtained and an iterative surface reconstruction scheme is proposed to derive the three-dimensional pool surface from this set of data based on the reflection law. The reconstructed results proved the effectiveness of the system. Using the proposed surface measurement (machine vision) system, the fluctuation of weld pool surface parameters has been studied. In addition, analysis has been done to study the measurement error and identify error sources in order to improve the measurement system for better accuracy. The achievements in this dissertation provide a useful guidance for the further studies in on-line pool measurement and welding quality control.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_diss-1534 |
| Date | 01 January 2007 |
| Creators | Song, Hongsheng |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Doctoral Dissertations |
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