Quality control of specular ceramic materials
Industry is lacking a method for the rapid and automated inspection of complex, glossy goods, especially if on-line, eg moving at high speed. These products still need to be inspected manually, which is labour-intensive, monotonous and expensive.
Most existing on-line inspection systems for specular surfaces only deliver qualitative results such as size, orientation or basic geometrical measures. Other powerful systems can only be applied to smooth, non-complex surfaces.
This PhD project aimed to develop a method and device to rapidly reverse engineer specular surfaces while they are on-line. The outcome is a device to generate a full representation of the surface geometry.
Additionally, an earlier implementation will for the first time be able to qualitatively flag the presence of defects, even for complex surfaces with high normal angles.
While specular ceramic tiles are employed as example application, the results of our work is directly applicable to all surfaces showing specular characteristics, such as metals, plastics and polished or lacquered materials.
We addressed the problem by examining what we coined the specular signature. This is the reflection of a line laser of the surface in question, made visible on a translucent screen. It contains all information of the surface normals and magnifies the tiniest defects. Unfortunately, any spatial information is lost. Standard laser triangulation on the other hand is highly inaccurate for specular surfaces but preserves spatial information. Our unique device fuses these two independent measuring techniques and, for the first time, will allow for the fast, objective and repeatable quality control of complete batches of specular objects.
The specular signature is captured and dynamically thresholded. A specially developed, novel, real-time, multi-scale line tracing algorithm is then used to extract a maximum of information of it. Afterwards, suspicious regions that point towards abnormalities can be identified.
Simultaneously, standard laser triangulation with centre of gravity peak detection is applied and a likely signature is calculated. At the moment, we are developing ways to effectively fuse the signatures together.
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Centre for Machine Vision is part of Bristol Robotics Laboratory, a centre of excellence in the Department of Engineering, Design and Mathematics at UWE Bristol.