Publications / 2002 Proceedings of the 19th ISARC, Washington, USA

Artificial Intelligence-Based Quality Control of Aggregate Production

H. Kim, C. Haas, A. Rauch
Pages 369-374 (2002 Proceedings of the 19th ISARC, Washington, USA, ISSN 2413-5844)

This paper discusses a quality control method, based on artificial neural networks, that enables a plant operator to quickly detect property variations during the production of stone aggregates. The group texture concept in digital image analyses, two-dimensional wavelet transforms, and artificial neural networks are reviewed first. An artificial intelligence based aggregate classification system is then described. This system relies on three-dimensional aggregate particle surface data, acquired with a laser profiler, and conversion of this data into digital images. Two-dimensional wavelet transforms are applied to the images and used to extract important features that can help to differentiate between in-spec and out-of-spec aggregates. These wavelet-based features are used as inputs to an artificial neural network, which is used to assign a predefined class to the aggregate sample. Verification tests show that this approach can potentially help a plant operator determine, in a fast and accurate manner, if the aggregates currently being produced are in-spec or out-of-spec.

Keywords: aggregate, artificial neural networks, group texture, laser profiling, wavelet transforms