Accurate modeling of tire characteristics is one of the most challenging tasks. Many mathematical models can be used to fit measured data. Identification of the parameters of these models usually relies on least squares optimization techniques. Different researchers have shown that the proper selection of an initial set of parameters is key to obtain a successful fitting. Besides, the mathematical process to identify the right parameters is, in some cases, quite time-consuming and not adequate for fast computing. This paper investigates the possibility of using Artificial Neural Networks (ANN) to reliably identify tire model parameters. In this case, the Pacejka’s “Magic Formula” has been chosen for the identification due to its complex mathematical form which, in principle, could result in a more difficult learning than other formulations. The proposed methodology is based on the creation of a sufficiently large training dataset, without errors, by randomly choosing the MF parameters within a range compatible with reality. The results obtained in this paper suggest that the use of ANN to directly identify parameters in tire models for real test data is possible without the need of complicated cost functions, iterative fitting or initial iteration point definition. The errors in the identification are normally very low for every parameter and the fitting problem time is reduced to a few milliseconds for any new given data set, which makes this methodology very appropriate to be used in applications where the computing time needs to be reduced to a minimum.

Quality control of products is everyday more and more demanding. Machine vision is becoming one of the most efficient technologies for the reliable and fast control of different types of products. The more classical techniques in machine vision 2D are valuables in lots of applications, but insufficient when it is necessary a three-dimensional data of the object to study. Classical linear 3D laser detection scanners are not optimized for revolution elements, since the features extraction algorithm needs to be different in each inspection zone and there are shadow zones where the inspection is not possible. In this paper, a novel 3D laser based rotating scanner is described (Patent Pending request nº ES-P200600068). This approach enables inspecting revolution elements avoiding the problems mentioned before. This rotating scanner implementation in a 3D Steel tube Quality Control Application is also described.