The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process

Authors:
F. SáNchez Lasheras;J. A. ViláN ViláN;P. J. GarcíA Nieto;J. J. Del Coz DíAz
Affiliations:
Research Department, Tecniproject SL, C/ Marqués de Pidal 7, 33004 Oviedo, Spain;Department of Mechanical Engineering, University of Vigo, 36310 Vigo, Spain;Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain;Department of Construction, University of Oviedo, 33204 Gijón, Spain
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

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Abstract

The hard chromium plating process aims at creating a coating of hard and wear-resistant chromium with a thickness of some micrometres directly on the metal part without the insertion of copper or nickel layers. Chromium plating features high levels of hardness and resistance to wear and it is due to these properties that they can be applied in a huge range of sectors. Resistance to corrosion of a hard chromium plate depends on the thickness of its coating, and its adherence and micro-fissures. This micro-fissured structure is what provides the optimal hardness of the layers. The hard chromium plating process is one of the most effective ways of protecting the base material against a hostile environment or improving the surface properties of the base material. However, in the electroplating industry, electroplaters are faced with many problems and undesirable results with chromium plated materials. Common problems faced in the electroplating industry include matt deposition, milky white chromium deposition, rough or sandy chromium deposition and insufficient thickness and hardness. This article presents an artificial neural network (ANN) model to predict the thickness of the layer in a hard chromium plating process. The optimization of the ANN was performed by means of the design of experiments theory (DOE). In the present work the purpose of using DOE is twofold: to define the optimal experiments which maximize the ratio of the model accuracy, and to minimize the number of necessary experiments (ANN models trained and validated).