Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process

Abstract

The hard chromium plating process aims at creating a coating of hard and wear-resistant chromium with a thickness of some microns directly on the metal part, without the insertion of copper or nickel layers. It is one of the most difficult electroplating processes due to the influence of the hydrogen evolution that occurs on the cathode surface simultaneously to the chromium deposition. Chromium plating is characterized by high levels of hardness and resistance to wear and it is thanks 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 the coating, adherence and micro-fissures of the latter. This micro-fissured structure is what provides the optimal hardness of the layers. The electro-deposited chromium layer is not uniformly distributed: there are zones such as sharp edges or points where deposits are highly accentuated, while deposits are virtually nonexistent in holes or in the undercuts. The hard chromium plating process is one of the most effective ways of protecting the base material in a hostile environment or improving surface properties of the base material. However, in the electroplating industry, electro-platers are faced with many problems and often achieve undesirable results on chromium-plated materials. Problems such as matt deposition, milky white chromium deposition, rough or sandy chromium deposition and insufficient thickness or hardness are the most common problems faced in the electroplating industry. Finally, it must be remarked that defects in the coating locally lower the corrosion resistance of the layer and that the decomposition of chromium hydrides causes the formation of a network of cracks in the coating. This innovative research work uses an evolutionary support vector regression algorithm for the prediction of the thickness of the chromium layer in a hard chromium plating process. Evolutionary support vector machines (ESVMs) is a novel technique that assimilates the learning engine of the state-of-the-art support vector machines (SVMs) but evolves the coefficients of the decision function by means of evolutionary algorithms (EAs). In this sense, the current research is focused on the estimation of the hyper-parameters required for the support vector machines technique for regression (SVR), by means of evolutionary strategies. The results are briefly compared with those obtained by authors in a previous paper, where a model based on an artificial neural network was tuned using the design of experiments (DOE).