Hard chrome plating for ring grooves of piston crown

Abstract

Today hard chromium plating is generally accepted in engineering as an invaluable means of prolonging the life of all types of metal parts subjected to wear by friction or abrasion. Such parts can be protected from new or they can be reconditioned to as good as new state when they are worn and would otherwise be scrapped. At the commencement of this Innovation Technology Fund project, the chromium plating bath employed in the Teaching Company was a static system, which involved fixing the anode close to the surface of the work-piece while the work-piece was kept at the cathode. Electric current of appropriate strength was then applied and passed from the anode through the plating solution to the cathode. Chromium was then gradually deposited onto the unmasked surface of the work-piece. Thickness and quality of the chromium deposits depend on several operating parameters, viz. current density, temperature and concentration of the plating solution. Best electroplating result depends on the optimization of these operating parameters, which involves a very complicated analytic process. This project was aimed to improve the hard chrome plating process through (i) the development of a more efficient plating system and (ii) to predict plating thickness through the development of an empirical model between plating thicknesses and the input variables. On completion of this study, a rotary electroplating system was constructed and predictions of the plating thickness were achieved by using the Full Factorial Design (FFD) and the Neural Network (NN) methods. The values obtained by both methods were compared with the experimental values of the plating thickness to evaluate about the accuracy of the predictions. Within the range of input variables for the present study, the results showed that NN came ahead of the FFD in nearness of the predictions to the experimental values of plating thickness. The average errors in the plating thickness in the case of NN were less than that obtained using FFD (average error is 1.5% for NN as compared to 4.4% in case of FFD predictions; maximum deviation is 4.2% for NN as compared to 9.8% in case of FFD predictions). The information gained as well as the proposed methodology in this study contributes reliable and reproducible data on the absorption of chromium metal. A significant contribution has been made to provide in-depth understanding of the industrial electroplating process. Another significant contribution made to the present investigation is the development of a new approach in the hard chrome plating of ring grooves of piston crowns.