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Aluminum alloy is a kind of high-strength metal which has toughness and lightness, people often use it on the component of transportation car body, sheet metal, wheel rim, etc. Even though the aluminum alloy has toughness and lightness, some of factors like high temperature, acid rain, gravel, dust and dirt, all these factors can cause increasing the corrosion risk on the surface of aluminum alloy. To solve this problem, coating is applied to the surface of aluminum alloy to protect aluminum alloy from external factors and enhance its corrosion resistance. In this research the test sample is heated curing, then the aluminum alloy and the lacquer are processed through chemical pretreatment due to make both kinds of material tight and anti-corrosion. Therefore, the composition and proportion of chemical ingredients for chemical pretreatment is extremely important. This research conducted an empirical study on a domestic metal coating factory. The DMAIC method of six standard deviations is used to establish the project process, with the process data obtained from the case company, the Taguchi method is used to find the current best factor to improve the process capability of the coating process and to establish an inverse transfer neural model to predict the aluminum alloy. The corrosion length can be fully controlled for customer needs. This research combines the relevant industries to find out the key factors affecting the process, and improves the process stability through process parameter modification.In addition, a neural network prediction model such as the prediction result with an accuracy rate more than 95% is established, which reduce the waste time and cost of subsequent experiments, then predict the corrosion length of aluminum alloy metal to accurately find the processing parameters that meet the different needs of customers, and provides customers with budget Choose coating quality.
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