Abstract
Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.
Highlights
The industries and national economy in China have rapidly developed [1,2]
This was the first report in this field of the optimization of performance parameters based on grey relational analysis and predictions of missing data based on back propagation (BP) neural network
The results showed that the predictive value of BP neural network showed more precision than grey system theory
Summary
The industries and national economy in China have rapidly developed [1,2]. The foundry industry is the basis of modern equipment manufacturing, which occupies a very important position in the national economy [3,4]. The proportion of sand casting has increased nearly 90% [11], and it is of great significance to select appropriate performance parameters of sand casting for the safe foundry production. The common optimization method of sand casting performance parameters is Taguchi’s method [14], but this method has some disadvantages. The purpose of Taguchi’s method is to reduce the effects of mutagenic factors rather than removing the mutagenic factors to improve quality. It is necessary to find a simple method for optimizing sand casting performance parameters. A novel optimization method of performance parameters based on grey relational analysis was introduced here
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