| 摘要 |
<p>The present invention relates to a method and apparatus for predicting the non-color properties of a chemical mixture, such as an automotive paint, using an artificial neural network. The neural network includes an input layer having nodes for receiving input data related to the chemical components of the mixture and environmental and process conditions that can affect the properties of the mixture. An output layer having nodes generate output data which predict the properties of the chemical mixture as a result of variation of the input data. A hidden layer having nodes is connected to the nodes in the input and output layers. Weighted connections connect the nodes of the input, hidden and output layers and threshold weights are applied to the hidden and output layer nodes. The connection and threshold weights have values to calculate the relationship between input data and output data. The data to the input layer and the data to the output layer are interrelated through the neural network's nonlinear relationship. When implemented, accurate predictions of the final properties of the mixture can be obtained. The invention is especially useful in relating automotive paint formulation variables (e.g., paint ingredient amounts and application process conditions) to physical properties (e.g., viscosity, sag), appearance (e.g., hiding, gloss, distinctness of image) or other measured properties enabling comparison of formula properties to target values or tolerances without expensive experimental work.</p> |
