Prediction Algorithm for Aerospace Product Quality Classification

Productive activities in aerospace industry are characterized by their use of multiple species, small batches, discrete types etc.Therefore, the quality classification of aerospace products is a typical small-sample classification problem.The existing general algorithms are inadequate for an effective classification as well as prediction of aerospace production quality.To fill the gap, the research presents a specialized algorithm to the classification prediction of aerospace production quality by integrating isometric feature mapping (ISOMAP) with support vector machine (SVM).The accuracy of each main kernel function is compared, followed by a determination of the algorithm model using a radial basis function (RBF).Experiments show that the proposed algorithm significantly improves classification and prediction accuracy for aerospace product quality, thereby enabling the timely identification of potential quality issues and the subsequent reduction of quality issues in aerospace products.