Enhancing aerospace products quality with ISOMAP key factor identification

The nonlinear and high-dimensional nature of the impact factors affecting the production quality of aerospace products represents a major difficulty for the quality control in the aerospace industry. To obtain the impact factors affecting the product quality, it is plausible to perform dimensionality reduction on acquired samples before further manipulation. In this work, the isometric feature mapping (ISOMAP) algorithm of stream learning is employed to perform nonlinear dimensionality reduction on aerospace data. This enables a calculation of the correlation coefficients between the principal components after dimensionality reduction and the original factors, the classification of the correspondence, and the ranking of the principal components according to their degree of influence. The experimental results show that the algorithm is able to carry out correlation analysis of 17 factors affecting the production quality of aerospace products, and analyze the 13 main factors affecting the production quality of aerospace products, and the degree of influence, in descending order, are the rationality of measurement methods, the rationality of test point design, tool wear, equipment normalization rate, the degree of equipment aging, the rationality of program design, the degree of material defects, the rationality of process route design, the rationality of tooling design, the technical level of personnel, the level of personnel experience, the personnel work status, and operational standardization. The ISOMAP algorithm was used to reduce the dimensionality of these 13 factors to form and rank the six main influence components, thus eliminating redundant factors, highlighting main influence features and extracting the intrinsic relation in data. The data analysis conclusions can facilitate a prevention of potential quality issues in aerospace production. To ensure the enhancement of the quality of aerospace product production, it is recommended that standard automated measurement methods be employed wherever feasible. Additionally, it is recommended that the regular maintenance of machining tools and equipment be strengthened to ensure that the machining tools and equipment are in perfect condition.