Product quality improvement method in manufacturing process based on kernel optimisation algorithm
针对制造过程质量数据混合、分布不均、高维和耦合问题，提出基于等价关系的数据预处理和KML-SVM算法，通过核优化提高分类精度，并用航空发动机公司实际数据验证。
Quality data in manufacture process has the features of mixed type, uneven distribution, dimension curse and data coupling. To apply the massive manufacturing quality data effectively to the quality analysis of the manufacture enterprise, the data pre-processing algorithm based on equivalence relation is employed to select the characteristic of hybrid data and preprocess data. KML-SVM (Optimised kernel-based hybrid manifold learning and support vector machines algorithm) is proposed. KML is adopted to solve the problems of manufacturing process quality data dimension curse. SVM is adopted to classify and predict low-dimensional embedded data, as well as to optimise support vector machine kernel function so that the classification accuracy can be maximised. The actual manufacturing process data of AVIC Shenyang Liming Aero-Engine Group Corporation Ltd is demonstrated to simulate and verify the proposed algorithm.