Identification of CTQs for Complex Products Based on Mutual Information and Improved Gravitational Search Algorithm

Abstract

The identification of CTQs for complex products is the first step to implement quality control. To improve the efficiency and accuracy of CTQs identification, we propose a novel hybrid approach based on mutual information and improved gravitational search algorithm, which has advantages of filter and wrapper. At first, the information relevance and redundancy are measured by mutual information. Then, the improved gravitational search algorithm is used to search the CTQs. Experimentation is carried out using 2 UCI data sets, and the classification capability of CTQs is tested by SVM and tenfold cross validation. The results show that the presented method is verified to be effective and practically applicable.

Auto-encoder-based algorithm for the selection of key characteristics for products to reduce inspection efforts

“…Mutual information uses OK and NOK labels to measure the redundancy and relevance of a feature. The Improved Gravitational Search algorithm is an optimization algorithm, which then uses the laws of Newtonian mechanics and the measured redundancy and relevance of the features to identify the KCs (Wang et al. , 2015).…”

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

“…They proposed the Mahalanobis-Taguchi System (MTS) based on the RELIEFF algorithm, which combined the least-squares regression with the state-space model to identify KQCs [13]. Wang et al proposed an improved IGSA algorithm based on reverse learning and immune algorithm, which combined the advantages of filtering efficiency and high-precision packaging to solve the problem of high-quality feature output dimension [14]. The critical procedure determined by this method are subjective and one-sided, ignoring the impact of the manufacturing process on product quality.…”