Two-stage imbalanced learning-based quality prediction method for wheel hub assembly

Abstract

Wheel hub assembly plays a pivotal role in automobile manufacturing, and its quality directly affects the overall reliability of the automobile transmission system. Accurate prediction of wheel bearing assembly quality can save costs and increase productivity. However, the imbalanced data in wheel hub assembly production led to poor model prediction ability, which poses challenges to existing quality prediction methods. To this end, a two-stage imbalanced learning-based quality prediction method is proposed for wheel hub assembly. Concretely, an improved extremely randomized tree is developed for key process variables adaptive selection in the wheel hub assembly production. Then, a Differential Evolution-based Cost-sensitive AdaBoost (DE-CostAda) method is proposed for quality prediction under imbalanced data scenarios. Finally, a real-world experiment has demonstrated the superiority of the proposed method compared with several machine learning and peer imbalance learning methods. The experimental results also reveal some key process variables related to the final product quality, which provides guidance for process optimization of wheel hub assembly.