Deep Probabilistic Learning for Process Quality Evaluation With a Case Study of Gear Hobbing Process

Abstract

Conventional semisupervised learning approaches are limited by the reliance on assumptions of samples (manifold assumption, cluster assumption, and smoothness assumption) and may fail to reach their full potential when it comes to industrial scenarios with complicated data characteristics. In this article, a semisupervised process quality evaluation framework is proposed based on conditional variational auto-encoder. Label inference process is utilized to handle unlabeled samples during training stage and to directly predict the label during the evaluation stage. Real-valued nonvolume preserving (Real NVP) is introduced to optimize the posterior distribution in order to increase flexibility. The proposed framework does not rely on assumptions of data distributions and it learns the hidden distributions from samples. Gear hobbing simulation and experiment investigations are conducted to verify the effectiveness of the proposed framework. The results indicate that the proposed method can achieve remarkable classification performance compared with other state-of-the-art approaches.