Synthetic Data Generation and Sampling for Online Training of DNNs in Manufacturing Supervised Learning Problems

The deployment of Industrial Internet offers abundant passive data from manufacturing systems and networks, which enables data-driven modeling with high-data-demand, advanced statistical models such as Deep Neural Networks (DNNs). Deep Neural Networks (DNNs) have proven to be remarkably effective in supervised learning in critical manufacturing applications, such as AI-enabled automatic inspection, quality modeling, etc. However, there is a lack of performance guarantee of DNN models primarily due to data class imbalance, shifting distribution, and multi-modality variables (e.g., time series and images) in training and testing datasets collected in manufacturing. Inspired by active data generation through Design of Experiments (DoE) and passive observational data collection for manufacturing data analytics, we propose a SynthetIc Data gEneration and Sampling (SIDES) framework, to provide adequate DNN model performance through the improvement of training data preparation. In the SIDES framework, a bi-level Hierarchical Contextual Bandits is proposed to provide a scientific way to integrate DoE and observational data sampling, which optimizes DNNs' online learning performance. Multimodality-Aligned Variational Autoencoder transforms the multimodal predictors from manufacturing into a shared low-dimensional latent space for controlled data generation from DoE and effective sampling from observational data. The merits of SIDES are evaluated by a real case study of printed electronics with a binary multimodal data classification problem. Results show the advantages of the cost-effective integration of DoE in improving the DNNs' online learning performance.