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, multi-modality variables (e.g., time series and images) in training and testing datasets collected in manufacturing. Moreover, implementing these models on the manufacturing shop floor is difficult due to limitations in human-machine interaction. 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 with a Graphical User Interface named SIDESync. This framework is designed to streamline SIDES execution within manufacturing environments, to provide adequate DNN model performance through the improvement of training data preparation and enhancing human-machine interaction. 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 SIDESync Graphical User Interface (GUI), developed using the Streamlit library in Python, simplifies the configuration, monitoring, and analysis of SIDES experiments. This streamlined approach facilitates access to the SIDES framework and enhances human-machine interaction capabilities. 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. / Master of Science / The Industrial Internet's growth has brought in a massive amount of data from manufacturing systems leading to advanced data analysis methods using techniques like Deep Neural Networks (DNNs). These powerful models have shown great promise in critical manufacturing tasks, such as AI-driven quality control. However, challenges remain in ensuring these models perform well. For example, the lack of good data results in models with poor performance. Furthermore, deploying these models on the manufacturing shop floor poses challenges due to limited human-machine interaction capabilities. To tackle these challenges, we introduce the SynthetIc Data gEneration and Sampling (SIDES) framework with a user-friendly interface called SIDESync to enhance the human-machine interaction. This framework will improve how training data is prepared, ultimately boosting the performance of DNN models. Within this framework, we proposed a method called bi-level Hierarchical Contextual Bandits that combines real-world data sampling with a technique called Design of Experiments (DoE) to help Deep Neural Networks (DNNs) learn more effectively as they operate. We also used a tool called a Multimodality-Aligned Variational Autoencoder, which helps convert various types of manufacturing data (like sensor readings and images) into a standard format. This conversion makes it easier to generate new data from experiments and efficiently use real-world data samples. The SIDESync Graphical User Interface (GUI) is created using Python's Streamlit library. It makes setting up, monitoring, and analyzing SIDES experiments much easier. This user-friendly system improves access to the SIDES framework and boosts interactions between humans and machines. To prove how effective SIDES is, we conducted a real case study of data collected from printed electronics manufacturing. We focused on a problem where we needed to classify the final product quality using in-situ data with DNN model prediction. Our results clearly showed that integrating DoE improved how DNNs learned online, all while keeping costs in check. This work opens up exciting possibilities for making data-driven decisions in manufacturing smarter and more efficient.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119181 |
| Date | 29 May 2024 |
| Creators | Thiyagarajan, Prithivrajan |
| Contributors | Industrial and Systems Engineering, Jin, Ran, Ellis, Kimberly P., Sarin, Subhash C. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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