A cyber manufacturing system (CMS) is a concept generated from the cyber-physical system (CPS), providing adequate data and computation resources to support efficient and optimal decision making. Examples of these decisions include production control, variation reduction, and cost optimization. A CMS integrates the physical manufacturing equipment and computation resources via Industrial Internet, which provides low-cost Internet connections and control capability in the manufacturing networks. Traditional quality engineering methodologies, however, typically focus on statistical process control or run-to-run quality control through modeling and optimization of an individual process, which makes it less effective in a CMS with many manufacturing systems connected. In addition, more personalization in manufacturing generates limited samples for the same kind of product designs, materials, and specifications, which prohibits the use of many effective data-driven modeling methods. Motivated by Additive Manufacturing (AM) with the potential to manufacture products with a one-of-a-kind design, material, and specification, this dissertation will address the following three research questions:
(1) how can in situ data be used to model multiple similar AM processes connected in a CMS (Chapter 3)?
(2) How to improve the accuracy of the low-fidelity first-principle simulation (e.g., finite element analysis, FEA) for personalized AM products to validate the product and process designs (Chapter 4) in time?
(3) And how to predict the void defect (i.e., unmeasurable quality variables) based on the in situ quality variables.
By answering the above three research questions, the proposed methodology will effectively generate in situ process and quality data for modeling multiple connected AM processes in a CMS. The research to quantify the uncertainty of the simulated in situ process data and their impact on the overall AM modeling is out of the scope of this research. The proposed methodologies will be validated based on fused deposition modeling (FDM) processes and selective laser melting processes (SLM). Moreover, by comparing with the corresponding benchmark methods, the merits of the proposed methods are demonstrated in this dissertation. In addition, the proposed methods are inherently developed with a general data-driven framework. Therefore, they can also potentially be extended to other applications and manufacturing processes. / Doctor of Philosophy / Additive manufacturing (AM) is a promising advanced manufacturing process that can realize the personalized products in complex shapes with unprecedented materials. However, there are many quality issues that can restrict the wide deployment of AM in practice, such as voids, porosity, cracking, etc. To effectively model and further mitigate these quality issues, the cyber manufacturing system (CMS) is adopted. The CMS can provide the data acquisition functionality to collect the real-time process data which directly or indirectly related to the product quality in AM. Moreover, the CMS can provide the computation capability to analyze the AM data and support the decision-making to optimize the AM process. However, due to the characteristics of AM process, there are several challenges effectively and efficiently model the AM data. First, there are many one-of-a-kind products in AM, and leads to limited observations for each product that can support to estimate an accurate model. Therefore, in Chapter 3, I would like to discuss how to jointly model personalized products by sharing the information among these similar-but-non-identical AM processes with limited observations. Second, for personalized product realization in AM, it is essential to validate the product and process designs before fabrication quickly. Usually, finite element analysis (FEA) is employed to simulate the manufacturing process based on the first-principal model. However, due to the complexity, the high-fidelity simulation is very time-consuming and will delay the product realization in AM. Therefore, in Chapter 4, I would like to study how to predict the high-fidelity simulation result based on the low-fidelity simulation with fast computation speed and limited capability. Thirdly, the defects of AM are usually inside the product, and can be identified by the X-ray computed tomography (CT) images after the build of the AM products. However, limited by the sensor technology, CT image is difficult to obtain for online (i.e., layer-wise) defect detection to mitigate the defects. Therefore, as an alternative, I would like to investigate how to predict the CT image based on the optical layer-wise image, which can be obtained during the AM process in Chapter 5. The proposed methodologies will be validated based on two types of AM processes: fused deposition modeling (FDM) processes and selective laser melting processes (SLM).
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/104655 |
| Date | 16 August 2021 |
| Creators | Wang, Lening |
| Contributors | Industrial and Systems Engineering, Jin, Ran, Wernz, Christian, Kong, Zhenyu, Johnson, Blake |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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