A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. / Master of Science / This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/79620 |
| Date | January 2017 |
| Creators | Li, Jingran |
| Contributors | Materials Science and Engineering, Yu, Hang, Kapania, Rakesh K., Jin, Ran |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution 3.0 United States, http://creativecommons.org/licenses/by/3.0/us/ |
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