Over the past decade, research into computer vision has proliferated with the goal to incorporate artificial intelligence into a wide range of applications. These applications can be as sophisticated as intelligent assistants in smartphones and self-driving cars or as mundane as text and face recognition. While most of these applications are software based, they represent unique challenges when it comes to industrial implementation. This thesis concentrates on an optical non-destructive testing (NDT) and automatic classification methodology using customized image processing techniques. In contrast to conventional spatial analyses, which are highly susceptible to noise and human perception, our proposed transform domain approach provides a high degree of robustness and flexibility in feature selection and hence a better classification efficiency. Our presented algorithm classifies the Part-Under-Test (PUT) into two bins of either acceptable or faulty using transform domain techniques in conjunction with a classifier. Because the classification is critically dependent on the features extracted from these images, a sophisticated scalable database was created. This thesis applies transform domain techniques such as Discrete Wavelet Transform (DWT) and Rotated Wavelet Transform (RWT) for feature extraction and then classifies the PUT based on those features. Although, this approach achieves promising classification efficiency, it does not meet industrial standards. It was concluded that in order to achieve those standards, the effect of emissivity fluctuations of the PUT should be negated. The research was then extended to apply an image acquisition algorithm in the form of shape from polarization. The approach exploits the partially linearly polarization of reflected light from the PUT surface. It was observed that this method could not only detect if the PUT is faulty or fault free, but also highlight the locations of the flaws.

The need for reliable and cost-effective testing procedures for Additive Manufacturing (AM) is growing. In this Dissertation, the development of a new computational-experimental method based on the realization of specific testing artifacts to address this need is presented. This research is focused on one of the widely utilized AM technologies, Fused Deposition Modeling (FDM), and can be extended to other AM technologies as well. In this method, testing artifacts are designed with simplified boundary conditions and computational domains that minimize uncertainties in the analyses. Testing artifacts are a combination of thin and thick cantilever structures, which allow measurement of natural frequencies, mode shapes, and dimensions as well as distortions and deformations. We apply Optical Non-Destructive Testing (ONDT) together with computational methods on the testing artifacts to predict their natural frequencies, thermal flow, mechanical properties, and distortions as a function of 3D printing parameters. The complementary application of experiments and simulations on 3D printed testing artifacts allows us to systematically investigate the density, porosity, moduli of elasticity, and Poisson’s ratios for both isotropic and orthotropic material properties to better understand relationships between these characteristics and the selected printing parameters. The method can also be adapted for distortions and residual stresses analyses. We optimally collect data using a design of experiments technique that is based on regression models, which yields statistically significant data with a reduced number of iterations. Analyses of variance of these data highlight the complexity and multifaceted effects of different process parameters and their influences on 3D printed part performance. We learned that the layer thickness is the most significant parameter that drives both density and elastic moduli. We also observed and defined the interactions among density, elastic moduli, and Poisson’s ratios with printing speed, extruder temperature, fan speed, bed temperature, and layer thickness quantitatively. This Dissertation also shows that by effectively combining ONDT and computational methods, it is possible to achieve greater understanding of the multiphysics that governs FDM. Such understanding can be used to estimate the physical and mechanical properties of 3D printed components, deliver part with improved quality, and minimize distortions and/or residual stresses to help realize functional components.

The need for reliable and cost-effective testing procedures for Additive Manufacturing (AM) is growing. In this Dissertation, the development of a new computational-experimental method based on the realization of specific testing artifacts to address this need is presented. This research is focused on one of the widely utilized AM technologies, Fused Deposition Modeling (FDM), and can be extended to other AM technologies as well. In this method, testing artifacts are designed with simplified boundary conditions and computational domains that minimize uncertainties in the analyses. Testing artifacts are a combination of thin and thick cantilever structures, which allow measurement of natural frequencies, mode shapes, and dimensions as well as distortions and deformations. We apply Optical Non-Destructive Testing (ONDT) together with computational methods on the testing artifacts to predict their natural frequencies, thermal flow, mechanical properties, and distortions as a function of 3D printing parameters. The complementary application of experiments and simulations on 3D printed testing artifacts allows us to systematically investigate the density, porosity, moduli of elasticity, and Poisson’s ratios for both isotropic and orthotropic material properties to better understand relationships between these characteristics and the selected printing parameters. The method can also be adapted for distortions and residual stresses analyses. We optimally collect data using a design of experiments technique that is based on regression models, which yields statistically significant data with a reduced number of iterations. Analyses of variance of these data highlight the complexity and multifaceted effects of different process parameters and their influences on 3D printed part performance. We learned that the layer thickness is the most significant parameter that drives both density and elastic moduli. We also observed and defined the interactions among density, elastic moduli, and Poisson’s ratios with printing speed, extruder temperature, fan speed, bed temperature, and layer thickness quantitatively. This Dissertation also shows that by effectively combining ONDT and computational methods, it is possible to achieve greater understanding of the multiphysics that governs FDM. Such understanding can be used to estimate the physical and mechanical properties of 3D printed components, deliver part with improved quality, and minimize distortions and/or residual stresses to help realize functional components.