Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF

Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF

Ratio of Coefficients of Variation for Comparing the Dispersions of Several Independent Populations

The coefficient of variation (CV) is an important and useful statistical tool for comparing several populations. In cases where there are multiple populations with different means and variances, the ratio of the coefficients of variation (CVs) is a good way to compare the dispersion of the populations. Because of the possible minor differences between multiple CVs and the lack of a robust interpretation, the ratio of CVs is more accurate than the difference of CVs. When a statistical analysis consists of some simultaneous statistical tests such as equality of several CVs, multiple testing is useful. As an example, the multiple testing about the ratio of CVs is performed to evaluate and compare scale of hand, foot and mouth disease (SHFMD) of 79911 patients between January 2010 and December 2017 in the three Malaysian provinces

Exploratory Study of Andropause Syndrome in 40-65 Years in Arak: A Cross Sectional Study

Objective: Andropause syndrome is caused due to the deficiency in sex hormones and brings about symptoms of physical, psychological, and sexual nature. This study aims at investigating the prevalence of andropause syndrome in 40-65-year-old men in the central city of Iran (Arak). Materials and methods: This study is a descriptive cross-sectional research conducted on 600 men living in the city of Arak in 2017. The subjects were selected through cluster sampling and qADAM was used for collecting data including three areas (level of energy, psychological and sexual). The data were analyzed through descriptive and inferential statistics (independent t-test and ANOVA) and using SPSS 16. Results: The results showed that the average questionnaire score increased with an increase in age up to 50 years and above. Correlation test for the three subscales of physical, psychological, and sexual showed that the psychological subscale had the highest correlation with andropause score (p < 0.05). Among the items related to the subscales, the statement” I feel my sex drive has decreased” with mean and standard deviation of 3.62 ± 1.06 had the highest correlation with andropause and the statement “I feel I have no value for society” with mean and standard deviation of 1.7 ± 0.86 had the lowest correlation with andropause.   Conclusion: Andropause age in Arak is 50 years and above. Average questionnaire score showed a positive direct relation with age. Decreased sex drive had the highest correlation and losing social value had the lowest correlation with andropause state

Multivariate Calibration and Experimental Validation of a 3D Finite ElementThermal Model for Laser Powder-Bed Fusion Metal Additive Manufacturing

Metal additive manufacturing (AM) typically suffers from high degrees of variability in the properties/performance of the fabricated parts, particularly due to the lack of understanding and control over the physical mechanisms that govern microstructure formation during fabrication. This paper directly addresses an important problem in metal AM: the determination of the thermal history of the deposited material. Any attempts to link process to microstructure in AM would need to consider the thermal history of the material. In situ monitoring only provides partial information and simulations may be necessary to have a comprehensive understanding of the thermo-physical conditions to which the deposited material is subjected. We address this in the present work through linking thermal models to experiments via a computationally efficient surrogate modeling approach based on multivariate Gaussian processes (MVGPs). The MVGPs are then used to calibrate the free parameters of the multi-physics models against experiments, sidestepping the use of prohibitively expensive Monte Carlo-based calibration. This framework thus makes it possible to efficiently evaluate the impact of varying process parameter inputs on the characteristics of the melt pool during AM. We demonstrate the framework on the calibration of a thermal model for laser powder bed fusion AM of Ti-6Al-4V against experiments carried out over a wide window in the process parameter space. While this work deals with problems related to AM, its applicability is wider as the proposed framework could potentially be used in many other ICME-based problems where it is essential to link expensive computational materials science models to available experimental data.NASA’s Space Technology ResearchGrants Program, Grant No.NNX15AD71G. NSF-DGE-1545403, “NRT-DESE:Data-Enabled Discovery and Design of Energy Materials (D3EM).” NSF-CMMI-153453

Simultaneous retrograde venous and anterograde arterial bullet embolism: a case report

Background Bullet embolus is a rare condition following gunshot injuries and represents a clinical challenge regarding both diagnosis and management. Case presentation We report the case of a 35-year-old Iranian (Middle-Eastern) male patient with a shotgun injury to both buttocks, which traveled to the heart and the popliteal area through the femoral vein and superficial femoral artery, respectively. Surgical intervention was applied for the popliteal pellet, and the patient was discharged without further complications. Conclusion Although bullet emboli can be a clinical challenge, with the advent of modern procedures, removal has become safer. X-ray, computed tomography, and transthoracic and/or transesophageal echocardiography may be used as adjuncts to help establish the diagnosis

The combination of arsenic, interferon-alpha, and zidovudine restores an “immunocompetent-like” cytokine expression profile in patients with adult T-cell leukemia lymphoma

BACKGROUND: HTLV-I associated adult T-cell leukemia/lymphoma (ATL) carries a dismal prognosis due to chemo-resistance and immuno-compromised micro-environment. The combination of zidovudine and interferon-alpha (IFN) significantly improved survival in ATL. Promising results were reported by adding arsenic trioxide to zidovudine and IFN. RESULTS: Here we assessed Th1/Th2/T(reg) cytokine gene expression profiles in 16 ATL patients before and 30 days after treatment with arsenic/IFN/zidovudine, in comparison with HTLV-I healthy carriers and sero-negative blood donors. ATL patients at diagnosis displayed a T(reg)/Th2 cytokine profile with significantly elevated transcript levels of Foxp3, interleukin-10 (IL-10), and IL-4 and had a reduced Th1 profile evidenced by decreased transcript levels of interferon-γ (IFN-γ) and IL-2. Most patients (15/16) responded, with CD4(+)CD25(+) cells significantly decreasing after therapy, paralleled by decreases in Foxp3 transcript. Importantly, arsenic/IFN/zidovudine therapy sharply diminished IL-10 transcript and serum levels concomittant with decrease in IL-4 and increases in IFN-γ and IL-2 mRNA, whether or not values were adjusted to the percentage of CD4(+)CD25(+) cells. Finally, IL-10 transcript level negatively correlated with clinical response at Day 30. CONCLUSIONS: The observed shift from a T(reg)/Th2 phenotype before treatment toward a Th1 phenotype after treatment with arsenic/IFN/zidovudine may play an important role in restoring an immuno-competent micro-environment, which enhances the eradication of ATL cells and the prevention of opportunistic infections