To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

Metal additive manufacturing (MAM) provides a larger design space with accompanying manufacturability than traditional manufacturing. Recently, much research has focused on simulating the MAM process with regards to part geometry, porosity, and microstructure properties. Despite continued advances, MAM processes have many variables that are not well understood with respect to their effect on the part quality. With the common use of in-situ sensors — such as CMOS cameras and infrared cameras — numerous, real-time datasets can be captured and analyzed for monitoring both the process and the part. However, currently, real-time data predominantly focuses on the build failure and process anomalies by capturing the printing defects (cracks/peel-off). A large amount of data — such as melt pool geometries and temperature gradients — are just beginning to be explored, along with their connections to final part quality. Towards investigating these connections, in this paper we propose models that capture numerous sensor capabilities and associate them with the corresponding, real-time, physical phenomena. These sensor models lay the foundation for a comprehensive, knowledge framework that forms the basis for quality monitoring and management of MAM process outcomes. Using our previously developed process ontology model [1–3], which describes the relationship between process variables and process outcomes, we can discover the relationship between the real-time, physical phenomena and the deviations in the targeted, build quality. For example, statistically significant sensor data that predicts deviations from targeted process qualities can be detected and used to control the process parameters. Case studies that scope the physical phenomena and sensor data are provided for verifying the effectiveness and efficiency of the proposed qualification and certification models.

S-shaped intakes are widely used in aero-engines of modern fighters because of the demand for reducing radar cross-section. Besides, boundary layer ingestion (BLI) configurations are proposed in civil engines recently due to the high propulsion efficiency and low fuel consumption. And S-shaped ducts are usually used as transition sections of diffusers in BLI intakes. Compared with normal straight intakes, it is inevitable to bring in the influence of inlet distortion and acoustic reflection for S-shaped intakes. Meanwhile, composite fan blades, shorter intakes and integrated blisks are common in engine designs. So, fan blades are prone to serious vibrations such as flutter and forced response, which may lead to high-cycle fatigue, and further cause structural failure. The aeromechanical characteristics of a transonic fan (NASA rotor67) in presence of a s-shaped intake are predicted by an in-house integrated time-domain aeroelasticity code. The three dimensional, time-accurate, unsteady Reynolds-Averaged Navier-Stokes equations are solved in fluid domain, and the structural dynamic equations of blade vibration are solved with a modal superimposition method. Mode shapes and natural frequencies of rotor blade are obtained with a commercial Finite Element code, and the Campbell diagram is presented. Full-annulus aeroelastic calculations are conducted to obtain the transient response and the aerodynamic damping of fan blades. Different techniques for interface between the intake and the rotor are used for comparison to demonstrate the influence of upstream interaction. A mixing-plane model is used at the interface to model the blade vibration without interactions with the distortion, while a sliding-plane model is used at the same condition to include the flow distortion and acoustic effects on the fan blade motion. S-shaped intakes with two different axial length are investigated for the forced response and flutter stability. This study indicates that the forced response level is attenuated due to the decrease of distortion level as the length increases, while the flutter stability is determined by the phase difference between the upstream and the reflective acoustic wave.

The numerical study is performed by means of an in-house CFD code to investigate the effect of circumferential nonuniform tip clearance due to the casing ovalization on flow field and performance of a turbine stage. A method called fast-moving mesh is used to synchronize the non-circular computational domain with the rotation of the rotor row. Four different layouts of the circumferential nonuniform clearance are calculated and evaluated in this paper. The results show that, the circumferential nonuniform clearance could reduce the aerodynamic performance of the turbine. When the circumferential nonuniformity δ reaches 0.4, the aerodynamic efficiency decreases by 0.58 percentage points. Through the analysis of the flow field, it is found that the casing ovalization leads to the difference of the size of the tip clearance in the circumferential direction, and the aerodynamic loss of the position of large tip clearance is greater than that of small tip clearance, which is related to the scale of leakage vortex. In addition, the flow field will become nonuniform in the circumferential direction, especially at the rotor exit, which will adversely affect the downstream flow field.

The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall ( l ) to its thickness ( t ) defined as the aspect ratio (length-to-thickness ratio, l/t ), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. 2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.

Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in-situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Few, if any, previous works investigated how to tackle the impact of AM customization on image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel real-time and multi-scale process monitoring methodology for quality control of customized AM builds. Specifically, we leverage the computer-aided design (CAD) file to perform shape-to-image registration and delineate the regions of interests in lay-erwise images. Next, a hierarchical dyadic partitioning methodology is developed to split layer-to-layer regions of interest into subregions with the same number of pixels to provide freeform geometry analysis. Then, we propose a semiparametric model to characterize the complex spatial patterns in each customized subregion and boost the computational speed. Finally, a DNN model is designed to learn and detect fine-grained information of flaws. Experimental results show that the proposed process monitoring and control methodology detects flaws in each layer with an accuracy of 92.50±1.03%. This provides an opportunity to reduce inter-layer variation in AM prior to completion of the build. The proposed methodology can also be generally applicable in a variety of engineering and medical domains that entail image-based process monitoring and control with customized designs.

A conventional parallel manipulator consists of a moving platform, a fixed base platform, as well as several serial chains that connect the two platforms. This paper presents a novel five degree-of-freedom (DOF) hybrid manipulator such that its base platform itself is movable and thus reconfigurable. This hybrid structure greatly expands the workspace of the end-effector platform so that it can reach and perform tasks over a large-scale spherical honeycomb structure. First, the inverse kinematics as well as the Jacobian matrix is developed. Then, the workspace and singularity of the hybrid manipulator are studied for four different positions of the reconfigurable base platform to show the enlarged singularity-free workspace. For a large-scale honeycomb structure with prefusion requirements, which is placed within the workspace of the hybrid manipulator, the minimum and maximum limit positions of the reconfigurable base are obtained. Finally, a simulation model for the hybrid manipulator is developed using Mathematica and Adams and numerical simulations are conducted to evaluate the kinematic performance and verify the effectiveness of the hybrid manipulator.

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

The goal of this work is to understand the effect of process conditions on part porosity in laser powder bed fusion (LPBF) Additive Manufacturing (AM) process, and subsequently, detect the onset of process conditions that lead to porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are two-fold: (1) Quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P). (2) Monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. This is important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to sensor signatures and defects is the first-step towards in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti-6Al-4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these parameters on count, size and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built was identified with the statistical fidelity over 80% (F-score).

The configuration of MOSES TLP is quite different from conventional types. A compact central columns group and extended tendon support structure provides better deck support and hydrodynamic efficiency. A fully-coupled time domain analysis was adopted to evaluate effects of first and second order wave forces, motions and tendon tensions in hurricane conditions. The nonlinear responses due to the environmental load and interaction between the hull and tendons that includes large surge-heave motion in the low frequency and resonant heave/pitch responses with the spring loads in the high frequency are focused on in the paper. Results from numerical simulation are compared with data in full scale measurement.

Design of a floating structure is supposed to be based on the extreme responses experienced by the components of the structure during its lifetime. The airgap response and potential deck impact of ocean structures under sea waves is of considerable interest. Non-linear diffraction models are usually called for a more consistent evaluation of the wave field under the deck and the wave run-up upon the columns, but even second-order analysis is not free of uncertainties. Therefore, air gap evaluation still relies heavily on experimental analysis. This paper presents some deep-tank results performed for the evaluation of the dynamic airgap of a large-volume semisubmersible platform. A series of model tests were carried out for the scale model of a horizontal moored semi in regular and extreme irregular wave conditions. Airgap response combined with run-up close to columns at total 11 locations on the deck was evaluated under oblique wave status. Motions and elevation data are analyzed by statistical treatment. Weibull-tail fitting procedure is realized to determine the extreme response levels.

This paper reports a numerical method for the design of a unrunning blade with the consideration of both nonlinear aerodynamic and centrifugal forces. Accurate prediction of blade manufacture shape in turbomachinery is crucial for performance, efficiency and aeroelastic stability. An iterative procedure starting from a given blade running shape is developed to predict the manufacture blade shape. The model is based on a three-dimensional (3D) unsteady nonlinear Navier-Stokes Computational Fluid Dynamics (CFD) solver and the mode superposition structural dynamic theory in conjunction with a finite element structural model for the rotor blade. The manufacture profile of the blade (“Cold” blade) is estimated from the running blade shape (“Hot” blade). ANSYS finite element code is used to compute the deflection of the cold blade due to centrifugal loads. A finite volume based 3D nonlinear CFD code, coupled with a mode superposition structural dynamic modal method, is employed to determine the blade deflection due to unsteady aerodynamic loading. The difference between the computed blade profile and the targeted hot blade shape is used to predict a new cold blade for the next iteration if the convergence criterion is not met. The method is applied to predict the manufacture blade shape of a large-scale propfan and a NASA rotor 67 fan. The predicted blade profile and the twist angle of the blade at various spans are presented. The results show that improvements of the manufacture blade profile can be made by including proper nonlinear aerodynamic effect on the blade deflection in the numerical model. The results also illustrate that aerodynamic nonlinear effects on structural deformation should be included for a better cold blade design.

This study focuses on developing a new method to remove uranium from aqueous solution. Chitosan and ferrous ions were used together to remove uranium ions from aqueous solution. Through two-step pH adjustment, the uptake behavior of chitosan and ferrous ions toward uranium in aqueous solution using batch systems were studied in different experimental conditions. The experimental results indicated that the removal of uranium by synergetic effect of chitosan and ferrous ions was more effective than the way of adsorbing uranium ions by chitosan alone. Under the given experimental conditions, the concentration of the residual uranium in the effluent after chitosan and ferrous ions treatment could meet the discharge standard (< 0.05mg·l −1 ) when initial concentration of uranium ions was 10 mg·l −1 or 100 mg·l −1 . The synergetic effect of chitosan and ferrous ions including adsorption, coacervation and coprecipitation, are responsible for the high removal rate of uranium.