Uncertainty Quantification in Predicting Behaviour of Rubber-Like Materials in Uni-Axial Loading

Material parameters related to deterministic models can have different values due to variation of experiments outcome. From a mathematical point of view, probabilistic modeling can improve this problem. It means that material parameters of constitutive models can be characterized as random variables with a probability distribution. To this end, we propose a constitutive models of rubber-like materials based on uncertainty quantification (UQ) approach. UQ reduces uncertainties in both computational and real-world applications. Constitutive models in elastomers play a crucial role in both science and industry due to their unique hyper-elastic behavior under different loading conditions (uni-axial extension, biaxial, or pure shear). Here our goal is to model the uncertainty in constitutive models of elastomers, and accordingly, identify sensitive parameters that we highly contribute to model uncertainty and error. Modern UQ models can be implemented to use the physics of the problem compared to black-box machine learning approaches that uses data only. In this research, we propagate uncertainty through the model, characterize sensitivity of material behavior to show the importance of each parameter for uncertainty reduction. To this end, we utilized Bayesian rules to develop a model considering uncertainty in the mechanical response of elastomers. As an important assumption, we believe that our measurements are around the model prediction, but it is contaminated by Gaussian noise. We can make the noise by maximizing the posterior. The uni-axial extension experimental data set is used to calibrate the model and propagate uncertainty in this research.

Impact Analysis of Honeycomb Core Sandwich Panels

The goal of this paper is to analyze the impact behavior among geometrically different sandwich panels shown upon impact velocities. Initially, composite model with aluminum honeycomb core and Kevlar (K29) face sheets is developed in ABAQUS/Explicit and different impact velocities are applied. Keeping other parameters constant, model is simulated with T800S/epoxy face sheets. Residual velocities, energy absorption (%), and maximum deformation depth is calculated for sandwich panel for both models at five different velocities by executing finite element analysis. Once the better material is found for face sheets, process is extended by varying the ratio of front face sheet thickness to back face sheet thickness keeping other geometrical parameters constant to find the better geometry. Also, comparison of impact responses of sandwich composite panel on different ratio of front face sheet thickness to back face sheet thickness is done and validated with other results available in literature.

A Hybrid Supervised Deep Learning and Nonlinear Finite Element Framework for Efficient Fatigue Life Predictions of Rotary Shouldered Threaded Connections

In this paper, a hybrid computational framework that combines the state-of-the art machine learning algorithm (i.e., deep neural network) and nonlinear finite element analysis for efficient and accurate fatigue life prediction of rotary shouldered threaded connections is presented. Specifically, a large set of simulation data from nonlinear FEA, along with a small set of experimental data from full-scale fatigue tests, constitutes the dataset required for training and testing of a fast-loop predictive model that could cover most commonly used rotary shouldered connections. Feature engineering was first performed to explore the compressed feature space to be used to represent the data. An ensemble deep learning algorithm was then developed to learn the underlying pattern, and hyperparameter tuning techniques were employed to select the learning model that provides the best mapping, between the features and the fatigue strength of the connections. The resulting fatigue life predictions were found to agree favorably well with the experimental results from full-scale bending fatigue tests and field operational data. This newly developed hybrid modeling framework paves a new way to realtime predicting the remaining useful life of rotary shouldered threaded connections for prognostic health management of the drilling equipment.

Failure Analysis of Coil Racks in a Batch Annealing Furnace Using Finite Element Methods

In this work, a series of finite element method simulations were performed on cooling racks used for the loading and unloading of steel coils in a batch annealing furnace. Normal furnace operations were simulated using ANSYS Thermal FEA and ANSYS Mechanical FEA to reproduce the thermal and structural loads experienced by the racks, as well as additional lifting forces representing removal by forklift after annealing. In addition to warping found due to uneven heating from the radiative elements of the furnace, severe stress leading to failure was identified on the base of the racks from the lifting force. These results correspond with failures seen in the actual units. The simulation data was used for design adjustments to reduce the damage from the above sources.

Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

Manufacturing Error Detection in Plate and Cylindrical Composite Structures

Due to their superior weight to strength ratio of composites to common metallic structures, composite technology is widely used in aerospace industry. Assessment of damage in composites has gained interest after a large number of accidents caused by unanticipated damages in the composite structures. Many different structural health monitoring applications were developed over the years due to the fact that composite materials may inherit damage from within, not always visible from surface. The most common types of errors encountered in the industry are due to misaligned fibers, a mix-up in ply order, and delaminations: all presenting changes in the vibro-acoustical performance of the composite structure. This paper discusses the change in the dynamic properties of a composite structure contains a manufacturing error such as a ply lay-up error, and a ply angle error. Both plate and cylindrical structure types were considered for the stated error types. Effect of symmetric errors, unsymmetrical and unbalanced errors, and mid-plane errors were considered in the case of ply orientations, and dynamic stiffness matrix was used to identify the error. Identification of the structure’s layup properties and manufacturing error identification is employed. From the measured modal properties of the structure, a back-tracking strategy was used to generate the ply lay-up of the composite structure. Prepreg plates of a single carbon fiber system and filament wound hybrid cylinders consisting of glass and carbon fibers were manufactured for testing. Modal tests on plates and cylindrical composite structures were performed and compared with the analysis. A good match between the finite element model and experiment was shown in natural frequencies and mode shapes.

Composite Core Cross Tube Crushing Analysis

Thin walled axial members are typically used in automobiles’ side and front chassis to improve crashworthiness of vehicles. Extensive work has been done in exploring energy absorbing characteristics of thin walled structural members under axial compressive loading. The present study is a continuation of the work presented earlier on evaluating the effects of inclusion of functionally graded cellular structures in thin walled members under axial compressive loading. A compact functionally graded composite cellular core was introduced inside a cross tube with side length and wall thickness of 25.4 mm and 3.048 mm, respectively. The parameters governing the energy absorbing characteristics such as deformation or collapsing modes, crushing/ reactive force, plateau stress level, and energy curves, were evaluated. The results showed that the inclusion of composite graded cellular structure increased the energy absorption capacity of the cross tube significantly. The composite graded structure underwent progressive stepwise, layer by layer, crushing mode and provided lateral stability to the cross tube thus delaying local tube wall collapse and promoting large localized folds on the tube’s periphery as compared to highly localized and compact deformation modes that were observed in the empty cross tube under axial compressive loading. The variation in deformation mode resulted in enhanced stiffness of the composite structure, and therefore, high energy absorption by the structure. This aspect has a potential to be exploited to improve the crashworthiness of automobile structures.

A Sequential Two-Step Design Framework for Deformable Mechanical Metamaterials

Deformable metamaterials are materials that are made up of several repeating elastic building blocks whose geometries can be tailored to obtain a specified global shape change or stiffness behavior. They are deemed useful in soft robotics, shape morphing mechanisms, stretchable electronics, wearable devices, and devices that adapt according to their environment. This paper presents a two-step sequential design framework for the synthesis of deformable mechanical metamaterials where (a) topology optimization is used to map global deformation requirement to local elasticity matrix, followed by (b) a selection of building block microstructure geometry from a database and refining it to match the elasticity requirement. The first step is accomplished through a unique parameterization scheme that enables the classification of the planar orthotropic elasticity matrix into four distinct classes. The second step uses a kinetostatic framework known as load flow visualization to populate candidate microstructure geometries within these four classes. Finally, the framework is validated for the design of a cantilever beam with a specified lateral stiffness requirement and the design of planar sheets that exhibit sinusoidal deformation patterns.

Corrosion Evaluation of a Navy MK50 Weapon Station Friction Brake Assembly

The presence of corrosion on or within structures is of major concern as corrosion reduces the integrity of the materials which could potentially result in large-scale failures of structures and equipment.1 The United States Navy is an organization that actively works to prevent large equipment failure due to corrosion. One such problem is the corrosion of the friction brake assembly on the MK50 Weapon Station, which has recently been experiencing corrosion between the friction brake and its set screw preventing it from operating correctly. The friction brake was known to be stainless steel; however, the set screw was of unknown composition. Through elemental analysis it was determined that the MK50 Weapons Station friction brake set screw was similar in composition to commonly available black oxide coated steel screws. Electrochemical polarization measurements of the friction brake assembly components revealed that the set screw and the friction brake were electrochemically dissimilar metals which resulted in the galvanic corrosion of the assembly when out at sea. The electrochemical polarization measurements of a stainless steel screw showed a corrosion potential similar to that of the friction brake; therefore, replacing the current set screw with a stainless steel screw would decrease the galvanic potential difference between the set screw and the friction brake. This proposed solution is expected to slow or prevent further corrosion of the MK50 Weapon Station ensuring the combat readiness of the equipment.

Fatigue Properties of 3D Welded Thin Structures

Additive manufacturing technology has matured enough to produce real industrial components. A newer method of 3D printing is the deposition of molten metal beads using a MIG weld torch. This involves a 3D printer equipped with a MIG torch layering the metals in desired shapes. It allows the fabrication of components made of MIG weld wires, currently available from various elements including Cu, Al, steel and alloys. Some of these structures made by 3D welding will have applications in critical load bearing conditions. The reliability of such components will be vital in applications where human lives are at stake. Tensile tests are conducted to verify the required strength of the fabricated parts which will undergo monotonic loading; however, fatigue tests are required for cases where cyclic loading will take place. Conventional tensile and fatigue testing requires macro-scale samples. With MIG welding, it is possible to make thin-walled structures. Fatigue testing on samples extracted from thin walls is made possible by microtesting. This study is focused on the mechanical properties of 3D welded structures made from MIG welding wires. Our earlier results showed orientation dependence of mechanical properties in 3D welded structures. They also showed the effect of substrates in expression of the orientation dependence. Welding on metal substrate produces weld beads that are harder at the substrate interfacial area. However, for structures welded on ceramics, the opposite is true. They exhibit a softer substrate interfacial area and a relatively harder top. Our newer results show fatigue properties of structures made by 3D welding. Microsamples measuring 0.2 mm × 0.2 mm × 1.0 mm were extracted from metal beads using a CNC mill along with an EDM. The contours of the samples were machined by milling and the back side was cut by electro discharge machining. Specimens were then polished to the desired size and mounted in the grippers of an E1000 Instron load frame. WaveMatrix® application software from Instron was used to control the machine and to obtain testing data. Fatigue tests were performed, and life cycles were determined for various stress levels up to over 5 million cycles. The preliminary results of tensile tests of these samples show strength levels that are comparable to those of parent metal, in the range of 600–950MPa. Results of fatigue tests show high fatigue lives associated with relatively high stresses. The preliminary results will be presented and the implications of the use of 3D welded rebar in 3D printing of reinforced concrete structures will be discussed.