Welcome to ResearchWorks at the University of Washington

First time adding to ResearchWorks? Please contact us.

ResearchWorks is the University of Washington's digital repository (also known as "institutional repository") for disseminating scholarly work.

Looking to deposit a dataset? UW has a membership with the generalist data repository Dryad. Use our guide to see if Dryad is the right place for you to deposit your data.

About ResearchWorks UW Bothell UW Tacoma UW Press

Communities in ResearchWorks

Select a community to browse its collections.

Now showing 1 - 7 of 7

Recently Added

Accelerated Pyrolysis Analysis of Thick High-Temperature Composite Parts Using Theory-Guided Probabilistic Machine Learning and Finite Element Analysis
(9/18/2023) Portales Picazo, Paulina; Ong, Derrick; Gray, Alexander; Zobeiry, Navid
Fabrication of advanced composites for high-temperature applications typically involves a complex, multi-step process. This process includes an initial lay-up step, curing, high-temperature pyrolysis to transform the cured resin into a carbonized amorphous structure, several resin-backfilling steps to fill voids and cracks formed during pyrolysis, additional pyrolysis-densification cycles to further increase the carbon content, and a final graphitization step to achieve the desired crystalline structure of carbon atoms. The processing parameters in each step, as well as the lay-up and overall thickness of the part, directly impact the kinetics of reactions, phase transformations, and the resulting end-part properties. During pyrolysis, the resin undergoes a complex system of degradation reactions affected by heat and mass transfer through the part thickness. To achieve optimal performance of high-temperature composites, it is crucial to accurately establish the relationship between processing conditions, part geometry, and end-part properties. Current process optimization relies heavily on time-consuming and resource-intensive testing campaigns to characterize pyrolysis kinetics, followed by trial-and-error efforts. This is further complicated by complex temperature cycles, gradients of pyrolysis rate across thick composite parts, uncertainties, and variabilities of the material and process. This paper introduces a novel framework combining theory-guided probabilistic machine learning (ML) and finite element (FE) process simulation to address these challenges. Limited targeted experiments are used to characterize pyrolysis kinetics using an in-house developed ML code while quantifying uncertainty at each testing step. The kinetics model is then used by an FE model to simulate heat transfer and rate of pyrolysis, allowing the gradient of degree of pyrolysis across the thickness of a laminate to be simulated. For validation, laminates of varying thicknesses were fabricated and pyrolyzed using different temperature cycles. Imaging techniques including light microscopy and scanning electron microscopy (SEM) were utilized to capture the resulting microstructures. An image analysis code was developed to detect cracks and voids to identify potential gradients and patterns. This, along with Raman spectroscopy, was used to estimate the degree of mass loss across the thickness of different parts and compared with predictions of the combined ML-FE framework. This framework significantly reduces trial-and-error experimental efforts and enables pyrolysis process optimization of thick composites to achieve desired properties, such as yield, porosity, and laminate permeability.
A Theory-Guided Machine Learning Method for Cure Cycle Optimization to Minimize Process-Induced Deformations in Composites
(9/18/2023) Schoenholz, Caleb; Zobeiry, Navid
One of the most prevalent challenges faced in aerospace manufacturing is the accurate prediction, control, and mitigation of residual stresses and resulting process-induced deformations (PIDs) in composites. Unwanted dimensional changes in composite parts can cause significant geometry mismatches during aerostructure assembly, leading to decreased throughput and a loss of structural performance. In recent decades, process simulation tools have been developed to facilitate predictions and mitigations of PIDs through process optimization as well as methods such as tool compensation. However, such numerical approaches often rely on time-consuming, expensive, and deterministic characterization and model-fitting techniques. As a result, simulation tools may be error-prone in industrial settings, making PID predictions unreliable. Therefore, manufacturers rely on labor-intensive methods, such as shimming, to compensate for geometry mismatches during the assembly process. This paper presents an alternative methodology to predict and minimize PIDs in composites without using process simulation. The proposed method is solely based on a limited amount of element-level experimental tests and theory-guided machine learning (TGML). Experimental methods include autoclave-curing of L-shaped laminates with different cure cycles and quantifying the resulting PIDs using laser profilometry. Probabilistic ML models are then built to correlate temperature cycles directly to PIDs using Gaussian Process Regression (GPR) guided by the closed-form theory available for PID predictions. Finally, the theory-guided ML models are iteratively calibrated using additional targeted tests specified by the GPR algorithm to efficiently converge on an optimal cycle that minimizes PIDs and satisfies cost specifications. The proposed ML-based approach is a generalizable probabilistic method to optimize process parameters and mitigate PIDs without using expensive process simulations or conducting exhaustive characterization experiments.
A Fast Method for Evaluating Effects of Process Parameters on Morphology of Semi-crystalline Thermoplastic Composites
(9/20/2021) Wynn, Mathew; Zobeiry, Navid
Semi-crystalline thermoplastics such as PEEK have microstructures that are influenced by process parameters like temperature cycle, humidity, and oxygen levels. Inclusions such as carbon fibers lead to heterogeneous crystal nucleation. Further, manufacturing uncertainties involved with techniques such as automated fiber placement, compression molding, or induction welding influence the microstructure of thermoplastic composites. These contributing factors impact type (e.g., spherulitic, cross-linked, transcrystalline, and needle-like), size and distribution of morphologies in the material. Even with similar degrees of crystallinities, these differences affect mechanical properties and overall performance of composite parts. In this study, an experimental method has been developed that allows for fast evaluation of morphology as a function of process parameters in semi-crystalline thermoplastic composites. A compression fixture in a Dynamic Mechanical Analyzer (DMA) is used to process thin films of thermoplastics with embedded carbon fibers, sandwiched between thin glass covers, while carefully controlling processing conditions including temperature, pressure, and strain rate. The sample morphology is then analyzed using through transmission Polarizing Light Microscopy (PLM). Samples can be reprocessed using DMA several times to analyze changes in microstructure. This experimental approach allows for fast exploration of time-temperature-transformation relationships and their effects on morphology. This can be used to enhance our understanding of the material microstructure and develop more accurate process simulation tools, leading to optimization of processing parameters.
Accelerating Composite Cure Cycle Optimization with Combined Probabilistic Machine Learning and Finite Element Process Simulation
(9/18/2023) Fu, Huilong; Schoenholz, Caleb; Portales Picazo, Paulina; Eskandariyun, Amirali; Zobeiry, Navid
Advanced composites are widely used in industries such as aerospace. To certify aerospace structures, the manufacturing process of composites is tightly controlled to ensure compliance with process specifications. For example, in autoclave curing of thermoset composites, process specifications often limit the maximum temperature in the composite part due to exothermic reaction, maximum thermal lag between the part and autoclave temperature cycle, maximum part porosity, and minimum degree of cure. Given all the requirements in process specifications, optimizing the temperature cycle in autoclave curing to reduce cycle time and increase production throughput is often challenging. While finite-element process simulation is used for coupled analysis of heat transfer and thermo-chemical curing reactions during the curing process, optimization across the entire design spectrum is often infeasible because of the long simulation time of high-fidelity models and commonly relies on trial and error and engineering insight. In this study, we present a novel framework based on combined probabilistic machine learning and finite-element simulation to accelerate composite-process optimization. The probabilistic machine-learning model is developed using the underlying theory of curing and the mathematical foundation of Gaussian Process Regression. Instead of trial and error or a time-consuming grid search, this approach relies on targeted finite-element evaluations of selected cure cycles by the machine-learning model, such that an optimized solution is obtained through a step-by-step simulation approach. While minimizing the effort required to optimize cure-processing conditions, the approach also enables evaluation of nontraditional cycles, such as multiple ramps and holds, to minimize autoclave cycle time. For validation, the framework is applied to optimize the cure cycle of a thick HEXCEL AS4/8552 composite laminate cured on a typical Invar tool. A validated finite-element model is used for process simulation along with the machine-learning model for targeted evaluation. Results show that an optimized solution satisfying process specifications while minimizing cycle time can be obtained with only a few analysis steps.
Investigating the Effects of Cure Pressure on Tool-Part Interaction and Process-induced Deformations in Composites
(9/19/2022) Schoenholz, Caleb; Moomaw, Joann; Zobeiry, Navid
The interaction between a tool and part during composites manufacturing may significantly contribute to the formation of residual stresses and process-induced deformations (PIDs). High levels of uncertainty are often associated with tool-part interaction and resulting PIDs due to the complex underlying physics, and variabilities and uncertainties in processing. During the assembly of aerospace composite structures, PIDs may create significant geometry mismatches and lead to a loss of mechanical performance. Aerospace manufacturers commonly use costly and time-consuming techniques such as shimming to compensate for PIDs and meet process specification requirements. Although process simulation tools have evolved significantly in recent years to enable mitigation of PIDs via tool geometry compensation, in practice, they are not implemented often due to challenges associated with the characterization and calibration of numerical models. One such challenge is accurately measuring the degree of tool-part interaction and interfacial stresses during processing cycles. This paper presents a custom-built test fixture to directly measure tool-part interfacial stress development during processing of composites. The experimental setup is installed in a Dynamic Mechanical Analyzer (DMA) for in-situ measurement of tool-part interfacial stresses. The technique allows for the evaluation of the effects of process parameters, including pressure, temperature, lay-up, and tool surface condition. In this study, tool-part stress development was characterized as a function of the applied pressure for Toray T800S/3900-2 laminates cured on steel tools treated with Frekote release agent. The characterized stresses were then validated by measuring warpages of long and symmetric laminates cured on flat steel tools using different pressures similar to DMA tests. Results demonstrated that cure pressure significantly impacts the multi-physics interactions between fibers, resin, interply tougheners, and the tool surface throughout a cure cycle. The results of this paper can be used to expand the current understanding of tool-part interaction and potentially mitigate PIDs in composites processing.
Characterizing Thermal Degradation in Semi-Crystalline Thermoplastic Composites
(9/18/2023) Wynn, Mathew; Chen, Kuan-Ting; Zobeiry, Navid
Semi-crystalline thermoplastic composites, such as carbon fiber-reinforced polyetheretherketone (PEEK) and polyetherketoneketone (PEKK), are processed and consolidated while melted at high temperatures. During cool-down, polymer chains fold into lamellar structures at the nanoscale to form crystalline morphology. These lamellar structures radiate from a nucleus, creating spherulitic structures in bulk polymers and transcrystallinity in fiber-reinforced polymers. A certain amount of thermal degradation occurs when the thermoplastic matrix is melted, and the amount of degradation is a function of several parameters, such as melting temperature, time at melt, and whether the material is processed in an inert environment such as nitrogen. One form of degradation that occurs in the matrix is cross-linking and oxidation. In this case, the polymer chain breaks and new bonds form between chains or within the chain. Moreover, thermal degradation affects crystallization, lamellar thickness and spacing, and the overall degree of crystallinity. The spacing between lamellar structures can be measured through Small Angle X-ray Scattering (SAXS) at the nanoscale, while the degree of crystallinity can be found using Wide Angle X-ray Scattering (WAXS). To study thermal degradation, the effects of melting temperature, environmental condition, and reprocessing were investigated using samples of neat PEEK and carbon-fiber PEKK prepreg. These samples were thermally cycled multiple times, with repeats performed for each condition. Degree of crystallinity, spacing, and lamellar thickness were measured using an X-ray scattering system. To study the underlying physics and correlations, a probabilistic machine-learning framework was used for regression. Using this approach, different thermal-degradation mechanisms for neat resin and prepreg samples were demonstrated at the nanoscale. The differences were explained in terms of crystallinity and nucleation around fibers in prepreg. This framework provides a holistic understanding of crystal formation and degradation, which ultimately affects the reprocessability and end-part properties of semi-crystalline thermoplastic composites.
Exploring Underlying Correlations in Multi-Fidelity Finite Element Simulations of Open-Hole Tension Tests: A Comparative Study Using Machine Learning
(9/18/2023) Eskandariyun, Amirali; Fu, Huilong; Reiner, Johannes; Vaziri, Reza; Zobeiry, Navid
Certification of composite aerostructures is typically achieved via analysis supported by testing, following the building block approach. Analysis of failure and crash at different scales, as well as design iterations and optimization, require fast and validated numerical models. Given the complexity and interactions of multi-failure mechanisms in composites, high-fidelity FE models are often needed to explicitly simulate the behavior of individual layers and their interlaminar interfaces, using a mix of continuum and discrete damage models. However, high-fidelity FE models require a considerable amount of time to set up, calibrate, and perform. On the other hand, low-fidelity modeling approaches, such as laminate-based smeared models, may provide reduced accuracy in some cases, while offering shorter simulation times and thereby lower computational costs. In this study, progressive failure of Open-Hole Tension (OHT) tests on HEXCEL IM-7/8552 quasi-isotropic laminates are investigated using both low- and high-fidelity FE models. A Machine Learning (ML) technique is then employed to perform a comparative study between the inputs and outputs of these models. The ultimate goal is to identify areas in the FE parameter space where the low-fidelity model can be used as a substitute for the high-fidelity model with reasonable accuracy and to discover the relationships among main parameters to reproduce high-fidelity results from the low-fidelity model in an accelerated manner. To generate the low-fidelity model, a laminate-based FE model was created in LS-DYNA, using a continuum elasto-plastic damage-based material card, MAT081. The model parameters were varied randomly, and simulations were performed to generate a large database of low-fidelity FE results. Similarly, a ply-based FE model employing MAT261 for continuum intralaminar damage modeling combined with cohesive tiebreak contacts to capture discrete interlaminar delamination was created. This high-fidelity model was also used to generate another database of simulation results. Both databases were used to create a training set for an ML model, aiming to study the correlation between low- and high-fidelity inputs that result in similar failure responses. The results enable increasing use of low-fidelity FE models to generate data for the training of ML models.
Representation, Characterization and Simulation of Tool-Part Interaction and its Effects on Process-induced Deformations in Composites
(9/20/2021) Schoenholz, Caleb; Slade, Daniel; Zappino, Enrico; Petrolo, Marco; Zobeiry, Navid
The interaction between a tool and part during composites processing contributes to the formation of residual stresses and dimensional changes. A resultant mismatch of part geometries during assembly can cause a potential loss of mechanical performance in aerospace structures. Costly shimming steps are needed to compensate for process-induced deformations and satisfy specifications on mechanical performance. Due to difficulties associated with accurate measurement of interfacial shear stresses, current analysis methods fail to represent the interaction between a tool and part throughout processing. A combined approach to represent, characterize, and simulate tool-part interaction and its effects on dimensional changes is proposed. First, a characterization method was established using a custom Dynamic Mechanical Analysis (DMA) shear test setup to measure tool-part interfacial stress development in a simulated autoclave curing environment. Tool-part interfacial stresses were characterized for Toray T800S/3900-2 UD prepreg as a function of temperature, degree of cure, strain rate, and tool surface condition. Then, a previously developed numerical model was modified to include the effects of tool-part interaction in predicting dimensional changes of L-shape parts. For validation, composite parts were fabricated on tools with different surface conditions and successfully compared to simulation results. This paper demonstrates that tool-part interaction significantly impacts the spring-in of angled composite parts. The proposed method is a comprehensive and practical approach to study and simulate the effects of tool-part interaction. The results of this paper can be used to understand the complex interaction between a tool and part throughout processing and potentially mitigate process-induced deformations.
Detecting hidden transient events in noisy nonlinear time-series
(Chaos, 2022-07-28) A. Montoya; E. Habtour; F. Moreu
The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.
Hybrid Compliant Musculoskeletal System for Fast Actuation in Robots
(Micromachines, 2022-10-20) PieterWiersinga; Aidan Sleavin; Bart Boom; Thijs Masmeijer; Spencer Flint; Ed Habtour
A nature-inspired musculoskeletal system is designed and developed to examine the principle of nonlinear elastic energy storage–release for robotic applications. The musculoskeletal system architecture consists of elastically rigid segments and hyperelastic soft materials to emulate rigid–soft interactions in limbless vertebrates. The objectives are to (i) improve the energy efficiency of actuation beyond that of current pure soft actuators while (ii) producing a high range of motion similar to that of soft robots but with structural stability. This paper proposes a musculoskeletal design that takes advantage of structural segmentation to increase the system’s degrees of freedom, which enhances the range of motion. Our findings show that rigid–soft interactions provide a remarkable increase in energy storage and release and, thus, an increase in the undulation speed. The energy efficiency achieved is approximately 68% for bending the musculoskeletal system from the straight configuration, compared to 2.5–30% efficiency in purely soft actuators. The hybrid compliance of the musculoskeletal system under investigation shows promise for alleviating the need for actuators at each joint in a robot.