The present work describes the early steps in creating a digital twin to predict aging for multi-material adhesive step lap joints (ASLJs), considering simultaneous mechanical and environmental influences. It begins b...
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The present work describes the early steps in creating a digital twin to predict aging for multi-material adhesive step lap joints (ASLJs), considering simultaneous mechanical and environmental influences. It begins by defining the digital twin context and its specific architecture known as the data-driven digital twin (D3T). For a D3T to work, a theoretical and computational framework must be established to understand how the properties of materials in ASLJs degrade due to environmental damage. The developed framework describes a thermodynamics-based theory for predicting material degradation. The computational implementation of the framework and its performance are evaluated using two models of multi-material ASLJs. The first model contains a single-step ASLJ made of titanium Ti-6Al-4V and carbon epoxy composite with FM-300K adhesive, featuring three variations to show different levels of homogenization. Studies on this model assess the impact of various parameters such as the finite element order, mesh density, damage parameters, and inclusion of damage models for the participating domains. The validation of this model is also provided against experimental data. The second model addresses a multistep ASLJ using the same materials. Predictions from this model are compared favorably with experimental results under two different environmental conditions to gain insights into the aging and performance degradation of the ASLJs. Finally, conclusions and plans close the present paper.
Metal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing. Despite continued advances, MAM processes still face huge uncertainty, resulting in variab...
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Metal additive manufacturing (MAM) offers a larger design space with greater manufacturability than traditional manufacturing. Despite continued advances, MAM processes still face huge uncertainty, resulting in variable part quality. Real-time sensing for MAM processing helps quantify uncertainty by detecting build failure and process anomalies. While the high volume of multidimensional sensor data-such as melt-pool geometries and temperature gradients-is beginning to be explored, sensor selection does not yet effectively link sensor data to part quality. To begin investigating such connections, we propose network-based models that capture in real-time (1) sensor data's association with process variables and (2) as-built part qualities' association with related physical phenomena. These sensor models and networks lay the foundation for a comprehensive framework to monitor and manage the quality of MAM process outcomes.
Robotic additive manufacturing (RAM) offers significant improvements in maximum build volume compared to conventional bounded designs (e.g., gantry) by leveraging high degrees-of-freedom machines and multi-robot coope...
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Robotic additive manufacturing (RAM) offers significant improvements in maximum build volume compared to conventional bounded designs (e.g., gantry) by leveraging high degrees-of-freedom machines and multi-robot cooperation. However, cooperative RAM suffers from the same defect generation challenges as conventional systems, necessitating improvements in the detection and prevention of flaws within fabricated components. Quality assurance can be further bolstered through the integration of AM models, which utilize sensor feedback to localize defects, vastly reducing false positives. This research explores defect localization through a novel dynamic defect model created from simulated sensing data. In particular, two cooperative robots are simulated to estimate defect parameters, while observing the workspace and accurately classifying different regions of the part, generating a Gaussian mixture map that identifies and assigns appropriate actions based on defect types and characteristics. The experimental result shows that the implementation of the dynamic defect model and selective reevaluation achieved an effective defect detection accuracy of 99.9%, an improvement of 9.9% without localization. The proposed framework holds potential for application in domains that utilize high degrees-of-freedom machines and collaborative agents, offering scalability, improved fabrication speeds, and enhanced mechanical properties.
Metal powder bed fusion additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build...
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Metal powder bed fusion additive manufacturing (AM) processes have gained widespread adoption for the ability to produce complex geometries with high performance. However, a multitude of factors still affect the build process, which significantly impacts the adoption rate. This, in turn, leads to great challenges in achieving consistent and reliable part quality. To address this challenge, simulations and measurements have been progressively deployed to provide valuable insights into the quality of individual builds. This paper proposes an AM data fusion framework that combines data sources beyond a single-part, development cycle. Those sources include the aggregation of measurements from multiple builds and the outputs from their related models and simulations. Both can be used to support decision-makings that can improve part quality. The effectiveness of the holistic AM data fusion framework is illustrated through three use case scenarios: one that fuses process data from a single build, one that fusses data from a build and simulation, and one that fuses data from multiple builds. The case studies demonstrate that a data fusion framework can be applied to effectively detect over-melting scan strategies, monitor material melting conditions, and predict down-skin surface defects. Overall, the proposed method provides a practical solution for enhancing part quality management when individual data sources or models have intrinsic limitations.
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