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Comparing Machine Learning Paired Optimization Strategies for Single-Component Waterborne Direct-To-Metal Coatings
Get PDFAs R&D attempts to fine tune properties before production, the redesign and testing process can become highly iterative. The repetitive nature of formulating can be difficult to escape when relying on chemical intuition alone. To provide some guidance in this process, machine learning (ML) can leverage data from previous trials1 to design suggestions for subsequent trials. Although ML capabilities have vastly expanded in the last few years, integration of ML into chemistry R&D and education has been slow. Unraveling the mystique of ML can help change what has been a slow embrace in the coatings industry. It is important to demonstrate the utility of ML in industrial coatings R&D, while also developing an intuitive curriculum to prepare the next generation of chemists. By demonstrating effectiveness and ease-of-use, this paper hopes to establish the need for ML in coatings R&D and undergraduate laboratory curricula.
In this paper, different ML strategies are compared for their ability to optimize and reformulate single-component waterborne direct-to-metal (DTM) coatings. This coating system is sensitive to ingredient selection and ingredient amounts, making invalid coating formulations easy to distinguish from valid candidates in testing. Three different ML strategies are compared, built on two neural network designs. A commercial electronic lab notebook software with native ML feature integration2, is compared to two in-house models built on open-source libraries in Python. Starting with a single DTM formulation, the design space was populated with formulations by varying ingredient ratios while maintaining the ingredient list and order of addition. The ML models have a consistent problem to solve, and improvements in the model performance can be assessed over iteration of model development and material formulation. All three ML strategies struggled to predict test scores accurately, however they did manage to generate formulations that narrowed the design space towards target property scores. The neural networks struggled with making accurate predictions on a limited dataset. Combining a better model with a strong optimization strategy could still streamline the coatings R&D process at a larger scale
Low Earth Orbit Drag Sail Degradation Due to Atomic Oxygen and Ultraviolet Radiation
Get PDFAs the orbital debris population increases, so does the risk of collisions with operational spacecraft. In response, the Federal Communications Commission has shortened the deorbit requirement from 25 years to five years. While natural atmospheric decay was sufficient under the previous guideline, many objects with low area to mass ratios can no longer passively comply. Smaller, non-propellant-carrying space objects such as CubeSats lack the ability to maneuver and must rely on alternative deorbit methods. One proposed solution is to deploy a drag sail at end of life to increase atmospheric drag and accelerate orbital decay. However, exposure to atomic oxygen (AO) in the low Earth orbit environment degrades the thin membrane of the sail, compromising its functionality. This study investigates the survivability of single-sided aluminized Mylar® exposed to AO, generated by a capacitively coupled plasma system, and vacuum ultraviolet radiation (VUV) from a deuterium lamp. Forty-nine AO tests were conducted: 16 with the aluminized side exposed and 33 with the uncoated side. Fluence was calculated using the mass loss of a Kapton® HN witness sample, and each Mylar® sample was evaluated for survival. Two tests exposed samples to both VUV and AO, but no additional deterioration as a result of VUV was observed at the tested exposure levels. Binary logistic regression was used to develop an inverse prediction model for Mylar® survival as a function of AO fluence. The aluminized model showed a better fit than the uncoated model. Based on this analysis, a drag sail deorbit simulation was developed that models degradation due to AO. Given the poor fit of the uncoated model, only the data from the aluminized side was used; consequently, the simulation assumes perfect pointing accuracy. The model simulates degradation in 10% increments from full sail area to complete loss and is validated against the orbital decay of NanoSail-D2. Results show that deorbit time is highly sensitive to both initial sail size and solar activity
Case Study: Cal Poly Housing Program (CPHP) - Student Housing Project at California Polytechnic University, San Luis Obispo
Get PDFFull volumetric modular construction is a building technique that involves the off-site prefabrication of three-dimensional modular units, which are later transported and assembled on-site. California Polytechnic State University in San Luis Obispo and general contractor, Whiting-Turner, are building new on-campus student housing by utilizing full volumetric modular construction techniques. The buildings will be produced in modules that, when put together, reflect the identical design and intent of a site-built building. This paper will analyze the effectiveness of modular construction on a large-scale project and look at the project cost, schedule, and waste impacts from choosing this approach. The main driver of the Cal Poly Housing Program (CPHP) was the schedule and getting students housed as fast as possible. The project encountered unique challenges during the initial construction phases, such as a high start-up cost for design and creating an assembly line for the product. However, there are also many benefits, such as reduced project schedule, material waste, and improved quality control. These results provide valuable lessons, such as the importance of adequate preparation, allocating ample time for design, an established assembly line, and coordination amongst the project team
Towards a Pedagogy of Unruly Bodyminds
Get PDFThis commentary underscores critical practices from Jane’s “Unruly Bodies” seminar that Ali, a student in the course and Jane’s advisee, has since mobilized towards anti-colonial organizing. Purposely experimental, the seminar attempted to rethink embodiment as intercorporeal and thus as capable of generating new ways to think about agency and solidarity in contexts marred by forms of militarization, medical violence, and histories of domination. Thinking through our bodyminds as both terrains of contestation and weapons in broader struggles for liberation required us to cultivate a classroom space of radical openness and vulnerability. We describe these efforts, as well as our broader orientation to community and struggle in the course, to show how they provided both theoretical tools and political strategies for engaging in direct action. Focusing specifically on a die-in as a form of embodied action aimed at a ceasefire in Gaza, we show how attuning to our bodies collectively can generate political consciousness, forge coalitions of anti-colonial resistance, and enable new forms of activism rooted in intercorporeality
Design, Analysis, and Implementation of an Improved Zero Voltage Switching Hybrid Voltage Divider
No full textThis thesis entails the study, design, and construction of a novel DC-DC converter topology, the Zero Voltage Switching Hybrid Voltage Divider (ZVS-HVD), with improved modularization, optimization, and compactness. The ZVS-HVD facilitates higher load currents and accomplishes DC-DC voltage division through switching inductors and capacitors. Low switching loss is achieved through zero voltage switching (ZVS) and the use of GANFET switches. An analysis of duty cycle and high-frequency operation is conducted, enabling 1 MHz switching and improvements in output voltage ripple, voltage drooping, board size, and efficiency at higher load currents. Two versions of ZVS-HVD prototypes were constructed with on-board signal generation: one optimized for efficiency (PCB1) and the other to demonstrate full ZVS capability (PCB2). Both prototypes achieve highly efficient voltage conversion of a 24V input to a 12V output at up to 120W with a board size of 70mm x 70mm. PCB1 reaches 95.87% efficiency at full load with a peak efficiency of 96.04% at 70% load. PCB2 achieves full ZVS operation across all loads, with 94.77% efficiency at full load and a peak efficiency of 95.02% at 80% load. Both versions maintain an output voltage ripple below 3% and an output error below 3.5%. Ultimately, the converter proposed in this thesis brings the ZVS-HVD closer to a state of practical integration for efficient high-current voltage conversion without the need for feedback circuitry
In-Situ Defect Detection in Selective Laser Melting using Convolutional Neural Networks
No full textThis thesis develops and trains a convolutional neural network (CNN) to classify defects in metal parts produced by selective laser melting (SLM) using acoustic emission (AE) data. The model successfully identified porosity and standard build layers with high accuracy but consistently struggled to classify lack of fusion (LOF) defects, regardless of input type, normalization, or class count. These results suggest that while AE signals contain distinguishable features for certain defect types, LOF signals may be too subtle or inconsistent for reliable detection. The study addresses the ongoing challenge of in-situ quality monitoring in metal additive manufacturing, where defects like cracks, porosity, and warping can compromise part reliability. AE sensing offers a low-cost, real-time method for defect detection, and artificial intelligence presents a powerful tool for pattern recognition within this data. Fast Fourier Transforms (FFT) and wavelet-based spectrograms were explored as CNN inputs, with testing performed on labeled AE images from SLM builds using 3 defined laser parameters. Validation of predicted defect types was carried out using metallography, microscopy and relative density measurements. The findings support the potential of AI-based AE monitoring for detecting porosity issues in SLM, while highlighting the need for further refinement in detecting LOF-related defects