Digital Commons Network^™

Deep Reinforcement Learning For The Design Of Structural Topologies, Nathan Brown

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Advances in machine learning algorithms and increased computational efficiencies have given engineers new capabilities and tools for engineering design. The presented work investigates using deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to design 2D structural topologies. Three unique structural topology design problems are investigated to validate DRL as a practical design automation tool to produce high-performing designs in structural topology domains.

The first design problem attempts to find a gradient-free alternative to solving the compliance minimization topology optimization problem. In the proposed …

A Value-Based Sequential Optimization Framework For Efficient Materials Design Considering Uncertainty And Variability, Maher Alghalayini

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Many problems in engineering and science can be framed as decision problems in which we choose values for decision variables that lead to desired outcomes. Notable examples include maximizing lift in airplane wing design, improving the efficiency of a power plant, or identifying processing protocols resulting in structural materials with desired mechanical properties. These problems typically involve a significant degree of uncertainty about the often-complex underlying relationships between the decision variables and the outcomes. Solving such decision problems involves the use of computational models or physical experimentation to generate data to make predictions and test hypotheses. As a result, both …

Comparative Design Space For Bistable Composites: An Integrated Framework Of Optimization, Finite Element Analysis, And Experimental Testing, Jonathan Bolanos

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Bistable composites are a class of advanced materials that can actuate between two stable shapes, making them attractive for a wide range of engineering applications. However, designing these composites to achieve optimal performance remains a challenging task. To address the challenge, this research develops an integrated framework that combines a genetic algorithm optimization technique, finite element analysis in Abaqus, and experimental testing to explore the design comparative space for square bistable composites composed of DA 409 carbon fibers. This leads to the study of generating an optimization algorithm to account for the relationship between the chances of a successful maximum …