Engineering Commons^™

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 …

Modeling And Solution Methodologies For Mixed-Model Sequencing In Automobile Industry, Ibrahim Ozan Yilmazlar

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The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the …

Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan

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The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …

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 …