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Abstract Designing the 3D layout of interconnected systems (SPI2), which is a ubiquitous task in engineered systems, is of crucial importance. Intuitively, it can be thought of as the simultaneous placement of (typically rigid) components and subsystems, as well as the design of the routing of (typically deformable) interconnects between these components and subsystems. However, obtaining solutions that meet the design, manufacturing, and life-cycle constraints is extremely challenging due to highly complex and nonlinear interactions between geometries, the multi-physics environment in which the systems participate, the intricate mix of rigid and deformable geometry, as well as the difficult manufacturing and life-cycle constraints. Currently, this design task heavily relies on human interaction even though the complexity of searching the design space of most practical problems rapidly exceeds human abilities. In this work, we take advantage of high-performance hierarchical geometric representations and automatic differentiation to simultaneously optimize the packing and routing of complex engineered systems, while completely relaxing the constraints on the complexity of the solid shapes that can be handled and enable intricate yet functionally meaningful objective functions. Moreover, we show that by simultaneously optimizing the packing volume as well as the routing lengths, we produce tighter packing and routing designs than by focusing on the bounding volume alone. We show that our proposed approach has a number of significant advantages and offers a highly parallelizable, more integrated solution for complex SPI2 designs, leading to faster development cycles with fewer iterations, and better system complexity management. Moreover, we show that our formulation can handle complex cost functions in the optimization, such as manufacturing and life-cycle constraints, thus paving the way for significant advancements in engineering novel complex interconnected systems. 

Abstract The temperature history of an additively manufactured part plays a critical role in determining process–structure–property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data. 

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the R2 metric, which provides a relative measure of the model’s performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by R2 and maintains generalizability to geometries that were not included in the training process.