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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.
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Analyzing Expert Design Cost Estimation for Additive Manufacturing
Abstract Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges.
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- Award ID(s):
- 2309250
- PAR ID:
- 10439736
- Date Published:
- Journal Name:
- ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2022-90260
