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- Tim Brix Nerenst, Tim Brix, et al.
(författare)
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Sequential Design Process for Screening and Optimization of Robust and Reliability Based on Finite Element Analysis and Meta-Modelling
- 2022
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Ingår i: Journal of Computing and Information Science in Engineering. - : ASME International. - 1530-9827 .- 1944-7078. ; 22:4
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Tidskriftsartikel (refereegranskat)abstract
- A new medical device can take years to develop from early concept to product launch. Three approaches are often combined to mitigate risks: Failure Modes and Effects Analysis (FMEA), simulation and modeling, and physical test programs. Although widely used, all three approaches are generally time-consuming and have their shortcomings: The risk probabilities in FMEA's are often based on educated guesses, even in later development stages as data on the distribution of performance is not available. Thus, the traditional use of safety factors in structural analysis versus the probabilistic approach to risk management presents an obvious misfit. Therefore, the above three approaches are not ideal for addressing the design engineer's key question; how should the design be changed to improve robustness and failure rates. The present work builds upon the existing Robust and Reliability-Based Design Optimization (R2BDO) and adjusts it to address the key questions above using Finite Element Analysis (FEA). The two main features of the presented framework are screening feasible design concepts early in the embodiment phase and subsequently optimizing the design's probabilistic performance (i.e., reduce failure rates) while using minimal computational resources. A case study in collaboration with a medical design and manufacturing company demonstrates the new framework. The optimization minimizes the failure rate (and improves design robustness) concerning three constraint functions (torque, strain, and contact pressure). Furthermore, the study finds that the new framework significantly improves the design's performance function (failure rate) with limited computational resources.
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| 2. |
- Lochner, Michelle, et al.
(författare)
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Optimizing the LSST Observing Strategy for Dark Energy Science : DESC Recommendations for the Wide-Fast-Deep Survey
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Cosmology is one of the four science pillars of LSST, which promises to be transformative for our understanding of dark energy and dark matter. The LSST Dark Energy Science Collaboration (DESC) has been tasked with deriving constraints on cosmological parameters from LSST data. Each of the cosmological probes for LSST is heavily impacted by the choice of observing strategy. This white paper is written by the LSST DESC Observing Strategy Task Force (OSTF), which represents the entire collaboration, and aims to make recommendations on observing strategy that will benefit all cosmological analyses with LSST. It is accompanied by the DESC DDF (Deep Drilling Fields) white paper (Scolnic et al.). We use a variety of metrics to understand the effects of the observing strategy on measurements of weak lensing, large-scale structure, clusters, photometric redshifts, supernovae, strong lensing and kilonovae. In order to reduce systematic uncertainties, we conclude that the current baseline observing strategy needs to be significantly modified to result in the best possible cosmological constraints. We provide some key recommendations: moving the WFD (Wide-Fast-Deep) footprint to avoid regions of high extinction, taking visit pairs in different filters, changing the 2x15s snaps to a single exposure to improve efficiency, focusing on strategies that reduce long gaps (>15 days) between observations, and prioritizing spatial uniformity at several intervals during the 10-year survey.
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| 3. |
- Nerenst, Tim Brix, et al.
(författare)
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Probabilistic Performance Evaluation and Optimization of Medical Plastic Moulded Components Subject to Large Scale Production
- 2021
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Ingår i: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). - 9780791885567
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Konferensbidrag (refereegranskat)abstract
- A new medical device can take years to develop from early concept to product launch. The long development process can be attributed to the severe consequences for the patient if the device malfunctions. Three approaches are often combined to mitigate risks: rigorous simulation and modeling, physical test programs, and Failure Mode Effect Analysis (FMEA) - all of which are time-consuming. Physical test programs are often carried out on prototype components from the same batch and, therefore, limited in revealing the actual distribution of performance. The risk probabilities are subsequently based on educated guesses. Furthermore, simulation and modeling are usually performed on nominal geometry - not accounting for variation - and only provide a safety factor against failure. The traditional use of safety factors in structural analysis versus the probabilistic approach to risk management presents an obvious misfit. Therefore, these three approaches are not ideal for addressing the two key questions that the design engineer has: 1) How often will the design fail, and 2) How should the design be changed to improve robustness and failure rates. The present work builds upon the existing Robust and Reliability-Based Design Optimization (R2BDO) and adjusts it to address the key questions above using finite element analysis. The key feature of the new framework is the focus on minimal use of computational resources while being able to screen feasible design concepts early in the embodiment phase and subsequently optimize their probabilistic performance. A case study in collaboration with a medical design and manufacturing company demonstrates the new framework. The case study includes FEA contact modeling between two plastic molded components with twelve geometrical variables. The optimization focuses on minimizing the failure rate (and improving design robustness) concerning three constraint functions (contact pressure, strain, torque). The study finds that the new framework achieves significant improvements to the component’s performance function (failure rate) with minimal computational resources.
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