Probabilistic process simulation to predict process induced distortions of a composite frame

Liebisch, Martin; Hein, Robert; Wille, Tobias

doi:10.1007/s13272-018-0302-7

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…One example of this is the geometric imperfection of a cylindrical shell, which heavily influences the load carrying capability and is, at the same time, dependent on the design (see, e.g., [1,2]). Further examples include the build stress and distortion in additively manufactured metal parts, which are dependent on the design and the support structure (see, e.g., [11]), or in composite structures where build stress and distortion are influenced by the shape and the laminate stacking sequence [17]. The dependency of the input distribution on the design parameters can be determined by probabilistic manufacturing simulations, which is a challenging task on its own and beyond the scope of this paper.…”

Section: B Kriegesmannmentioning

confidence: 99%

Robust design optimization with design-dependent random input variables

Kriegesmann

2019

Struct Multidisc Optim

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This paper addresses the dependency of design parameters and random variables within robust design optimization. If the stochastic distributions of random input variables are design-dependent, then this dependency must be included in the gradient, when using gradient-based optimization methods. The paper provides the basic theoretical principles and two approaches for incorporating design-dependent distributions of random variables in robust design optimization: one approach based on Monte Carlo sampling and another based on Taylor series expansions. Both these approaches do not require additional structural analyses (e.g. finite element simulations). Describing the design dependency of input distributions can, however, be a challenging task. Numerical applications to different academic examples are presented, demonstrating the potential of the proposed approaches and several implications that may emerge in the process.

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Section: B Kriegesmannmentioning

confidence: 99%

Robust design optimization with design-dependent random input variables

Kriegesmann

2019

Struct Multidisc Optim

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“…It is widely used to create small batches or prototypes [1]. Similar to other manufacturing processes [2], thermal effects influence the structural behavior of the resulting parts. However, a major challenge arises because the material used is created during the process, making it difficult to conduct representative testing in advance.…”

Section: Introductionmentioning

confidence: 99%

Peridynamic framework to model additive manufacturing processes

Hesse,

Willberg,

Hein

et al. 2023

Proc Appl Math and Mech

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This paper introduces a novel framework for analyzing additive manufacturing processes. By employing a peridynamic mesh‐free implementation, it overcomes certain limitations encountered with finite element‐based approaches in regard to defects and fractures. The framework includes the implementation of thermodynamical material behavior and a novel boundary detection algorithm, as the outer surface area will undergo changes during the manufacturing process. The proposed methods are thoroughly validated, and a comprehensive example is provided as a demonstration.

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Peridynamic Framework to Model Additive Manufacturing Processes

Willberg,

Hesse,

Winkelmann

et al. 2024

Advcd Theory and Sims

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The study presents a framework for analyzing Additive Manufacturing processes within the Peridynamics (PD) software PeriLab. This framewor k employs a mesh‐free, point‐based numerical approach to approximate the continuum PD equations. Implemented within this framework are thermal, thermo‐mechanical, and simple additive models. These models have been validated against analytical solutions, Finite Element (FE) models, and Peridigm simulations. To leverage the PD mesh‐free implementation, the study introduces a novel boundary detection algorithm. This algorithm is essential because the outer surface area may change during the manufacturing process. It operates without requiring surface or topology information, relying instead on the comparison of neighborhood volume to sphere volume. Additionally, the study introduces a wrapper that generates the mesh necessary for simulating the printing process, based on the G‐code machine input path. Finally, the study presents a comprehensive analysis of an L‐shaped profile utilizing the developed features, comparing the results with those obtained from an Abaqus solution.

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Probabilistic process simulation to predict process induced distortions of a composite frame

Cited by 5 publications

References 20 publications

Robust design optimization with design-dependent random input variables

Robust design optimization with design-dependent random input variables

Peridynamic framework to model additive manufacturing processes

Peridynamic Framework to Model Additive Manufacturing Processes

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