Texture-sensitive prediction of micro-spring performance using Gaussian process models calibrated to finite element simulations

Venkatraman, Aditya; Zapiain, David Montes de Oca; Lim, Hojun; Kalidindi, Surya R.

doi:10.1016/j.matdes.2020.109198

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…Lastly, the transient structural environment by ANSYS is used to analyze the modelled spring. A. Venkatraman et al [14], to make accurate projections on the functionality of micro springs, a surrogate model based on the Gaussian Process was developed. The overall computing cost was brought down thanks to the sequential design of the FE simulations.…”

Section: Introductionmentioning

confidence: 99%

Development of a computational system to design a helical spring

Kamil,

Abdulshaheed,

Kadhom

2024

IRASE

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Springs are the most basic mechanical elements used in transmission mechanisms. The rapid development of the computer and cellular industry has encouraged spring manufacturers to develop the industry to produce very small springs. Most computer-aided design programs for mechanical parts provide the possibility of designing these parts, as these programs include different types of decisions. All these decisions require coordinates for geometric data as well as metadata. The paper aims to develop software programs to design and analyze springs as one of the most significant mechanical elements used. This paper aims to develop a design software of a helical spring system, where this software is built using a computer program in the language of Visual Basic Version 5. When the user enters data into the system, the system will perform a series of complex calculations in the system, then provide a detailed report on all the engineering dimensions of the spring, and test its efficiency. The output of the software shows the required spring wire parameters. The software was tested with test data from the open literature, and the required wire spring parameters were obtained.

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Section: Introductionmentioning

confidence: 99%

Development of a computational system to design a helical spring

Kamil,

Abdulshaheed,

Kadhom

2024

IRASE

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“…In the work of Ortali et al [13] GP regression is used as a reduced-order surrogate model for fluid dynamics use cases. Venkatraman et al [14] use GP regression as a surrogate model of texture in micro-springs. GP regression can also be used on data with multiple fidelity levels, where Lee et al [15] investigate GP regression surrogate modeling with uncertain material properties of soft tissues and multi-fidelity data.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys

2022

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The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted. However, they are expensive in time and resources, and can slow down production. A promising way to overcome these drawbacks is process exploration through simulation, where the finite element method (FEM) is a well-established and robust simulation method. While FEM simulation can provide high-resolution results, it requires extensive computing resources to do so. In addition, the simulation design often depends on unknown process properties. To circumvent these drawbacks, we present a Gaussian Process surrogate model approach that accounts for real physical manufacturing process uncertainties and acts as a substitute for expensive FEM simulation, resulting in a fast and robust method that adequately depicts reality. We demonstrate that active learning can be easily applied with our surrogate model to improve computational resources. On top of that, we present a novel optimization method that treats aleatoric and epistemic uncertainties separately, allowing for greater flexibility in solving inverse problems. We evaluate our model using a typical manufacturing use case, the preforming of an Inconel 625 superalloy billet on a forging press.

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