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Design of induction hardening-tempering processes by means of multi-physical models, neural networks and multi-fidelity parallel optimization

Research output: Thesis › Doctoral thesis

Authors

  • Marco Baldan

Research Organisations

Details

Original languageEnglish
QualificationDoctor of Engineering
Awarding Institution
Supervised by
Date of Award19 Mar 2021
Place of PublicationGarbsen
Print ISBNs978-3-95900-614-9, 3-95900-614-4
Electronic ISBNs978-3-95900-634-7
Publication statusPublished - 2021

Abstract

This dissertation investigates and describes, in a broad sense, numerical methods to be adopted in the design of induction hardening-tempering processes. They include multi-physical models of the direct problems, optimization algorithms to solve inverse problems, use of surrogates and parallel computing as acceleration techniques. Multi-physical models consist of electromagnetic, thermal, metallurgical and, in case of hardening, mechanical analyses too. Due to the lack of available microstructure-dependent electromagnetic properties, their identification became a necessary step to get a complete multi-physical model. It follows the introduction of evolutionary optimization algorithms to solve inverse problems involving expensive (i.e. multi physical) field evaluations, in particular, properties identification and optimal control tasks. In this, including, on the one hand, single- and multi-fidelity metamodels (e.g. Gaussian Processes and neural networks), on the other, parallel computing, turns out to be a successful strategy.

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title = "Design of induction hardening-tempering processes by means of multi-physical models, neural networks and multi-fidelity parallel optimization",
abstract = "This dissertation investigates and describes, in a broad sense, numerical methods to be adopted in the design of induction hardening-tempering processes. They include multi-physical models of the direct problems, optimization algorithms to solve inverse problems, use of surrogates and parallel computing as acceleration techniques. Multi-physical models consist of electromagnetic, thermal, metallurgical and, in case of hardening, mechanical analyses too. Due to the lack of available microstructure-dependent electromagnetic properties, their identification became a necessary step to get a complete multi-physical model. It follows the introduction of evolutionary optimization algorithms to solve inverse problems involving expensive (i.e. multi physical) field evaluations, in particular, properties identification and optimal control tasks. In this, including, on the one hand, single- and multi-fidelity metamodels (e.g. Gaussian Processes and neural networks), on the other, parallel computing, turns out to be a successful strategy.",
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note = "Doctoral thesis",
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TY - BOOK

T1 - Design of induction hardening-tempering processes by means of multi-physical models, neural networks and multi-fidelity parallel optimization

AU - Baldan, Marco

N1 - Doctoral thesis

PY - 2021

Y1 - 2021

N2 - This dissertation investigates and describes, in a broad sense, numerical methods to be adopted in the design of induction hardening-tempering processes. They include multi-physical models of the direct problems, optimization algorithms to solve inverse problems, use of surrogates and parallel computing as acceleration techniques. Multi-physical models consist of electromagnetic, thermal, metallurgical and, in case of hardening, mechanical analyses too. Due to the lack of available microstructure-dependent electromagnetic properties, their identification became a necessary step to get a complete multi-physical model. It follows the introduction of evolutionary optimization algorithms to solve inverse problems involving expensive (i.e. multi physical) field evaluations, in particular, properties identification and optimal control tasks. In this, including, on the one hand, single- and multi-fidelity metamodels (e.g. Gaussian Processes and neural networks), on the other, parallel computing, turns out to be a successful strategy.

AB - This dissertation investigates and describes, in a broad sense, numerical methods to be adopted in the design of induction hardening-tempering processes. They include multi-physical models of the direct problems, optimization algorithms to solve inverse problems, use of surrogates and parallel computing as acceleration techniques. Multi-physical models consist of electromagnetic, thermal, metallurgical and, in case of hardening, mechanical analyses too. Due to the lack of available microstructure-dependent electromagnetic properties, their identification became a necessary step to get a complete multi-physical model. It follows the introduction of evolutionary optimization algorithms to solve inverse problems involving expensive (i.e. multi physical) field evaluations, in particular, properties identification and optimal control tasks. In this, including, on the one hand, single- and multi-fidelity metamodels (e.g. Gaussian Processes and neural networks), on the other, parallel computing, turns out to be a successful strategy.

M3 - Doctoral thesis

SN - 978-3-95900-614-9

SN - 3-95900-614-4

T3 - Institut für Elektroprozesstechnik

CY - Garbsen

ER -

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