Hierarchical optimization approaches in designing surface hardening induction systems

Baldan, Marco; Nikanorov, Alexander; Nacke, B.

doi:10.1088/1757-899x/424/1/012067

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Новым направлением исследований является разработка линейного индукционного отжига после процесса индукционной закалки [3]. Из-за низкотемпературной обработки при отжиге заготовка находится при температуре Кюри, что требует большего внимания к оптимальной частоте процесса и оптимальной конструкции индуктора.…”

Section: эбааке вашпенстunclassified

“…Индукционный нагрев, всегда сопровождаемый движущими силами в материале, может привести к проблемам, если заготовки покрыты легкоплавким сплавом, таким как Al-Si. В работах [3,6] были исследованы и оценены различные способы индукционного нагрева (продольный нагрев, поперечный магнитный нагрев, одностадийный индукционный нагрев, гибридный нагрев индукционным и обычным нагревом) с точки зрения потенциалов и ограничений индукционного нагрева для формирования горячего металла в зависимости от условий производства. В рамках реализуемого в настоящее время исследовательского проекта было проведено подробное численное и экспериментальное исследование одностадийного индукционного нагрева [6].…”

Section: эбааке вашпенстunclassified

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Recent scientific research on electrothermal metallurgical processes

Baake

Shpenst

2019

PMI

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В настоящее время широкий спектр промышленных металлургических процессов нагрева и плавления реализуется на основе использования электротермических технологий. Применение электротермических процессов является предпочтительным как с технологической, так и с экологической и экономической точек зрения. Достигнутый технологический уровень применения электронагрева и плавки является очень высоким и постоянно совершенствуется из-за растущей сложности аппаратуры и строгих требований к производительности и качеству продукции.В статье приводятся наиболее перспективные решения по эффективному нагреву и плавлению с помощью электротермических технологий в металлургическом производстве, а также отдельные примеры использования данных технологий, такие как индукционный нагрев для ковки или прокатки заготовок, термическая обработка полос и листов, процессы пресс-упрочнения, индукционное упрочнение поверхности сложной геометрии, индукционная сварка и индукционная плавка.

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Section: эбааке вашпенстunclassified

Recent scientific research on electrothermal metallurgical processes

Baake

Shpenst

2019

PMI

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“…The distance E-A, just like F-B, G-C, and H-D is 1 mm. On the base of the work of Baldan et al (2018b), making use of a one-dimensional model, frequency and heating time were firstly optimized. They are equal to 10.7 kHz and 2 s , respectively.…”

Section: Optimal Design Problemmentioning

confidence: 99%

A parallel multi-fidelity optimization approach in induction hardening

Baldan

Nikanorov

Nacke

2019

COMPEL

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Purpose Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.

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“…There is one aspect that differentiates the algorithm we used from its original version and it concerns the offspring generation. Instead of using only the differential evolution (DE) operators Optimal design of induction coils (Price et al, 2005), as already successfully shown in Baldan et al(2018aBaldan et al( ) (2018b, different mechanisms [from non-dominated sorting genetic algorithm-II, DE and BO (Deb et al, 2002;Dan, 2008)] are adaptively used. That means preferring the operators that have generated "good" children in the previous iterations.…”

Section: Evolutionary Algorithm Based On Adaptive Gaussian Process-assisted Differential Evolution For Mems Design Optimization (Agdemo)mentioning

confidence: 99%

“…Optimization plays a key role in designing induction heating systems (Pleshivtseva et al, 2017b;Baldan et al, 2018aBaldan et al, , 2018bPleshivtseva et al, 2018aPleshivtseva et al, , 2018bPleshivtseva et al, , 2017a. Schlesselmann et al (2015) have optimized numerically the geometry of an inductor for obtaining a prescribed final temperature distribution.…”

Section: Introductionmentioning

confidence: 99%

Effective methods for optimal design of induction coils on example of surface hardening

Pleshivtseva

Baldan

Popov

et al. 2019

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Purpose This paper aims to describe main ideas and demonstrates results of the research activities carried out by the authors in the field of optimal design concepts for induction heater for surface hardening. The main goal of the research studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the optimal design problem that is mathematically formulated in terms of the one of the most important optimization criteria for surface hardening technology, e.g. maximum temperature uniformity within the hardening surface layer. Design/methodology/approach Evolutionary algorithm based on Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) and alternance method of parametric optimization based on optimal control theory are applied as effective tools for the practice-oriented problem for optimization of induction heater design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into automated optimization procedure. Findings Optimization procedures are tested and investigated for optimal design problem solution on the examples of induction hardening of steel cylindrical billet. Practical implications Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design of different electromagnetic devices and systems. Originality/value This paper describes main ideas and results of the research activities carried out by the authors in the field of optimal design of induction heaters for hardening based on numerical coupled electromagnetic and temperature field analysis. The implementation of the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. This paper also demonstrates that different optimization strategies used (evolutionary algorithm based on AGDEMO and alternance method of optimal control theory) are effective for real-life industrial applications for optimization of induction heaters design.

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Hierarchical optimization approaches in designing surface hardening induction systems

Cited by 8 publications

References 7 publications

Recent scientific research on electrothermal metallurgical processes

Recent scientific research on electrothermal metallurgical processes

A parallel multi-fidelity optimization approach in induction hardening

Effective methods for optimal design of induction coils on example of surface hardening

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