AI based Multi Criterion Optimization Solution for Magnetic Design

Designing magnetic components is a complex task involving over forty parameters with numerous possible values, leading to countless design permutations. This complexity often hampers the pursuit of optimal designs, as results can vary based on different expert opinions. Traditional computational methods struggle to manage this challenge. To address this, an advanced AI-based autonomous approach that uses sophisticated algorithms to evaluate all design parameters comprehensively was developed. This method generates optimized designs that surpass the capabilities of experienced human designers, paving the way for significant advancements in magnetic design. This paper details the innovative methodology and its advantages, showcasing how AI-driven optimization can reduce design time by over 95% while exploring parameter combinations that human designers might overlook due to time constraints.

Publication Details

Journal
CLOK (University of Central Lancashire)
Published
2025-06-30
DOI
https://doi.org/10.30420/566541055
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
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article

AI based Multi Criterion Optimization Solution for Magnetic Design

CLOK (University of Central Lancashire)
Topology Optimization in Engineering
article

AI based Multi Criterion Optimization Solution for Magnetic Design

article en

Abstract

Designing magnetic components is a complex task involving over forty parameters with numerous possible values, leading to countless design permutations. This complexity often hampers the pursuit of optimal designs, as results can vary based on different expert opinions. Traditional computational methods struggle to manage this challenge. To address this, an advanced AI-based autonomous approach that uses sophisticated algorithms to evaluate all design parameters comprehensively was developed. This method generates optimized designs that surpass the capabilities of experienced human designers, paving the way for significant advancements in magnetic design. This paper details the innovative methodology and its advantages, showcasing how AI-driven optimization can reduce design time by over 95% while exploring parameter combinations that human designers might overlook due to time constraints.

CLOK (University of Central Lancashire)
Openalex Percentile: Top 55%
Topology Optimization in Engineering
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