Machine-Learning-Assisted Inverse Design of Centrally Thickened Plates for Vibration, Buckling, and Mass Efficiency

Abstract

This study proposes a machine-learning-assisted inverse design framework for simply supported rectangular plates with a centrally located thickened region, aiming to improve vibration and buckling performance simultaneously while minimizing structural mass. A verified finite-element model based on SHELL181 elements was used to generate a 150,000-case development database, while an additional 50,000 independently generated cases were held out for final testing. The fundamental natural frequency and first critical buckling load were normalized with respect to corresponding uniform plates, yielding the performance ratios R_f and R_b. Surrogate models were trained to predict these responses and then incorporated into a constrained inverse design procedure. The inverse search identified the lowest-mass feasible candidates within the screened design pools satisfying prescribed frequency and buckling targets, after which candidate solutions were re-evaluated using finite-element reanalysis. Because surrogate overprediction near active constraints may produce designs incorrectly classified as feasible, a conservative second search was introduced using fixed guard bands of 0.020 for R_f and 0.040 for R_b, selected slightly above the largest positive deviations observed during the initial FEM recheck. Final FEM verification confirmed the feasibility of the refined designs. The framework provides an efficient and computationally practical strategy for multi-constraint inverse design of locally thickened plates.

Keywords

• buckling
• finite element analysis
• inverse design
• locally thickened plate
• natural frequency
• surrogate modeling

Supporting Institution

The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Statement

The author of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission.

Merkezi Kalınlaştırılmış Plakaların Titreşim, Burkulma ve Kütle Verimliliği için Makine Öğrenmesi Destekli Ters Tasarımı

Abstract

Bu çalışma, merkezi bir bölgede yerel olarak kalınlaştırılmış basit mesnetli dikdörtgen plakaların, minimum yapısal kütle ile birlikte titreşim ve burkulma performansının eş zamanlı olarak iyileştirilmesini hedefleyen makine öğrenmesi destekli bir ters tasarım yaklaşımı önermektedir. SHELL181 elemanlarına dayalı doğrulanmış bir sonlu elemanlar modeli kullanılarak 150.000 analizden oluşan geliştirme veri tabanı oluşturulmuş, ayrıca bağımsız nihai test için 50.000 analiz gerçekleştirilmiştir. Temel doğal frekans ve birinci kritik burkulma yükü, karşılık gelen uniform plakaların değerlerine göre normalize edilerek Rf ve Rb performans oranları tanımlanmıştır. Bu çıktıları tahmin etmek amacıyla vekil modeller geliştirilmiş ve kısıtlı ters tasarım sürecine entegre edilmiştir. Ters tasarım kapsamında, belirlenen frekans ve burkulma hedeflerini sağlayan en düşük kütleli adaylar belirlenmiş ve bu adaylar sonlu elemanlar modeliyle yeniden doğrulanmıştır. Belirlenen kısıtlar yakınındaki vekil model aşırı tahminlerinin yanlış uygulanabilir tasarımlara yol açmasını önlemek amacıyla, Rf için 0,020 ve Rb için 0,040 koruma payları içeren ikinci bir arama uygulanmıştır. Nihai sonlu elemanlar doğrulaması, geliştirilen tasarımların tüm hedefleri sağladığını göstermiştir.

Keywords

• burkulma
• doğal frekans
• sonlu elemanlar analizi
• ters tasarım
• yerel kalınlaştırılmış plaka
• vekil model

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