Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm

Tran, Van-Thien; Nguyen, Trung-Kien; Nguyen‐Xuan, H.; Wahab, Magd Abdel

doi:10.1016/j.tws.2022.110267

Cited by 117 publications

(7 citation statements)

References 67 publications

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“…Hoang-Le Minh et al [19,20] aim at a damage assessment for a high-rise concrete structure, introduce a termite life cycle optimizer (TLCO) algorithm, and proved that can improve the convergence speed and accuracy, that can make a significant improvement in the damage identification of large-scale structures. Van-Thien Tran et al [21] developed a BCMO-ANN algorithm that combines artificial neural network and balancing composite motion optimization, it can effectively solve the vibration and buckling behaviors optimization problems caused by the uncertainty of material properties. In my research, a stress-life curve fitting optimization way based on neighborhood rough set reduction with improved firefly algorithm (IFANRSR) is proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Zou

Zhu

2023

Frattura Ed Integrità Strutturale

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S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal. In this work, the titanium alloy welded joints' fatigue data is used as analysis data, and the neighborhood rough set reduction with improved firefly algorithm efficient method of fitting stress-life curves is set forth. The welded joint's fatigue decision system is built with fatigue data. The continuous iteration of the firefly algorithm is used as the search strategy, the neighborhood rough set is adopted to decrease attributes, and the major deciding elements of welded joints' fatigue life is identified. The fatigue characteristic domains are divided based on the neighborhood rough set reduction with improved firefly algorithm's key factor set, and the S-N curves can then be fitted to each domain individually. According to the goodness-of-fit analysis, the proposed approach can improve fatigue life accuracy and reduce sample scattering from fatigue.

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Section: Introductionmentioning

confidence: 99%

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Zou

Zhu

2023

Frattura Ed Integrità Strutturale

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“…[27][28][29][30] In summary, the optimization of the process parameters is crucial to achieving the desired performance of the WAAM parts. ML-based approaches, such as ANN, [31,32] DT, [17] Fibonacci sequence-based optimization, [33] Shrimp and Goby association search algorithm, [34] and SVM, [23] have emerged as powerful tools for optimizing the process parameters and property prediction in WAAM compared to traditional methods. The significance of this research lies in the optimization of a multitude of process parameters, which surpasses the limited scope of previous studies that have examined fewer variables.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wire Arc Additive Manufacturing Process Parameters for Low‐Carbon Steel and Properties Prediction by Support Vector Regression Model

Barik,

Bhandari,

Mondal

2023

steel research int.

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This study aims to optimize the process parameters of wire arc additive manufacturing for ER70S6 steel and build a machine‐learning model to predict the properties of deposited specimens. Process parameters such as current, voltage, and travel speed are optimized considering other process parameters constant (gas flow rate, contact tip to the work distance, and preheat). The optimization is made using the response surface method and validated the properties by experimentation, including tensile testing and metallography. A support vector regression machine‐learning model is implemented to predict the material’s properties to substantiate the outcomes’ values in every possible combination for the given parameters. The study’s findings reveal a significant enhancement in specimen quality, marked by reduced irregularities and porosity, and a remarkable increase in ultimate tensile strength up to 40%, validated through the SVR model. This study sketches a valuable path that can be extended to predict properties of other material systems.This article is protected by copyright. All rights reserved.

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“…The rapid advancement of material science and relevant manufacturing technologies in recent years has enabled us to design and fabricate a wide range of advanced engineering structures. Among these structures, cellular materials have received significant attention from researchers worldwide due to their fascinating characteristics such as low density, high energy absorption capacity, or excellent thermal efficiency [1][2][3][4][5][6][7]. Consequently, cellular structures have seen widespread application across a wide range of engineering disciplines in recent years.…”

Section: Introductionmentioning

confidence: 99%

Dynamic instability characteristics of graphene‐reinforced cellular sandwich plates using a three‐variable isogeometric approach

Nguyen

2023

Z Angew Math Mech

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The primary objective of the current study is to introduce a potent computational model for analyzing the dynamic instability response of advanced sandwich plate models subjected to various time‐dependent in‐plane edge loadings. To achieve this, we employ the finite element formulations derived from high‐order shear deformation plate theory (HSDT) involving only three unknowns in conjunction with NURBS‐based isogeometric analysis. Additionally, advanced sandwich plate models are constituted by a middle layer with either a uniform or symmetric porosity distribution and two outer layers which are reinforced with graphene nanoplatelets (GNPs) to boost dynamic structural performance. The fundamental dynamic instability region of the sandwich plates can be then obtained by solving the Mathieu‐Hill equation using the Bolotin's method. Numerous numerical studies are performed to evaluate the impacts of numerous input parameters on the dynamic stability behavior of the cellular three‐layer plate structures. The crucial role of internal porosity distribution as well as GNPs reinforcement phase in enhancing the instability performance of the cellular structures is thoroughly evaluated. With the current results, this research provides important insights into the development and application of cellular materials as well as the GNPs for designing advanced engineering structures in the future.

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Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm

Cited by 117 publications

References 67 publications

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

Optimization of Wire Arc Additive Manufacturing Process Parameters for Low‐Carbon Steel and Properties Prediction by Support Vector Regression Model

Dynamic instability characteristics of graphene‐reinforced cellular sandwich plates using a three‐variable isogeometric approach

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