Development of a computational predictive model for the nonlinear in-plane compressive response of sandwich panels with bio-foam

Wong, Yong Jie; Arumugasamy, Senthil Kumar; Mustapha, K.B.

doi:10.1016/j.compstruct.2019.01.039

Cited by 14 publications

(2 citation statements)

References 48 publications

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“…Thus, in recent times the complementary role of computational intelligence-based methods for predicting the performance of composite materials has been recognized by researchers (Bezazi et al , 2007; Mini and Sowmya, 2012; Hidayat, 2015; Kessler et al , 2002; Duan et al , 2014; Waszczyszyn and Ziemiański, 2001; Liu et al , 2018; Wong et al , 2021). Some recent applications of this approach in areas allied to the current study are given in Vahabli and Rahmati (2016), Panda et al (2015); Wu et al (2016); and Wong et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Support vector machines for predicting the compressive response of defected 3D printed polymeric sandwich structures

Mustapha

Alhiyafi

Shafi

et al. 2021

JEDT

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Purpose This study aims to investigate the prediction of the nonlinear response of three-dimensional-printed polymeric lattice structures with and without structural defects. Unlike metallic structures, the deformation behavior of polymeric components is difficult to quantify through the classical numerical analysis approach as a result of their nonlinear behavior under mechanical loads. Design/methodology/approach Geometric models of periodic lattice structures were designed via PTC Creo. Imperfections in the form of missing unit cells are introduced in the replica of the lattice structure. The perfect and imperfect lattice structures have the same dimensions – 10 mm × 14 mm × 30 mm (w × h × L). The fused deposition modelling technique is used to fabricate the parts. The fabricated parts were subjected to physical compression tests to provide a measure of their transverse compressibility resistance. The ensuing nonlinear response from the experimental tests is deployed to develop a support vector machine surrogate model. Findings Results from the surrogate model’s performance, in terms of correlation coefficient, rose to as high as 99.91% for the nonlinear compressive stress with a minimum achieved being 98.51% across the four datasets used. In the case of deflection response, the model accuracy rose to as high as 99.74% while the minimum achieved is 98.56% across the four datasets used. Originality/value The developed model facilitates the prediction of the quasi-static response of the structures in the absence and presence of defects without the need for repeated physical experiments. The structure investigated is designed for target applications in hierarchical polymer packaging, and the methodology presents a cost-saving method for data-driven constitutive modelling of polymeric parts.

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Section: Introductionmentioning

confidence: 99%

Support vector machines for predicting the compressive response of defected 3D printed polymeric sandwich structures

Mustapha

Alhiyafi

Shafi

et al. 2021

JEDT

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“…Wong et al. 29 evaluated the performance of the application of simple linear regression, ANN, and ANFIS to predict the compressive strength of the sandwich panel with aluminum facings and bio-foam core. The results indicated that all developed models could offer a suitable estimate of the response being examined, but the model proposed by ANFIS had the highest performance compared to the other models.…”

Section: Introductionmentioning

confidence: 99%

Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

Nazerian

Naderi

Partovinia

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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The aim of this paper was to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) and to predict the flexural strength of the sandwich panels made with thin medium density fiberboard as surface layers, and polyurethane foam as a core layer, by applying metaheuristic optimization methods. For this purpose, various models, namely ant colony optimization for the continuous domain (ACOR), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) were applied and compared, as different efficient bio-inspired paradigms, to assess their suitability for training the adaptive neuro-fuzzy inference system model. The predicted values of the flexural strength resulting from applying adaptive neuro-fuzzy inference system trained by ACOR, DE, GA, and PSO, were compared with the values derived from adaptive neuro-fuzzy inference system classical model. The molar ratio of formaldehyde to melamine and urea, sandwich panel thickness, and the weight ratio of the modified starch to MUF resin (OS/MUF weight ratio) were used as an input variables and the modulus of rupture was used as an output one. The developed hybrid models were used to predict the values of the modulus of rupture obtained from experimental tests. In order to evaluate and compare the performance of the models, three performance criteria were employed namely, determination coefficient (R2), root mean square error, and mean absolute percentage error. It was found that ANFIS–ACOR, ANFIS–DE, ANFIS–GA, and ANFIS–PSO showed different performance ratios compared to the predicting model. In addition, the ANFIS–GA model is found to be by far more accurate than the other hybrid models.

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Flatwise Compression and Buckling Characterizations of Adhesive-Free Additively Manufactured Defected Architected Structures

Mustapha

Saad

Abakr

2020

Lecture Notes in Mechanical Engineering

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Development of a computational predictive model for the nonlinear in-plane compressive response of sandwich panels with bio-foam

Cited by 14 publications

References 48 publications

Support vector machines for predicting the compressive response of defected 3D printed polymeric sandwich structures

Support vector machines for predicting the compressive response of defected 3D printed polymeric sandwich structures

Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels

Flatwise Compression and Buckling Characterizations of Adhesive-Free Additively Manufactured Defected Architected Structures

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