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29 results on '"Mouloodi, Saeed"'

22. Experimental, regression learner, numerical, and artificial neural network analyses on a complex composite structure subjected to compression loading.

Author

Mouloodi, Saeed, Rahmanpanah, Hadi, Burvill, Colin, Gohari, Soheil, and Davies, Helen M. S

Subjects
*COMPRESSION loads, *COMPOSITE structures, *FORCE & energy, *ARTIFICIAL neural networks, *MORPHOLOGY, *THOROUGHBRED horse, *COMPOSITE columns
Abstract

This paper reports on an investigation into the relationship between stiffness and applied force of an advanced biological composite structure using four techniques: experimental observation; finite element analysis (FEA); regression learner analysis; and, artificial neural networks (ANNs). The entire hydrated third metacarpal bones (MC3) from 16 thoroughbred horses were loaded in compression in an MTS machine. The stiffness was then determined from the applied force, MC3 displacement, and load exposure time. A variety of mathematical functions were fitted to the sample data points using MATLAB to demonstrate force-dependent stiffness. Two functions were found that exhibited a strong correlation between force and stiffness (R2 = 0.75). Additionally, a power function was found that demonstrated a stronger correlation between the stiffness and force (R2 = 0.81) if the exposure time was also incorporated. FEA considered the calculated force-dependent stiffness when assigning material properties. FEA results were compared with experimental data (for verification and validation), and a good agreement was found for the displacement (RMSE = 0.032 mm) and strain (RMSE = 61.85 με). Machine learning regression models were also employed to predict the stiffness of this complex structure. Applied force, exposure time, MC3 geometry (length and area of cross sections), and age were defined as the independent variables. The regression learner offered excellent reliability (R2 = 0.98) for the prediction of stiffness. Also, feedforward back-propagation artificial neural networks were employed to improve and generalize the stiffness prediction ability to a wider population. ANN regression analysis showed R = 0.992 for training, R = 0.99 for testing, and R = 0.991 for the all datasets. To confirm its accuracy, the ANN was used to predict stiffness of specific samples that were not used in its training. This offered excellent reliability in predicting real-world data that ANNs have not seen before (R = 0.976). [ABSTRACT FROM AUTHOR]

Published
2022
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26. 3D modelling, finite element analysis, and artificial neural network analysis of a complex nonlinear structure: a study on a long bone

Author

Mouloodi, Saeed and Mouloodi, Saeed

Abstract

The bodies of humans and animals act like a mechanical system, in which bones are the major link for withstanding compressive loads. The extraordinarily hierarchical essence of the structure and mechanical properties of bones makes bone a difficult material to fully comprehend. It would be difficult to find another material that possesses such multivariable and exceptional mechanical properties. The horse has been identified as a useful model to investigate the effects of mechanical loading and induced deformations on tissues and structures in humans. Also, the equine distal forelimb is the source of most career-ending injuries to sport horses in all disciplines, and as such understanding how the materials in this part of the body respond to loads is key to understanding how and why they break down. The equine third metacarpal (MC3) is a large bone that shows a relatively restricted and simple range of normal movements in-vivo, has minimal muscle attachments (lacking any muscle or ligamentous attachments for a range around 6-8 cm along its midshaft), forms an essential part of the lower forelimb in withstanding loads, is vulnerable to failure during the majority of disastrous injuries occurring in racing horses worldwide, is well-adapted (well-engineered) for exercise at high speeds, and can withstand substantial compressive load as much as 22.5 kN in-vivo. To compare this huge force that MC3 is capable of withstanding in a normal daily basis, a large SUV car weighs 20.6 kN to 23.3 kN. Furthermore, this anatomical region provides a potential insight into how complex biological materials like bone and ligament respond to loading in all species and what parameters are vital to retain their integrity during normal use. Despite recent enormous advancement in techniques, gaining deep knowledge to find correlations between whole bone shape and material, mechanical, and overall physical responses as well as properties is a daunting task due to both complexity of the materi

Published
2020

29. Localized forming limit analysis of substrate-supported metals: Influence of yield-dependent necking bound angle

Author

Bandizaki, Ali, Zajkani, Asghar, and Mouloodi, Saeed

Abstract

In this paper, the influence of functional elastomeric substrate-supported layers for enhancing potential resistance capability against localized plastic failure of advanced high strength steels is considered based on a localized necking model of vertex theory. Application of this structure leads to postponing the plastic instability of the metallic part. By defining diffuse and localized modes of deformation in a general framework, the theoretical models are developed to predict necking limits at several stress states. In addition, the results of the Hookean and neo-Hookean elastomers are compared in terms of strain hardening with the anisotropy parameter of Hill’s yield criteria. Since necking band angle (NBA) is a principal factor for the necking prediction, its effect on bifurcation events is evaluated specifically for different ratios of stress rate, and quadratic and non-quadratic yield criteria. This analysis is performed by proposing a supported and yield-dependent necking bound angle (YD-NBA). All considerations are done by providing equilibrium conditions governed over the NBA. Finally, obtained results indicate good agreements between several theoretical considerations and experimental data.

Published
2022
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