Materials Science and Engineering Commons^™

Elucidating The Interfacial Bonding Behavior Of Over-Molded Hybrid Fiber Reinforced Polymer Composites: Experiment And Multiscale Numerical Simulation, Gideon A. Lyngdoh, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

This paper implements molecular dynamics (MD) simulation using reactive force field (ReaxFF) to evaluate the

atomistic origin of the interfacial behavior in the overmolded hybrid unidirectional continuous carbon fiber low-melt PAEK (CFR- LMPAEK)-short carbon fiber reinforced PEEK (SFR-PEEK) polymer composites. From the MD simulation, it was observed that the

interfacial properties improve with increasing maximum processing temperature and injection pressure although such an improving trajectory gets saturated beyond specific limits. The interfacial strength and fracture response of the hybrid polymer system at the interface are also evaluated. The mechanical responses obtained from MD simulation are used as adhesive properties in …

Fracture Response Of Wollastonite Fiber-Reinforced Cementitious Composites: Evaluation Using Micro-Indentation And Finite Element Simulation, Sami Doner, Gideon A. Lyngdoh, Sumeru Nayak, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

The paper presents indentation studies on wollastonite fiber incorporated cementitious systems. The acicular nature of the fibers is poised to delay the coalescence of micro-cracks in such systems thus leading to tougher building materials. Towards that end, load-penetration depth results from the indentation studies are employed to ascertain elastic and fracture properties of wollastonite-incorporated cementitious composites. While up to 10% mass-based cement-replacement by wollastonite results in comparable elastic moduli as compared to conventional binders, the fracture toughness increases by as much as 33%. In order to gain insights into the toughening mechanisms brought about by the fine fibers, a microstructure-guided …

Prediction Of Concrete Strengths Enabled By Missing Data Imputation And Interpretable Machine Learning, Gideon A. Lyngdoh, Mohd Zaki, N.M. Anoop Krishnan, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes requires a large, complete, and consistent dataset. However, the availability of such datasets is limited as the datasets often suffer from incompleteness because of missing data corresponding to different input features, which makes the development of robust ML-based predictive models challenging. Besides, as the degree of complexity in these ML models increases, the interpretation of the results becomes challenging. These interpretations of results are critical towards the development of efficient materials design strategies for enhanced materials performance. To address these challenges, this paper implements different data imputation approaches for enhanced …

Predicting The Near Field Underwater Explosion Response Of Coated Composite Cylinders Using Multiscale Simulations, Experiments, And Machine Learning, Sumeru Nayak, Gideon A. Lyngdoh, Arun Shukla, Sumanta Das

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Prediction of underwater explosion response of coated composite cylinders using machine learning (ML) requires a large, consistent, accurate, and representative dataset. However, such reliable large experimental dataset is not

readily available. Besides, the ML algorithms need to abide by the fundamental laws of physics to avoid non- physical predictions. To address these challenges, this paper synergistically integrates ML with high- throughput multiscale finite element (FE) simulations to predict the response of coated composite cylinders

subjected to nearfield underwater explosion. The simulated responses from the multiscale approach correlate very well with the experimental observations. After validation of the multiscale approach, a …