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Dr Zahur Ullah's Outputs (13)

An adaptive B-spline representation of topology optimization design for Additive Manufacturing (2023)
Journal Article
Khan, I., Ullah, B., Siraj-ul-Islam, Ullah, Z., & Khan, W. (2023). An adaptive B-spline representation of topology optimization design for Additive Manufacturing. Advances in Engineering Software, 186, Article 103552. https://doi.org/10.1016/j.advengsoft.2023.103552

Topology optimization is a renowned structural optimization approach used to compute the optimal topology for the enhancement of structural performance. It has been in common practice in different engineering fields such as automobile and aerospace.... Read More about An adaptive B-spline representation of topology optimization design for Additive Manufacturing.

An overset improved element-free Galerkin-finite element method for the solution of transient heat conduction problems with concentrated moving heat sources (2023)
Journal Article
Álvarez-Hostos, J. C., Ullah, Z., Storti, B. A., Tourn, B. A., & Zambrano-Carrillo, J. A. (2024). An overset improved element-free Galerkin-finite element method for the solution of transient heat conduction problems with concentrated moving heat sources. Computer Methods in Applied Mechanics and Engineering, 418(Part B), Article 116574. https://doi.org/10.1016/j.cma.2023.116574

A novel Overset Improved Element-Free Galerkin-Finite Element Method (Ov-IEFG-FEM) for solving transient heat conduction problems with concentrated moving heat sources is introduced in this communication. The method is a mesh-less/mesh-based chimera-... Read More about An overset improved element-free Galerkin-finite element method for the solution of transient heat conduction problems with concentrated moving heat sources.

Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction (2023)
Journal Article
Millen, S., Dahale, M., Fisher, T., Samy, A., Thompson, K., Ramaswamy, K., Ralph, C., Archer, E., McIlhagger, A., Ullah, Z., & Falzon, B. (2023). Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction. Composite Structures, 318, Article 117104. https://doi.org/10.1016/j.compstruct.2023.117104

A novel finite element modelling approach is presented which incorporates representative binder yarn compaction, for simulating the low-velocity impact (LVI) and compression after impact (CAI) response of 3D woven layer-to-layer carbon/epoxy composit... Read More about Modelling low-velocity impact damage and compression after impact of 3D woven structures considering compaction.

Studies on the impact and compression-after-impact response of ‘Double-Double’carbon-fibre reinforced composite laminates (2023)
Book Chapter
Millen, S. L. J., Aravand, M. A., Ullah, Z., & Falzon, B. (2023). Studies on the impact and compression-after-impact response of ‘Double-Double’carbon-fibre reinforced composite laminates. In S. W. Tsai, B. G. Falzon, & A. Aravand (Eds.), Double-double: simplifying the design and manufacture of composite laminates (161-180). (2nd ed.). Stanford University Press

This chapter presents comparative high fidelity computational finite element (FE) modelling of low-velocity impact (LVI) damage and compression-after-impact (CAI) strength of both ‘Legacy Quad Laminates’ (LQL) and ‘Double-Double’ DD laminates, using... Read More about Studies on the impact and compression-after-impact response of ‘Double-Double’carbon-fibre reinforced composite laminates.

Exploring the Elastic Properties of Woven Fabric Composites: A Machine Learning Approach for Improved Analysis and Design (2023)
Presentation / Conference Contribution
Hayat, K., Ullah, Z., Siddique, S., & Ahmad, Z. (2023, May). Exploring the Elastic Properties of Woven Fabric Composites: A Machine Learning Approach for Improved Analysis and Design. Presented at 1st International Conference on Modern Technologies in Mechanical & Materials Engineering (MTME-2023), Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan

Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elasti... Read More about Exploring the Elastic Properties of Woven Fabric Composites: A Machine Learning Approach for Improved Analysis and Design.

Comprehensive inter-fibre failure analysis and failure criteria comparison for composite materials using micromechanical modelling under biaxial loading (2023)
Journal Article
Wan, L., Ullah, Z., Yang, D., & Falzon, B. G. (2023). Comprehensive inter-fibre failure analysis and failure criteria comparison for composite materials using micromechanical modelling under biaxial loading. Journal of Composite Materials, 57(18), 2919-2932. https://doi.org/10.1177/00219983231176609

Inter-fibre failure analysis of carbon fibre-reinforced polymer (CFRP) composites, under biaxial loading conditions, has been a longstanding challenge and is addressed in this study. Biaxial failure analysis of IM7/8552 CFRP unidirectional (UD) compo... Read More about Comprehensive inter-fibre failure analysis and failure criteria comparison for composite materials using micromechanical modelling under biaxial loading.

Probability embedded failure prediction of unidirectional composites under biaxial loadings combining machine learning and micromechanical modelling (2023)
Journal Article
Wan, L., Ullah, Z., Yang, D., & Falzon, B. G. (2023). Probability embedded failure prediction of unidirectional composites under biaxial loadings combining machine learning and micromechanical modelling. Composite Structures, 312, Article 116837. https://doi.org/10.1016/j.compstruct.2023.116837

This study presents a data-driven, probability embedded approach for the failure prediction of IM7/8552 unidirectional carbon fibre reinforced polymer (CFRP) composite materials under biaxial stress states based on micromechanical modelling and artif... Read More about Probability embedded failure prediction of unidirectional composites under biaxial loadings combining machine learning and micromechanical modelling.