Research Activities
Data driven prediction of effective properties of TPMS based architected unit cells
Data driven prediction of effective properties of TPMS based architected unit cells
Data driven prediction of effective properties of TPMS based architected unit cells
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The objective of this work will be to collect the data from a simulation-driven approach and further develop algorithms to predict the effective properties of TPMS-based bio-inspired structures and further validate the algorithm on unseen data.
STATUS: Ongoing
Predictive Modeling and Optimization of Additive Friction Stir Deposition (AFSD)
Data driven prediction of effective properties of TPMS based architected unit cells
Data driven prediction of effective properties of TPMS based architected unit cells
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This research aims to integrate Machine Learning (ML) techniques with the thermal-mechanical simulation of the Additive Friction Stir Deposition (AFSD) process to predict and optimize process parameters for improved material properties and deposition quality.
STATUS: Ongoing
Structural integrity analysis of TPMS based architected structures
Data driven prediction of effective properties of TPMS based architected unit cells
Structural integrity analysis of TPMS based architected structures
In this work, a data-driven approach will be developed for evaluating the mechanical performance of TPMS-based lattice structures.
STATUS: Ongoing
Homogenization of Graph Unit Cell based architected metamaterial
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
Structural integrity analysis of TPMS based architected structures
Homogenization refers to the process of replacing a heterogeneous material with an equivalent homogeneous material in finite element analysis. Heterogeneous materials have non-uniform properties that vary spatially across the material. This spatial variation makes analysis complex.
STATUS: Completed
TALK TO LATTICE: Tailored LLM for Architected Materials
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
The main goal of this research project is to develop a customized LLM retrieval AI agent that is more accurate in terms of answering user queries.
STATUS: Completed
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
Machine learning-based structural integrity assessment of AlSi10Mg parts fabricated by L-PBF Process
In this ongoing research work, the main objective is to evaluate the mechanical properties of the AlSi10Mg parts fabricated by the laser powder bed fusion process by developing new machine-learning algorithms.
STATUS: Completed
COLLABORATIVE WORK WITH: Dr. Vijaykumar S Jatti
Evaluation of Tensile Properties of the Inconel based cellular material specimens
Evaluation of Tensile Properties of the Inconel based cellular material specimens
Evaluation of Tensile Properties of the Inconel based cellular material specimens
In this research work, structure-property relationship of the architected material based tensile specimens will be evaluated.
STATUS: Completed
COLLABORATIVE WORK WITH: Dr. Vijaykumar S Jatti
Friction Stir Additive Manufacturing of AA6061 Plates
Evaluation of Tensile Properties of the Inconel based cellular material specimens
Evaluation of Tensile Properties of the Inconel based cellular material specimens
In this research, a data-driven approach will be employed to model the mechanical properties of double-layered friction stir additive manufactured AA6061 plates.
STATUS: Ongoing
COLLABORATIVE WORK WITH: Dr. Vijaykumar S Jatti