86 citations · 175 across the 10 of their papers we have counts for
12 papers
Process Optimization Under Uncertainty for Improving the Bond Quality of Polymer Filaments in Fused Filament Fabrication
Berkcan Kapusuzoglu, Matthew Sato, Sankaran Mahadevan +1
This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabric…
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
Berkcan Kapusuzoglu, Sankaran Mahadevan, Shunsaku Matsumoto +2
This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for ti…
Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
Berkcan Kapusuzoglu, Paromita Nath, Matthew Sato +2
This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed…
Adaptive surrogate modeling for high-dimensional spatio-temporal output
Berkcan Kapusuzoglu, Shunsaku Matsumoto, Yoshitomo Miyagi +2
This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is…
Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data
Berkcan Kapusuzoglu, Sankaran Mahadevan
When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the accuracy a…
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
Berkcan Kapusuzoglu, Sankaran Mahadevan
This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for pr…