Date of Award
7-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical and Civil Engineering
First Advisor
Anand Balu Nellippallil
Second Advisor
Sneha Sudhakaran
Third Advisor
Chiradeep Sen
Fourth Advisor
Kim-Doang Nguyen
Abstract
Realizing the design for Integrated Computational Materials Engineering (ICME) requires the materials, product, and manufacturing-process disciplines to be designed together. Because these disciplines interact and influence one another's decisions, coordination is needed among their distributed decision-makers. Their interactions are captured through the processing-structure-property-performance (PSPP) linkages, which must be established from limited, costly data. Moreover, each discipline introduces its own sources of uncertainty, and these propagate through the process chain to influence final product performance. The effective realization of the product-material-manufacturing system, therefore, calls for a co-design approach that enables the concurrent coordination of the interacting disciplines while quantifying and managing the different sources of uncertainty.
This dissertation proposes a series of design frameworks that integrate the Decision-Based Design (DBD) paradigm with Multidisciplinary Design Optimization (MDO), data-driven probabilistic surrogate models, Bayesian Optimization, and robust design principles. Through them, the MDO coordination strategy is brought into the satisficing DBD paradigm to enable the inverse co-design exploration of the interacting disciplines; the data-driven inverse PSPP linkages are established from limited data, allowing the quantification and propagation of uncertainty; a unified uncertainty-aware decision-support strategy, cDSP-based BO, refines the surrogate models within the robust region; and a probabilistic coordination strategy reconciles coupled responses in both mean performance and variability, identifying robust satisficing solutions.
The proposed frameworks are generic and extend to a broad class of engineering problems, providing a data-driven decision-support foundation for the robust co-design of materials, products, and manufacturing processes, thereby advancing Design for ICME under uncertainty.
Recommended Citation
Digonta, H M Dilshad Alam, "Multidisciplinary Inverse Robust Co-design of Materials, Products, and Manufacturing Processes" (2026). Theses and Dissertations. 1664.
https://repository.fit.edu/etd/1664