Award Abstract # 2105811
Bridging Locally Stress?Constrained Topology Optimization and Additive Manufacturing

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: THE TRUSTEES OF PRINCETON UNIVERSITY
Initial Amendment Date: November 2, 2021
Latest Amendment Date: November 2, 2021
Award Number: 2105811
Award Instrument: Standard Grant
Program Manager: Siddiq Qidwai
sqidwai@nsf.gov
 (703)292-2211
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: November 1, 2021
End Date: October 31, 2024 (Estimated)
Total Intended Award Amount: $498,773.00
Total Awarded Amount to Date: $498,773.00
Funds Obligated to Date: FY 2022 = $498,773.00
History of Investigator:
  • Glaucio Paulino (Principal Investigator)
Recipient Sponsored Research Office: Princeton University
1 NASSAU HALL
PRINCETON
NJ  US  08544-2001
(609)258-3090
Sponsor Congressional District: 12
Primary Place of Performance: Princeton University
87 Prospect Avenue
Princeton
NJ  US  08544-2020
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): NJ1YPQXQG7U5
Parent UEI:
NSF Program(s): Mechanics of Materials and Str
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 022E, 024E, 026E, 9161
Program Element Code(s): 163000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Mechanics plays a fundamental role in the optimality of engineering structures at various scales. Starting in the late nineteenth century, the field of structural optimization grew out of key observations on the criticality of load path and its implication on spatial deformation. Yet, as the field has evolved in pursuit of complex engineering challenges, its focus has shifted towards formulations of tractable optimization statements more than on mechanics guiding structural optimization. This award will support the development of a general topology optimization framework based on local stress constraints that will bring the fundamental principles of mechanics at the center of structural optimality. The theoretical and computational framework will be bridged with advanced additive manufacturing techniques for convergent outcomes. The work will empower the next generation of engineers to solve challenging structural optimization problems involving a large number of local constraints, and to connect topology optimization and additive manufacturing in an inexpensive and easy?to?use way for education at all levels. Guided by theoretical, computational, experimental, and manufacturing challenges, the award will provide educational opportunities to K?12 students through university programs and integration of research with the graduate curriculum, and support dissemination of the findings to the industry and the broader community through computer codes, outreach meetings, and workshops.

This research advances the theory needed to effectively couple mechanics and optimization of highly constrained problems, the computational framework needed to solve such problems, and the manufacturing approach to fabricate the resultant spatially varying multi?lattice parts. From a mathematical perspective, a tailored augmented Lagrangian approach will be employed to solve the stress?constrained mass minimization problem by incorporating local stresses. Specific contributions will include: (i) unifying local formulations of the stress?constrained topology optimization problems to handle a wide range of failure criteria with a single strength function and analytically deriving worst?case stress states from infinite load cases that can be used to define the stress constraints; (ii) reformulating the stress constrained problem to handle an arbitrary number of linear, nonlinear, and/or microstructural materials and deriving interfacial behavior that can be used to enforce stress constraints at material interfaces; and (iii) engineering and experimentally validating functionally?graded, single? and multi?lattice optimized parts using novel manufacturing techniques, such as gray?scale digital light processing (g-DLP) without expensive stereolithography (STL) files. The research will explore a multi?phase design space with on demand structural fidelity.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

(Showing: 1 - 10 of 12)
Alcazar, Emily and Oliveira, Lorran F and Vasconcelos_Senhora, Fernando and Ramos, Adeildo S and H_Paulino, Glaucio "A smooth maximum regularization approach for robust topology optimization in the ground structure setting" Structural and Multidisciplinary Optimization , v.67 , 2024 https://doi.org/10.1007/s00158-024-03826-7 Citation Details
Giraldo-Londoño, Oliver and Russ, Jonathan B. and Aguiló, Miguel A. and Paulino, Glaucio H. "Limiting the first principal stress in topology optimization: a local and consistent approach" Structural and Multidisciplinary Optimization , v.65 , 2022 https://doi.org/10.1007/s00158-022-03320-y Citation Details
Gresia, Juana and Vasconcelos_Senhora, Fernando and Paulino, Glaucio H "Topology optimization with continuously varying load magnitude and direction for compliance minimization" Structural and Multidisciplinary Optimization , v.67 , 2024 https://doi.org/10.1007/s00158-024-03882-z Citation Details
Nale, Andrea and Chiozzi, Andrea and Senhora, Fernando V and Paulino, Glaucio H "Large-scale additive manufacturing of optimally-embedded spinodal material architectures" Additive Manufacturing , v.101 , 2025 https://doi.org/10.1016/j.addma.2025.104700 Citation Details
Russ, Jonathan B. and Paulino, Glaucio H. "On topology optimization with gradient-enhanced damage: An alternative formulation based on linear physics" Journal of the Mechanics and Physics of Solids , v.173 , 2023 https://doi.org/10.1016/j.jmps.2023.105204 Citation Details
Senhora, Fernando V. and Chi, Heng and Zhang, Yuyu and Mirabella, Lucia and Tang, Tsz Ling and Paulino, Glaucio H. "Machine learning for topology optimization: Physics-based learning through an independent training strategy" Computer Methods in Applied Mechanics and Engineering , v.398 , 2022 https://doi.org/10.1016/j.cma.2022.115116 Citation Details
Senhora, Fernando V. and Menezes, Ivan F. and Paulino, Glaucio H. "Topology optimization with local stress constraints and continuously varying load direction and magnitude: towards practical applications" Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , v.479 , 2023 https://doi.org/10.1098/rspa.2022.0436 Citation Details
Senhora, Fernando V. and Sanders, Emily D. and Paulino, Glaucio H. "OptimallyTailored Spinodal Architected Materials for Multiscale Design and Manufacturing" Advanced Materials , v.34 , 2022 https://doi.org/10.1002/adma.202109304 Citation Details
Senhora, Fernando_Vasconcelos and Sanders, Emily_D and Paulino, Glaucio_H "Unbiased mechanical cloaks" Proceedings of the National Academy of Sciences , v.122 , 2025 https://doi.org/10.1073/pnas.2415056122 Citation Details
Vijayakumaran, Harikrishnan and Russ, Jonathan B and Paulino, Glaucio H and Bessa, Miguel A "Consistent machine learning for topology optimization with microstructure-dependent neural network material models" Journal of the Mechanics and Physics of Solids , v.196 , 2025 https://doi.org/10.1016/j.jmps.2024.106015 Citation Details
Xian, Yeming and Paulino, Glaucio H and Rosen, David W "Integration of additive manufacturing process-induced material characteristics into topology optimization" Computer Methods in Applied Mechanics and Engineering , v.434 , 2025 https://doi.org/10.1016/j.cma.2024.117503 Citation Details
(Showing: 1 - 10 of 12)

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page