New Computational Approach Speeds Design of Functionally Graded Materials

 A research team led by Northwestern Engineering’s Ian McCue has developed a computational framework that accelerates the design of functionally graded materials by several orders of magnitude, opening new opportunities for applications in aerospace and beyond. 

The study introduces an approach that integrates robotic path planning algorithms with an on-the-fly sampling scheme to identify optimal composition gradients between dissimilar materials. By applying these algorithms, the framework reduces computational time from weeks or months to hours.

“Existing methods for computational gradient design were too slow when used on practical alloy systems hindering their adoption,” said McCue, the Morris E. Fine Junior Professor in Materials and Manufacturing at the McCormick School of Engineering. “With the new framework put forward in this paper, optimal solutions to practical problems can be generated in a matter of hours rather than weeks or months.”

The researchers also introduced a new idea about how phase diagrams are structured, which helps the algorithm find faster and more efficient paths through the material’s possible compositions.

“This work is exciting because it has the potential to greatly enhance the performance of multi-material systems,” McCue said. “By enabling the design of favorable gradients between dissimilar alloys bulky mechanical joints can be replaced with strong metallurgical bonds.” 

McCue and his team presented their work in the paper “On-the-Fly Path Planning for the Design of Compositional Gradients in High Dimensions,” published recently in the journal Materials & Design.

The framework developed by the team addresses a longstanding challenge in joining dissimilar materials. Traditional methods often rely on mechanical fasteners, which increase weight and bulk. By enabling metallurgical bonding through composition gradients, the approach offers lighter, stronger solutions.

“This is particularly important in aerospace applications such as NASA’s superelastic tire for future Moon and Mars missions where lightweight compact solutions are paramount,” McCue said.

The research is tied to McCue’s NASA Early Career Faculty Award focused on developing new joining methods for aerospace applications. The computational advances complement experimental efforts in graded materials within the McCue group, providing tools to quickly generate optimal gradients for additive manufacturing and other processes.

The team plans to integrate the framework into current projects to accelerate experiments and guide the design of new materials. McCue and his colleagues aim to use the results to quickly identify and test optimal material gradients.

Future work will also extend the framework to account for constraints specific to various manufacturing processes, tailoring designs to different synthesis methods.

“By combining computational tools from robotics with materials science challenges, the work demonstrates the power of interdisciplinary approaches,” McCue said. “The framework offers a pathway to faster design and implementation of functionally graded materials in applications where weight, strength, and performance are critical.”