Enhanced Running Performance Through 3D Printed Midsole Design
Concept
Using energy return as a parameter to guide performance, can 3D printed models match or exceed the function of current foam midsoles?
Execution
Our team tried to look for an answer to the question above by designing, 3D printing, testing and analyzing the data of each prototype. We had no experience in this field, so we obviously had to go through many iterations of samples in order to have functioning prototypes that would express significant data.
Each prototype consisted of a lattice structure (first and second images below), with a repeated base unit cell geometry (third image below). They were all designed with nTopology, a software specifically designed to engineer lattice structures.
The samples differed from each other in density, arrangement, base geometry and height, but the material being used, the FormLabs Elastic Resin, remained constant. The FormLabs Form 2 SLA printer was used to create all the prototypes.
There were 5 different optimization stages, with multiple prototypes in each stage. The main issue was being able to design a lattice structure that would support the minimum load requirement of 1.5 kN without failing, and with a maximum deformation within the industry standard numbers of 8 to 12 mm.
Overall, the outcome of the project was successful, as the team gained experience using valuable tools for innovation related to footwear specifically. The data shows promising results: in the first three stages the samples were not meeting the minimum requirements, but in stages 4 and 5, some valuable and significant data was found. The best sample was P18, with an energy return of 81.28%. The Nike ZoomX has an energy return of 87%, so the data shows that it is possible to achieve similar results with new additive manufacturing technologies.
The results are particularly promising considering the fact that the materials and resources used for this project were the most basic and available to the public. Actual companies will have proprietary materials that can allow even better results.
The data of each sample can be found below in the Data section.
Data
* Samples that reached the minimum requirements
Credits
I would like to thank my teammates Matteo Grando and Nolan Kim for helping me in this project, and for the positive results that we found together. I would also like to thank our professors Dr. Thornton and Dr. Hauser for the guidance and support during the whole project.
