3D printers often fail to match the precision of advanced computational designs, leaving materials performing differently than expected. Cambridge-based MIT researchers have developed a method that incorporates printing limitations into the design process, enabling materials to perform more reliably.
“If you don’t account for these limitations, printers can either over- or under-deposit material by quite a lot, so your part becomes heavier or lighter than intended. It can also over- or underestimate the material performance significantly,” explained Josephine Carstensen, Gilbert W. Winslow Associate Professor of Civil and Environmental Engineering. “With our technique, you know what you’re getting in terms of performance because the numerical model and experimental results align very well.”
The approach is detailed in Materials and Design in an open-access paper co-authored by Carstensen and PhD student Hajin Kim-Tackowiak.
A Context-Aware Design Method
As 3D printing has become more precise, so have methods for designing complex material structures. One advanced approach is topology optimization, which can generate structures that sometimes outperform conventional designs, approaching theoretical performance limits. It is used to optimize stiffness, strength, energy absorption, fluid permeability, and more.
However, it often creates designs at scales that 3D printers cannot reliably reproduce. The main issue is print head size: if a design specifies a 0.5 mm layer but the printer extrudes 1 mm, the final part is warped. Another problem is weak bonding between layers, which makes parts prone to separation or failure.
“One of the challenges of topology optimization has been that you need a lot of expertise to get good results, so that once you take the designs off the computer, the materials behave the way you thought they would,” Carstensen said. “We’re trying to make it easy to get these high-fidelity products.”
The researchers aimed to address the gap between expected and actual material performance caused by these limitations. Previously, Carstensen developed an algorithm that factored in nozzle size for beam structures. The team expanded the method to factor in print head orientation, interlayer bonding strength, and complex porous structures. It allows users to model the bead center and weaker bonding zones while automatically optimizing the print path.
The team created 2D designs with varying pore sizes and compared them against traditionally topology-optimized materials. Materials made with the new method deviated less from their intended performance, especially under 70% density, and avoided over-depositing material.
Expanding Materials Through Smarter Design
The authors described it as the first technique to account for both print head size and weak interlayer bonding. “When you design something, you should use as much context as possible,” Kim-Tackowiak said. “It was rewarding to see that putting more context into the design process makes your final materials more accurate. It means there are fewer surprises. Especially when we’re putting so much more computational resources into these designs, it’s nice to see we can correlate what comes out of the computer with what comes out of the production process.”
Future work aims to improve the method for higher densities and materials like cement or ceramics. The approach reduces the need for expert intervention, and the team hopes it will expand the range of usable materials.
Algorithmic Innovation in 3D Printing
MIT’s approach is part of a growing wave of smart algorithmic tools that aim to make 3D printing more precise and efficient.
In 2022, Engineering software developer Hyperganic announced the public launch of Hyperganic Core 3, the firm’s AI-based algorithmic design software. Supported by a $7.8 million funding round, the software platform allows users to design 3D printable parts using algorithmic models, providing an alternative to the traditional component design process. Hyperganic Core streamlines engineering workflows in sectors such as aerospace, enabling complex geometries to be finalized computationally within minutes while ensuring optimized performance.
Elsewhere, the French software start-up Spare Parts 3D (SP3D) unveiled the beta version of Théia, a digital tool that uses AI to automatically convert 2D technical drawings into 3D models. SP3D’s new solution combines its AI-powered DigiPart software and deep learning technology to transform 2D drawings of spare parts into 3D printable models, significantly reducing conversion time from days to minutes.
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Featured image shows 3D printing of complex material structures like airplane wings. Image via MIT; iStock
