Asian Scientist published a research article about a method to optimize the design process for 3D printed products, which was led by DManD research fellow Dr Xiong Yi. Original Link: https://www.asianscientist.com/2019/10/tech/3d-printing-data-driven-design-sutd/

Leveraging data, scientists in Singapore have developed a straightforward method to optimize the design process for 3D printed products. They reported their findings in the journal ASME Journal of Mechanical Design.

Additive manufacturing (AM), also known as 3D printing, is a process by which parts are fabricated in a layer-by-layer manner. In conventional manufacturing processes, the main task for designers is tailoring their designs to eliminate manufacturing difficulties and minimize costs.

On the contrary, AM has fewer manufacturing constraints while offering designers with much more design freedom to explore. Therefore, designers must search for optimal design solutions out of millions of design alternatives that are different in geometry, topology, structure and material. This can be a tedious task with current design methods and computer-aided design tools due to the lack of ability to rapidly explore and exploit such a high dimensional design space.

To address this issue, researchers led by Dr. Xiong Yi at the Singapore University of Technology and Design (SUTD) proposed a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process for AM products.

First, they used simple and computationally inexpensive surrogate models in the design exploration process to approximate and replace complex high-fidelity engineering analysis models. This allowed them to rapidly narrow down the high-dimensional design space. Next, they conducted design optimization based on refined surrogate models to obtain a single optimal design.

The researchers then demonstrated the efficiency of their approach in the design of an AM fabricated ankle brace that has tunable mechanical properties for facilitating the recovery of joints. Their data-driven method allowed them to alter the orientation and dimensions of the brace in different areas to achieve desired stiffness distributions.

The team noted that the ankle brace is very soft within the allowable range of motions, which provides comfort to patients. However, once the movement is out of the permissible range, the brace becomes stiff enough to protect the users’ joints from extreme load conditions.

“Previously, it was hard for designers to imagine a design of such complex geometry due to the limitations in conventional manufacturing, but now this design is easily achievable with AM. Our new approach allows designers to embrace the design freedom in AM that comes with the shift in design paradigm, [allowing them to] create more optimal products,” said Xiong.

 

Journal Reference:

  1. Yi Xiong, Pham Luu Trung Duong, Dong Wang, Sang-In Park, Qi Ge, Nagarajan Raghavan, David W. Rosen. Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing. Journal of Mechanical Design, 2019; 141 (10) DOI: 10.1115/1.4043587