As 3D printers have become more affordable and accessible, a growing community of makers, both experienced and novice, has emerged. These individuals rely on free, open-source repositories filled with user-generated 3D models that they can download and create using their 3D printers. However, customizing these models has often been a complex and challenging task, requiring expensive computer-aided design (CAD) software and significant expertise. MIT researchers recognized this challenge and decided to tackle it head-on. They developed Style2Fab, an AI-driven tool designed to simplify the process of adding custom design elements to 3D models. What makes Style2Fab truly remarkable is that it allows users to describe their desired design using natural language prompts, eliminating the need for CAD software and technical expertise. The driving force behind Style2Fab is deep-learning algorithms. These algorithms automatically divide a 3D model into two key segments: aesthetic and functional. The aesthetic segments can be customized, while the functional segments remain unchanged to ensure the object’s proper functionality.

To achieve this, Style2Fab employs machine learning to analyze the model’s topology, identifying segments where changes in geometry occur. These changes, such as curves or angles where two planes connect, help determine what parts of the model are functional. However, because 3D models can vary significantly, these initial recommendations are subject to user validation. Users can easily classify any segment as aesthetic or functional. Once the segmentation is complete, users can describe their desired design using natural language. For instance, a user could request a “rough, multicolor Chinoiserie planter” or a phone case “in the style of Moroccan art.” Style2Fab’s AI system, Text2Mesh, then interprets these prompts to modify the aesthetic segments of the model. It can add texture, adjust color, or alter shape to match the user’s criteria, all while preserving the functional aspects of the object.

Style2Fab’s user interface simplifies the entire process. Users need only a few clicks and input their design preferences to generate a customized 3D model. In a study conducted by MIT, makers of varying expertise levels found Style2Fab valuable. Novices appreciated its ease of use, while experienced users enjoyed the workflow acceleration and advanced customization options it offered. The potential applications of Style2Fab are vast. Beyond enhancing the 3D printing experience for hobbyists and professionals, it could play a significant role in medical making. Personalizing assistive devices by considering both aesthetics and functionality can lead to higher patient compliance. For example, a user could customize the appearance of a thumb splint to match their clothing without affecting its functionality. MIT researchers are continuously improving Style2Fab, with plans to provide fine-grained control over physical properties and geometry. They aim to make it even easier for users to create custom 3D models from scratch. Additionally, a collaboration with Google on a follow-up project is in progress. In a world where customization and accessibility are increasingly vital, Style2Fab is a shining example of how AI can revolutionize 3D printing and empower individuals to bring their unique ideas to life.

By embracing AI and simplifying the 3D printing process, MIT’s Style2Fab is poised to democratize design and manufacturing, opening up a world of possibilities for makers and innovators across various industries.