Manufacturing technology is advancing at an unprecedented rate as it become increasingly digital. Taking advantage of new manufacturing technology is critical for maintaining national security and prosperity, but even dedicated experts and leading-edge companies struggle to keep pace with manufacturing's rapid advancement. This makes it difficult for engineers to learn about the latest manufacturing technology and design products that take full advantage of new fabrication processes as they become available. Fortunately, many new manufacturing technologies rely on digital models that produce abundant design data to which machine learning can be applied to derive design knowledge. While design and manufacturing datasets may be useful for that reason, they are also highly variable in both number and quality of solutions. This work investigates how the size and quality of these datasets relate to the accuracy and usefulness of machine learning insights, and how this impacts the support provided to engineering designers. In this work, we focus on additive manufacturing (also called "3D printing") as a representative digital manufacturing technology that is rapidly evolving and growing, and which is projected to contribute substantially to the nation?s future manufacturing portfolio. Studies conducted with engineering students as part of this work will be used to provide skill training as well as collect data, helping prepare them for the manufacturing workforce.
The research will combine machine learning, additive manufacturing, and explainable artificial intelligence to evaluate the use of automated design feedback derived from existing crowdsourced additive manufacturing design challenges. First, part designs will be mined from open, online repositories as well as through curated repositories established in this work via in-class design challenges. Next, a machine learning pipeline will be implemented to extract design patterns from curated digital repositories. This will make it possible to test the effect of repository size on the accuracy of design feedback and of repository size on the granularity of feedback. Finally, a user validation study will be conducted in which students will undertake a design task specific to additive manufacturing technology. Feedback with varying characteristics will be provided to some participants by extending the machine learning pipeline developed previously with explainable capabilities. Specific technical deliverables will include (1) a novel dataset of voxelized part designs, (2) a deeper understanding of the impact of repository size and quality on usefulness of machine-generated feedback, and (3) empirical evidence of the impact of real-time additive manufacturing feedback on the solutions generated by engineering designers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
