While geometric deep learning and computational geometry are making strides in fields like drug design, their potential in manufacturing automation is just beginning to be explored. Our research is a step toward integrating these cutting-edge techniques into industrial settings, particularly in automating the complex assembly of electrical components. By adapting methods typically used for non-Euclidean data in biological systems, we are opening new frontiers in how machines can understand and manipulate the physical world of manufacturing (Bründl et al., 2023).
Semantic Part Segmentation: From CAD Models to Real-World Application
At the heart of our research lies the use of CAD models, central to modern design and manufacturing. These models contain detailed geometric data, offering rich information about the shape, size, and configuration of components. However, the lack of sufficient labeled data for training complex machine learning models poses a significant challenge. To address the lack of labeled data, we aggregated a dataset consisting of 234 triangle meshes, each labeled at the vertex level (Scheffler & Bründl, 2023). This dataset is specifically designed to support segmentation tasks, making it a key resource for training and validating our geometric deep learning models. By providing high-quality labeled data, we ensure that our models can achieve precise part segmentation and generalize well to different scenarios.
To ensure that our labels meet high standards of accuracy, we evaluated them through interrater reliability measures, providing an estimate of human-level performance (Scheffler et al., 2024). This evaluation is crucial, as it ensures that our automated systems can achieve performance levels comparable to expert human workers, further supporting the transition to fully or partially automated assembly lines.
Beyond this labeled dataset, we have accumulated over 46,000 additional unlabeled triangle meshes. These unlabeled models present an exciting opportunity for future research, particularly in leveraging semi-supervised learning techniques. As these approaches mature, these vast amounts of unlabeled data could be utilized to further refine semantic part segmentation models, potentially improving accuracy and efficiency with less reliance on fully labeled datasets.
Predictions from diffusion net for semantic part segmentation of triangle mesh.
Our approach, rooted in geometric deep learning, enables automated systems to accurately segment components. But the real test of our method is the application to real-life components, moving beyond simulated models to practical applications. This success highlights the robustness of our system in real-world manufacturing environments, where accurate part segmentation is critical for automation.
Enabling Advanced Automation
By accurately recognizing and differentiating between various parts, our research allows automated systems to determine the precise vectors needed for actions like inserting cables into electrical components. This requires a high level of precision and adaptability to accommodate the variability in real-world components and assembly processes. The level of accuracy was successfully measured using the newly introduced Spherical Boundary Score (https://dx.doi.org/10.2139/ssrn.4871779).
This capability marks a significant breakthrough in manufacturing automation. Not only does it enhance the efficiency of fully automated systems, but it also paves the way for the development of worker assistance systems. These systems can provide real-time guidance to human operators during manual assembly tasks, using laser-based information displays to streamline processes that once relied heavily on human precision and decision-making. By overlaying visual instructions onto physical components, such systems help ensure accuracy and reduce the risk of errors, transforming the role of the human worker from manual labor to one of oversight and management of the automated process (Bründl et al., 2024).
Laser-based assistance system based on geometric information.
These laser-guided systems are informed by our segmentation results, allowing workers to follow precise instructions during the assembly process which is particularly valuable in environments where full automation is not yet feasible, ensuring that productivity and quality remain high while reducing the cognitive and physical load on workers. As manufacturing continues to evolve, such technologies represent the next step in integrating advanced automation into complex, real-world assembly processes.
Looking to the Future: Advancing Beyond Current Capabilities
While our current approach achieves instantiation through conventional clustering algorithms, true instance segmentation for non-Euclidean data, such as the geometric structures we work with, remains an open challenge. Unlike in image processing, where instance segmentation is more developed, the application of similar techniques to graph-like or non-Euclidean data is still in its infancy. Our research highlights this gap, and part of our ongoing work involves investigating how concepts from image processing—such as heatmap regression—might be adapted to the domain of geometric deep learning.
Heatmap regression offers a promising approach for identifying individual instances within a complex mesh. By borrowing from these techniques and adapting them to handle non-Euclidean data, we aim to push the boundaries of what’s currently possible in automated segmentation. This exploration forms the basis of our future research, where we seek to refine our models to handle more intricate segmentation tasks, beyond the capabilities of existing clustering algorithms.
Moreover, advancing beyond traditional methods, we are exploring new ways of integrating geometric and topological information to improve segmentation. The challenge lies in translating the success seen in 2D image spaces into 3D geometric spaces, where data is inherently more complex and interconnected. By pursuing these paths, we hope to bridge the gap between theoretical advancements and practical applications, bringing a new level of sophistication to manufacturing automation. Our ongoing research in this area reflects our commitment to pushing the limits of geometric deep learning and computational geometry, and we are excited to see how these advancements can further transform the landscape of industrial automation.
A Vision for Geometry-Driven Automation
Our ultimate vision is to create an information retrieval system based on geometry, where automated systems can extract assembly and disassembly instructions directly from the shapes and configurations of components. This would revolutionize not only the assembly and dismantling process but also maintenance and repair operations to a single, geometry-driven system. As the fields of geometric deep learning and computational geometry continue to evolve, their impact on manufacturing automation will only grow. Our research represents a crucial step forward, laying the groundwork for future innovations that will reshape the industry.
Author Contributions:
Written by B. Scheffler and P. Bründl.
References:Â
Bründl, P., Scheffler, B., Stoidner, M., Nguyen, H., Baechler, A., Abrass, A., & Franke, J. (2023). Semantic part segmentation of spatial features via geometric deep learning for automated control cabinet assembly. Journal of Intelligent Manufacturing. Advance online publication.
Scheffler, B., & Bründl, P. (2023). Electrical and Electronic Components Dataset.Â
Scheffler, B., Bründl, P., Nguyen, H. G., Stoidner, M., & Franke, J. (2024). A Dataset of Electrical Components for Mesh Segmentation and Computational Geometry Research. Scientific Data, 11(1), 309.
Bründl, P., Scheffler, B., Straub, C., Nguyen, H. G., Stoidner, M., & Franke, J. (2024). Geometric Deep Learning as an Enabler for Data Consistency and Interoperability in Manufacturing. Â