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Abstract Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an alternative offers fast inference, its performance is dependently bounded by the need for abundant training data. Representation learning, particularly through pre-training, offers promise for few-shot learning, aiding in manufacturability tasks where data can be limited. This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The pretrained model is evaluated across four manufacturability indicators obtained from CAM simulations: subtractive machining (SM) time, additive manufacturing (AM) time, residual von Mises stress, and blade collisions during Laser Power Bed Fusion process. Across all case studies, the model pre-trained by VIRL shows substantial enhancements in generalizability, as measured by R2 regression results, with improved performance on limited data and superior predictive accuracy with larger datasets. Regarding deployment strategy, case-specific phenomenon exists where finetuning VIRL-pretrained models adversely affects AM tasks with limited data but benefits SM time prediction. Moreover, the efficacy of Low-rank adaptation (LoRA), which balances between probing and finetuning, is explored. LoRA shows stable performance akin to probing with limited data, while achieving a higher upper bound than probing as data size increases, without the computational costs of finetuning. Furthermore, static normalization of manufacturing indicators consistently performs well across tasks, while dynamic normalization enhances performance when a reliable task dependent input is available. 

Abstract Recent advances in computer-aided design tools have helped rapidly advance the development of wireframe DNA origami nanostructures. Specifically, automated tools now exist that can convert an input polyhedral mesh into a DNA origami nanostructure, greatly reducing the design difficulty for wireframe DNA origami nanostructures. However, one limitation of these automated tools is that they require a designer to fully conceptualize their intended nanostructure, which may be limited by their own preconceptions. Here, a generative design framework is introduced capable of generating many wireframe DNA origami nanostructures without the need for a predefined mesh. User-defined objectives that guide the generative process are input as either single- or multi-objective optimization problems. A graph grammar is used to both contextualize physical properties of the DNA nanostructure and control the types of generated design features. This framework allows a designer to explore upon and ideate among many generated nanostructures that comply with their own unique constraints. A web-based graphical user interface is provided, allowing users to compare various generated solutions side by side in an interactive environment. Overall, this work illustrates how a constrained generative design framework can be implemented as an assistive tool in exploring design-feature trade-offs of wireframe DNA nanostructures, resulting in novel wireframe nanostructures. 

Abstract Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education. 

Abstract As machine learning is used to make strides in medical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macroscale vascular formations, for producing assessments of medical conditions with relatively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a recursive subgraph mining algorithm. The distribution of rule instances in the data from which they are induced is then used as an intermediary representation enabling common classification and anomaly detection approaches to identify indicative rules with relatively small data sets. The method is applied to seven-tesla time-of-flight angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reliably. This application demonstrates the power of automated structured intermediary representations for assessing nuanced biological form relationships, and the strength of shape grammars, in particular for identifying indicative patterns in complex vascular networks.