The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

Ridges are characteristic curves of a surface that mark salient intrinsic features of its shape and are therefore valuable for shape matching, surface quality control, visualization and various other applications. Ridges are loci of points on a surface where either of the principal curvatures attain a critical value in its respective principal direction. These curves have complex behavior near umbilics on a surface, and may also pass through certain turning points causing added complexity for ridge computation. We present a new algorithm for numerically tracing ridges on B-Spline surfaces that also accurately captures ridge behavior at umbilics and ridge turning points. The algorithm traverses ridge segments by detecting ridge points while advancing and sliding in principal directions on a surface in a novel manner, thereby computing connected curves of ridge points. The output of the algorithm is a set of curve segments, some or all of which, may be selected for other applications such as those mentioned above. The results of our technique are validated by comparison with results from previous research and with a brute-force domain sampling technique.

Additive manufacturing (AM) enables creation of objects with complex internal lattice structures for functional, aesthetic, structural and fabrication considerations. Several approaches for lattice generation and optimization, and their implementations in commercial systems exist. However, these commercial systems are typically independent from a CAD system, and therefore introduces workflow complexities for product lifecycle management. In this paper, we present a unified computer-aided framework for design, computer-aided engineering analysis (CAE) of solids with lattice structures, and freeform topology optimization within the CAD system that enables a seamless workflow. The proposed framework takes as input a solid CAD model and enables rapid generation of different lattice structures as repeated arrangements of lattice template shapes that replace input solid volume. Generated internal patterns are further optimized through freeform modifications to improve structural characteristics of the input model. Lattice modeling and optimization is performed using discrete implicit surface representations for the ease in representing complex topologies and performing modeling and freeform deformation operations. The output of the proposed framework is a polygonal represenatation of the lattified model ready for 3D printing. We have implemented our framework as a plugin to the Siemens PLM NX software system and examples are demonstrated for typical products in aerospace, medical and automotive industries.

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