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.

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.

In product design, designers often create a multitude of concept sketches as part of the ideation and exploration process. Transforming such sketches to 3D digital models requires special expertise due to a lack of intuitive computer aided design (CAD) tools suitable for rapid modeling. Recent advances in sketch-based user interfaces and immersive environments have introduced novel curve design methods that facilitate the transformation of such sketches into 3D digital models. However, rapid surfacing of such data remains an open challenge. Based on the observation that a sparse network of curves is reasonably sufficient to convey the intended geometric shape, we propose a new method for creating approximate surfaces on curve clouds automatically. A notable property of our method is that it relieves many topological and geometric restrictions of 3D conventional networks such as the curves do not need to be connected to one another or gently drawn. Our method calculates a 3D guidance vector field in the space that the curve cloud appears. This guidance vector field helps drive a deformable closed surface onto the curves. During this deformation, surface smoothness is controlled through a set of surface smoothing and subdivision operations. The resulting surface can be further beautified by the user manually using selective surface modification and fairing operations. We demonstrate the effectiveness of our approach on several case examples. Our studies have shown that the proposed technique can be particularly useful for rapid visualization.

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In product design, designers often create a multitude of concept sketches as part of the ideation process. Transforming such sketches to 3D digital models usually require special expertise and effort due to a lack of suitable Computer Aided Design (CAD) tools. Although recent advances in sketch-based user interfaces and immersive environments (such as augmented/virtual reality) have introduced novel curve design tools, rapid surfacing of such data remains an open challenge. To this end, we propose a new method that enables a quick construction of approximate surfaces from a cloud of 3D curves that need not be connected to one another. Our method first calculates a vector field by discretizing the space in which the curve cloud appears into a voxel image. This vector field drives a deformable surface onto the 3D curve cloud thus producing a closed surface. The surface smoothness is achieved through a set of surface smoothing and subdivision operations. Our studies show that the proposed technique can be particularly useful for early visualization and assessment of design ideas.