Abstracts:Cohesive finite element simulation is a mechanics-based computational approach that can be used to model the pull-up process in bottom-up stereo-lithography (SLA) system to significantly increase the reliability and through-put of the bottom-up SLA process. This modeling relates the pull-up velocity and separation of the fabricated part during the pull-up process. However, finite element (FE) simulation of the pull-up process for the individual part is computationally very expensive, time-consuming, and not amenable to online monitoring. This paper outlines a computationally efficient data-driven scheme to predict the separation stress distribution in bottom-up SLA process. The proposed scheme relies on 2D shape context descriptor, neural network (NN), and a limited number of offline FE simulations. Towards this end, FE models and results for the cross-section of $n$-fold symmetric shapes form our databases. The 2D shape context descriptor represents different shapes through log-polar histograms in our database. A backpropagation (BP) neural network is trained using the log-polar histograms of the geometric shapes as inputs and the FE simulated stress distributions as outputs. The trained NN can then be used to predict the separation stress distribution of a new shape. The results demonstrate that the proposed data-driven method can drastically reduce computational costs and apply to any general databases. The comparison between the predicted results by the data-driven approach and the simulated FE results on new shapes verify the validity of the proposed method.

Abstracts:This paper presents a new computational framework for 3D graphic statics based on the concept of disjointed force polyhedra. At the core of this framework are the Extended Gaussian Image and area-pursuit algorithms, which allow more precise control of the face areas of force polyhedra, and consequently of the magnitudes and distributions of the forces within the structure. The explicit control of the polyhedral face areas enables designers to implement more quantitative, force-driven constraints and it expands the range of 3D graphic statics applications beyond just shape explorations. The significance and potential of this new computational approach to 3D graphic statics is demonstrated through numerous examples, which illustrate how the disjointed force polyhedra enable force-driven design explorations of new structural typologies that were simply not realisable with previous implementations of 3D graphic statics.

Abstracts:Reverse engineering of 3D industrial objects such as automobiles and electric appliances is typically performed by fitting B-spline surfaces to scanned point cloud data with a fairing term to ensure smoothness, which often smooths out sharp features. This paper proposes a radically different approach to constructing fair B-spline surfaces, which consists of fitting a surface without a fairing term to capture sharp edges, smoothing the normal field of the constructed surface with feature preservation, and reconstructing the B-spline surface from the smoothed normal field. The core of our method is an image processing based feature-preserving normal field fairing technique. This is inspired by the success of many recent research works on the use of normal field for reconstructing mesh models, and makes use of the impressive simplicity and effectiveness of bilateral-like filtering for image denoising. In particular, our approach adaptively partitions the B-spline surface into a set of segments such that each segment has approximately uniform parameterization, generates an image from each segment in the parameter space whose pixel values are the normal vectors of the surface, and then applies a bilateral filter in the parameter domain to fair the normal field. As a result, our approach inherits the advantages of image bilateral filtering techniques and is able to effectively smooth B-spline surfaces with feature preservation as demonstrated by various examples.