Additively manufactured objects often exhibit directional dependencies in their structure due to the layered nature of the printing process. While this dependency has a significant impact on the object's functional performance, the problem of finding the best build orientation to maximize structural robustness remains largely unsolved. We introduce an optimization algorithm that addresses this issue by identifying the build orientation that maximizes the factor of safety (FS) of an input object under prescribed loading and boundary configurations. First, we conduct a minimal number of physical experiments to characterize the anisotropic material properties. Next, we use a surrogate-based optimization method to determine the build orientation that maximizes the minimum factor safety. The surrogate-based optimization starts with a small number of finite element (FE) solutions corresponding to different build orientations. The initial solutions are progressively improved with the addition of new solutions until the optimum orientation is computed. We demonstrate our method with physical experiments on various test models from different categories. We evaluate the advantages and limitations of our method by comparing the failure characteristics of parts printed in different orientations.

Fig. 1. We present a method for lightweight structure design for scenarios where external forces may contact an object at a multitude of locations that are unknown a priori. For a given surface mesh (grey), we design the interior material distribution such that the final object can withstand all external force combinations capped by a budget. The red volume represents the carved out material, while the remaining solid is shown in clear. Notice the dark material concentration on the fragile regions of the optimum result in the backlit image. The cut-out shows the corresponding interior structure of the 3D printed optimum.We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force con guration capped by the budget. Our approach works by repeatedly nding the most critical force con guration and altering the internal structure accordingly. A key issue, however, is that the critical force con guration changes as the structure evolves, resulting in a signi cant computational challenge. To address this, we propose an e cient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests.

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Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part's structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part's structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling (PBCM) in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from industrial MAM operations. The approach is demonstrated on various 3D geometries for the electron beam melting (EBM) process with Ti64 material. Concurrent optimization of the part structures and EBM process variables is compared to sequential optimization, and to optimization of the structure alone. The results indicate that, once process variables are considered concurrently, more cost effective results can be obtained with similar amount of material through a combination of (1) building high stress regions with lower power values to obtain larger yield strength and (2) increasing the power elsewhere to reduce the number of passes required, thereby reducing build time. In our case studies, concurrent optimization of the part's structure and MAM process parameters lead to up to 15% lower estimated total production costs and 21% faster build time than optimizing the part's structure alone.

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.