Digital Engineering Lab

Additive manufacturing (AM) enables the fabrication of complex geometries that are not possible with traditional manufacturing methods. However, the design of parts for AM is often constrained by the need for support structures to prevent overhangs from collapsing during fabrication. In this paper, we present a topology optimization framework that incorporates support structure constraints to generate designs that are manufacturable using AM. The proposed method uses a density-based approach to optimize the material distribution in the design domain while ensuring that the resulting part can be fabricated without requiring excessive support structures. We demonstrate the effectiveness of our approach on several benchmark problems and show that it can produce designs with significantly reduced support requirements compared to traditional methods.

Additive manufacturing (AM) is emerging as a promising technology to fabricate cost-effective, customized functional parts. Designing such functional, i.e., load bearing, parts can be challenging and time consuming where the goal is to balance performance and material usage. Topology optimization (TO) is a powerful design method which can complement AM by automating the design process. However, for TO to be a useful methodology, the underlying mathematical model must be carefully constructed. Specifically, it is well established that parts fabricated through some AM technologies, such as fused deposition modeling (FDM), exhibit behavioral anisotropicity. This induced anisotropy can have a negative impact on functionality of the part, and must be considered. To the best of our knowledge, a robust TO method to handle anisotropy has not been proposed. In the present work, a strength-based topology optimization method for structures with anisotropic materials is presented. More specifically, we propose a new topological sensitivity formulation based on strength ratio of non-homogeneous failure criteria, such as Tsai-Wu. Implementation details are discussed throughout the paper, and the effectiveness of the proposed method is demonstrated through numerical and experimental tests.

In this paper, we present a topology optimization (TO) framework to enable automated design of mechanical components while ensuring the result can be manufactured using multi-axis machining. Although TO improves the part’s performance, the as-designed model is often geometrically too complex to be machined and the as-manufactured model can significantly vary due to machining constraints that are not accounted for during TO. In other words, many of the optimized design features cannot be accessed by a machine tool without colliding with the part (or fixtures). The subsequent post-processing to make the part machinable with the given setup requires trial-and-error without guarantees on preserving the optimized performance. Our proposed approach is based on the well-established accessibility analysis formulation using convolutions in configuration space that is extensively used in spatial planning and robotics. We define an inaccessibility measure field (IMF) over the design domain to identify non-manufacturable features and quantify their contribution to non-manufacturability. The IMF is used to penalize the sensitivity field of performance objectives and constraints to prevent formation of inaccessible regions. Unlike existing discrete formulations, our IMF provides a continuous spatial field that is desirable for TO convergence. Our approach applies to arbitrary geometric complexity of the part, tools, and fixtures, and is highly parallelizable on multi-core architecture. We demonstrate the effectiveness of our framework on benchmark and realistic examples in 2D and 3D. We also show that it is possible to directly construct manufacturing plans for the optimized designs based on the accessibility information.

In this paper, we present a topology optimization (TO) framework to simultaneously optimize the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced composites (FRC). Current approaches in density-based TO for FRC use the underlying finite element mesh both for analysis and design representation. This poses several limitations while enforcing sub-element fiber spacing and generating high-resolution continuous fibers. In contrast, we propose a mesh-independent representation based on a neural network (NN) both to capture the matrix topology and fiber distribution. The implicit NN-based representation enables geometric and material queries at a higher resolution than a mesh discretization. This leads to the accurate extraction of functionally-graded continuous fibers. Further, by integrating the finite element simulations into the NN computational framework, we can leverage automatic differentiation for end-to-end automated sensitivity analysis, i.e., we no longer need to manually derive cumbersome sensitivity expressions. We demonstrate the effectiveness and computational efficiency of the proposed method through several numerical examples involving various objective functions. We also show that the optimized continuous fiber reinforced composites can be directly fabricated at high resolution using additive manufacturing.

In this paper, we propose PATO—a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing (AM), while ensuring manufacturability with respect to cracking. Specifically, parts fabricated through Laser Powder Bed Fusion (LPBF) are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. Maturing the design for such parts and planning their fabrication can span months to years, often involving multiple handoffs between design and manufacturing engineers. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free during optimization, producibility is explicitly encoded within the standard formulation of TO, using a crack index. Multiple crack indices are explored and using experimental validation, maximum shear strain index (MSSI) is shown to be an accurate crack index. Simulating the build process, in order to estimate MSSI, is a coupled, multi-physics, time-complex computation and incorporating it in the TO loop can be computationally prohibitive. We leverage the current advances in deep convolutional neural networks (DCNN) and present a high-fidelity surrogate model based on an Attention-based U-Net architecture to predict the MSSI values as a spatially varying field over the part’s domain. Further, we employ automatic differentiation to directly compute the gradient of maximum MSSI with respect to the input design variables and augment it with the performance-based sensitivity field to optimize the design while considering the trade-off between weight, manufacturability, and functionality. We demonstrate the effectiveness of the proposed method through benchmark studies in 3D as well as experimental validation.