Generative Design (GD) has evolved as a transformative design approach,
employing advanced algorithms and AI to create diverse and innovative solutions
beyond traditional constraints. Despite its success, GD faces significant
challenges regarding the manufacturability of complex designs, often
necessitating extensive manual modifications due to limitations in standard
manufacturing processes and the reliance on additive manufacturing, which is
not ideal for mass production. Our research introduces an innovative framework
addressing these manufacturability concerns by integrating constraints
pertinent to die casting and injection molding into GD, through the utilization
of 2D depth images. This method simplifies intricate 3D geometries into
manufacturable profiles, removing unfeasible features such as
non-manufacturable overhangs and allowing for the direct consideration of
essential manufacturing aspects like thickness and rib design. Consequently,
designs previously unsuitable for mass production are transformed into viable
solutions. We further enhance this approach by adopting an advanced 2D
generative model, which offer a more efficient alternative to traditional 3D
shape generation methods. Our results substantiate the efficacy of this
framework, demonstrating the production of innovative, and, importantly,
manufacturable designs. This shift towards integrating practical manufacturing
considerations into GD represents a pivotal advancement, transitioning from
purely inspirational concepts to actionable, production-ready solutions. Our
findings underscore usefulness and potential of GD for broader industry
adoption, marking a significant step forward in aligning GD with the demands of
manufacturing challenges.

我们的研究通过将压铸和注塑成型相关的约束条件与 2D 深度图像的利用相结合，引入了一个创新的框架来解决 Generative Design 在制造可行性方面面临的挑战，并通过采用先进的 2D 生成模型进一步增强了这种方法，通过将实际制造考虑因素整合到 Generative Design 中，为广泛行业采用提供了有用且有潜力的解决方案。

深度生成设计用于大规模生产

Deep Generative Design for Mass Production

Short-fiber-reinforced composites (SFRC) are high-performance engineering
materials for lightweight structural applications in the automotive and
electronics industries. Typically, SFRC structures are manufactured by
injection molding, which induces heterogeneous microstructures, and the
resulting nonlinear anisotropic behaviors are challenging to predict by
conventional micromechanical analyses. In this work, we present a machine
learning-based multiscale method by integrating injection molding-induced
microstructures, material homogenization, and Deep Material Network (DMN) in
the finite element simulation software LS-DYNA for structural analysis of SFRC.
DMN is a physics-embedded machine learning model that learns the microscale
material morphologies hidden in representative volume elements of composites
through offline training. By coupling DMN with finite elements, we have
developed a highly accurate and efficient data-driven approach, which predicts
nonlinear behaviors of composite materials and structures at a computational
speed orders-of-magnitude faster than the high-fidelity direct numerical
simulation. To model industrial-scale SFRC products, transfer learning is
utilized to generate a unified DMN database, which effectively captures the
effects of injection molding-induced fiber orientations and volume fractions on
the overall composite properties. Numerical examples are presented to
demonstrate the promising performance of this LS-DYNA machine learning-based
multiscale method for SFRC modeling.

本研究提出了一种基于机器学习的多尺度方法，将注射成型引起的微观结构，材料均质化和深度材料网络（DMN）与有限元模拟软件 LS-DYNA 相结合，用于短纤维增强复合材料的结构分析。通过将 DMN 与有限元耦合，我们开发了一种高度准确和高效的数据驱动方法，该方法以比高保真度直接数值模拟快若干个数量级的计算速度预测复合材料和结构的非线性行为。