We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated on standard compliance-minimization and compliant mechanism benchmarks in two and three dimensions. The optimized designs achieve objective values comparable to those obtained with classical Moving Morphable Component (MMC) approaches, but with geometrically consistent, refined boundaries. Numerical examples demonstrate additional advantages of the GET framework, including mesh independence inherent to explicit parameterizations, strong geometric expressiveness, and effective control over smoothness, discreteness, and structural complexity through parameter tuning. As a robust and manufacture-ready approach to explicit topology optimization, GET opens avenues for tackling advanced and complex design problems.

翻译：本文提出了高斯集成拓扑（GET）方法，这是一种面向制造就绪拓扑优化的新型显式框架，其中设计几何通过各向异性高斯函数的叠加进行表征。通过将显式高斯描述与类水平集Heaviside投影相结合，GET能够固有地生成光滑且曲率连续的设计，无需进行网格或角点平滑、特征提取等后处理步骤。该方法在二维和三维的标准柔度最小化与柔顺机构基准问题上进行了验证。优化设计获得的目标函数值与经典移动可变形组件（MMC）方法相当，但具有几何一致且精细的边界。数值算例展示了GET框架的额外优势，包括显式参数化固有的网格无关性、强大的几何表达能力，以及通过参数调节对光滑性、离散性和结构复杂度的有效控制。作为一种鲁棒且制造就绪的显式拓扑优化方法，GET为解决先进复杂的设计问题开辟了新途径。