Integrating kinematic and dynamic factors with generative design for high-performance additive manufacturing structures

运动学 生成语法 工程制图 计算机科学 工程类 制造工程 人工智能 经典力学 物理
作者
Huaizhi Zong,Bin Lou,Haihui Yuan,Dandan Wang,Yicha Zhang,Jikun Ai,Junhui Zhang,Bing Xu
出处
期刊:Virtual and Physical Prototyping [Taylor & Francis]
卷期号:20 (1) 被引量:13
标识
DOI:10.1080/17452759.2025.2501383
摘要

Mechanical structures often operate under dynamic loads and complex kinematic conditions, but current design methods primarily adopt static or simplified boundary conditions, leading to inaccuracy in defining the design space and the resulting solutions. To address these limitations, this paper presents a novel generative design framework that integrates kinematic and dynamic factors to establish more precise design inputs. To accurately capture the real-world mechanical states of structures, extensive kinematic and dynamic data are extracted through virtual prototyping technology. A novel design domain determination method is proposed based on kinematic relationships, while a load condition extraction approach is formulated based on material deformation theory, collectively ensuring a more realistic representation of design inputs in generative design. Furthermore, a design for additive manufacturing strategy is incorporated to refine and optimise the generative design outcomes, enhancing the structural manufacturability. The proposed method is applied to a challenging case study: designing a hydraulically actuated quadruped robot leg with complex kinematic relationships and varying dynamic load conditions. Experiment results indicate that the proposed method greatly improves structural efficiency and performance, demonstrating its potential in designing complex mechanical structures.
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