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首页> 外文期刊>International journal of mechanics and materials in design >Topology optimization parallel-computing framework based on the inherent strain method for support structure design in laser powder-bed fusion additive manufacturing
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Topology optimization parallel-computing framework based on the inherent strain method for support structure design in laser powder-bed fusion additive manufacturing

机译：基于激光粉末融合添加剂制造的拓扑优化平行计算框架

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In this work, a topology optimization parallel-computing framework is developed to design support structures for minimizing deflections in Laser Powder-bed Fusion produced parts. The parallel-computing framework consists of a topology optimization model and an Inherent Strain Method (ISM) model. The proposed framework is used to design stiffer support structures to reduce the before and after-cutting deflections in printed cantilevers. Gravity load and residual stresses calculated from ISM are applied in the topology optimization model. The optimized results were printed and analyzed for validating the effectiveness of the proposed model. Experimental results show that the optimized supports can achieve over 60% reduction in part deflection as well as over 50% material usage reduction compared to the default support structure. In addition, ISM also was used to predict the part deflections and shows good agreement (average error of 6%) between the experimental and simulated results. Lastly, the multi-node parallelization of the proposed framework showed - 5 times speedup compared to a single-node implementation.
机译：在这项工作中，开发了一种拓扑优化并行计算框架，以设计支持结构，以最大限度地减少激光粉床融合产生的零件中的偏转。并行计算框架由拓扑优化模型和固有的应变方法（ISM）模型组成。所提出的框架用于设计冷却支持结构，以减少印刷悬臂中的前后偏转。由ISM计算的重力负荷和残余应力应用于拓扑优化模型。打印并分析优化的结果，以验证所提出的模型的有效性。实验结果表明，与默认支持结构相比，优化的支撑件可以达到零件挠度的减少超过60％，而不是超过50％的材料使用量。此外，ISM还用于预测零件偏转，并在实验和模拟结果之间显示良好的一致性（平均误差为6％）。最后，所提出的框架的多节点并行化显示 - 与单节点实现相比，加速5倍。

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