AI 中文总结
本研究针对选择性激光烧结，利用AI智能体系统，结合过往成型件知识与少量用户指导，在少量迭代内优化3种材料的工艺参数，达到指定力学性能，实现相关复杂任务的智能自动化。
AI 中文摘要
智能体系统可实现复杂工作流的智能自动化，在增材制造领域，其适用于力学性能相关的工艺参数优化等复杂任务。本研究针对选择性激光烧结（SLS），探究了AI驱动的智能体工艺优化方法，旨在迭代提升Inova Mk1设备上3种材料的拉伸与弯曲性能，这3种材料包括PA12 GF、PA11 Onyx以及PA12 Blend（由25% PA12 GF与75% PA12 White组成的体积混合物）。研究利用之前成型件的知识，仅需用户少量指导，智能体系统就能在少量迭代内优化工艺参数，达到与技术数据说明书（TDS）规定相当的力学性能。本研究展示了智能体系统从更新数据中持续学习的能力，可实现选择性激光烧结工艺参数优化等复杂任务的智能自动化。
英文摘要
Agentic systems enable the intelligent automation of complex workflows, specific to additive manufacturing this is applicable for complex tasks such as process parameter optimization for mechanical properties. This work investigates the AI enabled agentic process optimization within Selective Laser Sintering (SLS) to iteratively improve the tensile and flexural properties of 3 different materials on the Inova Mk1. These materials include PA12 GF, PA11 Onyx, and PA12 Blend (volume mixture of 25% PA12 GF and 75% PA12 White) and with using knowledge from previous builds and minimal guidance from the user, the agentic system was able to optimize process parameters over a small number of iterations to achieve comparable TDS specified mechanical properties. This work showcases the ability for an agentic system to continually learn from updated data, enabling the intelligent automation of complex tasks such as process parameter optimization for selective laser sintering.
