用AI快速生成导热材料涂布路径，替代传统耗时优化方法。
Rapid AI-based generation of coverage paths for dispensing applications
- 用神经网络直接输入目标区域，输出涂布路径。
- 无需标注数据，多场景测试均有效且无气泡缺陷。
- 适合自动化制造，可推广至其他实时工艺生成场景。
导热界面材料（TIM）的覆盖路径规划在功率电子和电子控制单元设计中至关重要。目前主要依赖专家手动设计或计算成本高昂的优化方法。本文提出一种新型基于人工智能的方法，用于生成TIM及类似涂布应用的涂布路径，可直接替代现有优化方法。该方法采用人工神经网络（ANN），以目标散热区域为输入，直接输出涂布路径，无需标签数据，并在多个目标区域上验证了其可行性。生成的路径可直接用于自动化生产设备，且不产生气泡。该基于ANN实时预测理想目标状态所需工艺参数的方法，未来有望推广至其他制造流程。
原文摘要 · Abstract (English)
Coverage Path Planning of Thermal Interface Materials (TIM) plays a crucial role in the design of power electronics and electronic control units. Up to now, this is done manually by experts or by using optimization approaches with a high computational effort. We propose a novel AI-based approach to generate dispense paths for TIM and similar dispensing applications. It is a drop-in replacement for optimization-based approaches. An Artificial Neural Network (ANN) receives the target cooling area as input and directly outputs the dispense path. Our proposed setup does not require labels and we show its feasibility on multiple target areas. The resulting dispense paths can be directly transferred to automated manufacturing equipment and do not exhibit air entrapments. The approach of using an ANN to predict process parameters for a desired target state in real-time could potentially be transferred to other manufacturing processes.
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