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.
View on arXiv@article{baeuerle2025_2505.03560,
title={ Rapid AI-based generation of coverage paths for dispensing applications },
author={ Simon Baeuerle and Ian F. Mendonca and Kristof Van Laerhoven and Ralf Mikut and Andreas Steimer },
journal={arXiv preprint arXiv:2505.03560},
year={ 2025 }
}