Rapid AI-based generation of coverage paths for dispensing applications

TL;DR

This paper introduces a rapid AI-based method using an ANN to generate coverage paths for dispensing applications, reducing computational effort and enabling real-time, automated manufacturing without the need for labels.

Contribution

It presents a novel, label-free ANN approach that directly predicts dispense paths from target areas, replacing traditional optimization methods in coverage path planning.

Findings

The ANN approach is feasible on multiple target areas.

Generated paths are directly transferable to manufacturing equipment.

Paths do not exhibit air entrapments.

Abstract

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.