Thermal management of power electronics and Electronic Control Units is
crucial in times of increasing power densities and limited assembly space.
Electric and autonomous vehicles are a prominent application field. Thermal
Interface Materials are used to transfer heat from a semiconductor to a
heatsink. They are applied along a dispense path onto the semiconductor and
spread over its entire surface once the heatsink is joined. To plan this
application path, design engineers typically perform an iterative
trial-and-error procedure of elaborate simulations and manual experiments. We
propose a fully automated optimization approach, which clearly outperforms the
current manual path planning and respects all relevant manufacturing
constraints. An optimum dispense path increases the reliability of the thermal
interface and makes the manufacturing more sustainable by reducing material
waste. We show results on multiple real products from automotive series
production, including an experimental validation on actual series manufacturing
equipment.

优化散布路径的自动化方法可提高热界面的可靠性，减少材料浪费，并在汽车系列生产中实验证明了其成果。

热界面材料的覆盖路径规划

Coverage Path Planning for Thermal Interface Materials

High-end vehicles have been furnished with a number of electronic control
units (ECUs), which provide upgrading functions to enhance the driving
experience. The controller area network (CAN) is a well-known protocol that
connects these ECUs because of its modesty and efficiency. However, the CAN bus
is vulnerable to various types of attacks. Although the intrusion detection
system (IDS) is proposed to address the security problem of the CAN bus, most
previous studies only provide alerts when attacks occur without knowing the
specific type of attack. Moreover, an IDS is designed for a specific car model
due to diverse car manufacturers. In this study, we proposed a novel deep
learning model called supervised contrastive (SupCon) ResNet, which can handle
multiple attack identification on the CAN bus. Furthermore, the model can be
used to improve the performance of a limited-size dataset using a transfer
learning technique. The capability of the proposed model is evaluated on two
real car datasets. When tested with the car hacking dataset, the experiment
results show that the SupCon ResNet model improves the overall false-negative
rates of four types of attack by four times on average, compared to other
models. In addition, the model achieves the highest F1 score at 0.9994 on the
survival dataset by utilizing transfer learning. Finally, the model can adapt
to hardware constraints in terms of memory size and running time.

本文提出了一种名为 SupCon ResNet 的深度学习模型，用于在 CAN 总线上处理多种攻击识别，以及将转移学习技术应用于有限大小数据集的性能改进。 在两个真实的汽车数据集上评估了该模型的能力，结果显示该模型比其他模型平均将四种攻击的整体假阴性率提高了四倍。 此外，该模型通过使用转移学习，在存储限制和运行时间方面进行了改进。