activity
20202025
most citedRapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

Are Foundation Models Ready for Industrial Defect Recognition? A Reality Check on Real-World Data

Simon Baeuerle, Pratik Khanna, Nils Friederich +4

Foundation Models (FMs) have shown impressive performance on various text and image processing tasks. They can generalize across domains and datasets in a zero-shot setting. This c…

cs.LG2025

Rapid AI-based generation of coverage paths for dispensing applications

Simon Baeuerle, Ian F. Mendonca, Kristof Van Laerhoven +2

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 b…

eess.SY2024

Coverage Path Planning for Thermal Interface Materials

Simon Baeuerle, Andreas Steimer, Ralf Mikut

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…

cs.CV2024

Iterative Cluster Harvesting for Wafer Map Defect Patterns

Alina Pleli, Simon Baeuerle, Michel Janus +3

Unsupervised clustering of wafer map defect patterns is challenging because the appearance of certain defect patterns varies significantly. This includes changing shape, location,…

cs.LG2023

MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis

Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle +3

Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and meth…

cs.LG2022★ 1 cited

Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks

Simon Baeuerle, Marius Gebhardt, Jonas Barth +2

Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooli…

cs.CV2020

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings

Simon Baeuerle, Jonas Barth, Elton Renato Tavares de Menezes +2

Electronic control units (ECUs) are essential for many automobile components, e.g. engine, anti-lock braking system (ABS), steering and airbags. For some products, the 3D pose of e…