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Patrik Hoffmann

Phone: +41 58 765 6262

e-mail:


Address: Empa – Swiss Federal Laboratories for Material Science & Technology, Switzerland

Location: Empa map or google map

Image Processing

Algorithms as ImageJ Plugins

  • Tribology: An ImageJ Plugin for surface topography analysis of textured surfaces
  • Line defect: An ImageJ Plugin for line defect analysis of grinded surfaces

Supplementary data of scientific publication

Ball-on-disk vacuum tribometer with in situ of the wear measurement by DHM

3D reconstruction of cracks propagation in mechanical workpieces using AE

Laser welding quality monitoring via graph SVM with data adaptive kernel

Differentiation of materials and laser powder bed fusion processing regimes
from airborne acoustic emission combined with machine learning

In situ quality monitoring in direct energy deposition process using co-axial
process zone imaging and deep contrastive learning

Deep transfer learning of additive manufacturing mechanisms across materials
in metal-based laser powder bed fusion process

Deep learning-based monitoring of laser powder bed fusion process on varia-
ble time-scales using heterogeneous sensing and operando X-ray radiography
Guidance

Thermal modelling and experimental validation in the perspective of tool steel
laser polishing

Semi-supervised Monitoring of Laser powder bed fusion process based on acoustic
emissions

Long short-term memory based semi-supervised encoder—decoder for early
prediction of failures in self-lubricating bearings

Monitoring of direct energy deposition process using deep-net based manifold
learning and co-axial melt pool imaging

Self-Supervised Bayesian Representation Learning of Acoustic Emissions from
Laser Powder Bed Fusion Process for In-situ Monitoring

Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy
Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning
Framework

Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process
Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering
acoustic emission and sensor sensitivity with explainable machine learning

Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer
Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic
Emission and Machine Learning