Automated discrimination of surface imperfections and adhered particles on customer-specific optical elements
-
Alexander Schöch
Dr. Alexander Schöch received his BSc in computer engineering and his MSc in industrial engineering from the NTB where he worked on cryptoanalysis with FPGAs and highly parallelized software frameworks respectively. He received his PhD degree from the University of Padua for his work on the topic of metrology at elevated temperature and industrial process control. Currently, he is employed as a research associate at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB. His interests include computer graphics, computer vision and machine learning technology.
,
Patric Perez
Patric Perez received a BSc in systems engineering with specialisation in computer engineering from the NTB. He created an interactive android teaching game for electrostatics and magnetism on bachelor level. Since his graduation he is employed at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB as research associate. His main activities are planning and realisation of machine vision setups and subsequent image processing as applied research.
Sabine Linz-Dittrich completed an apprenticeship as an optician at the company Zeiss in Jena. Afterwards she studied Optical Hardware and Spectroscopy Engineering at the institute LITMO in St. Peterbursburg, Russia. She completed her physics diploma thesis at the TU Berlin with the focus on thin film solar cells. During eleven years she was employed at Balzers Optics in Liechtenstein, there she gained experience as a development engineer in optical coatings as well as leading a production line of optical components. Since 2010 she is employed as a research associate at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB.
Prof. Dr. Carlo Bach received his diploma and his PhD in computer science from ETH Zurich. After several years in industry working in the field of artificial intelligence he joined the university of applied sciences NTB where he teaches computer science and image processing courses. Mr. Bach leads the machine vision group of the Institute for Production Metrology, Materials and Technical Optics (PWO). He is interested in all kinds of surface inspection with 2D and 3D methods.
Carsten Ziolek finished his studies in physics at the University of Hanover in 1997 with a work on wavelength selection and Q-switching of erbium lasers. During his doctorate at the Laser Centre Hanover he worked on the laser dynamics of these systems. He earned his PhD in 2000 with a work on particular Erbium-based infrared-lasers for ophthalmology applications. From 2001 until 2015 he worked for the company TRUMPF in different positions, finally for more than 10 years as Head R&D of TRUMPF’s marking lasers. In 2015 he was appointed as professor at the NTB. There he leads the Institute for Production Metrology, Materials and Optics as well as its technical optics and photonics field of competence. Carsten Ziolek is author of multiple technical publications and talks and is also involved in numerous patents.
Abstract
In previous work, we proposed an automated system, capable of detecting surface defects (e. g. edge chips, digs, scratches) on optical components as defined in the international standard ISO 10110-7. It objectively discriminates between defect classes and quantifies their geometrical size. During assessment of quality control at multiple manufacturers, the need for a method to discriminate between dust particles and surface imperfections has been identified. This article describes an approach to automatically assess dust particles and imperfections on the surface of interest based on the stereo vision approach.
Zusammenfassung
In früheren Arbeiten haben wir ein automatisiertes System vorgestellt, das in der Lage ist, Oberflächeunvollkommenheiten (z. B. Randaussprünge, Löcher, Kratzer) an optischen Komponenten zu erkennen, wie sie in der Norm ISO 10110-7 definiert sind. Das System unterscheidet objektiv zwischen Fehlerklassen und quantifiziert deren geometrische Größe. Während der Bewertung der Qualitätskontrolle von mehreren Herstellern hat sich die Notwendigkeit für eine Methode zur Unterscheidung von Staubpartikeln und Oberflächenfehlern herausgestellt. Dieser Artikel beschreibt einen Stereo basierten Ansatz zur automatischen Unterscheidung zwischen Staubpartikeln und Unvollkommenheiten auf zu prüfenden Oberfläche.
Funding statement: This work was supported by Innosuisse (KTI 18084.1 PFNM-NM: SurfInspect: Automatisierte Oberflächeninspektion von planen und sphärischen optischen Komponenten).
About the authors
Dr. Alexander Schöch received his BSc in computer engineering and his MSc in industrial engineering from the NTB where he worked on cryptoanalysis with FPGAs and highly parallelized software frameworks respectively. He received his PhD degree from the University of Padua for his work on the topic of metrology at elevated temperature and industrial process control. Currently, he is employed as a research associate at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB. His interests include computer graphics, computer vision and machine learning technology.
Patric Perez received a BSc in systems engineering with specialisation in computer engineering from the NTB. He created an interactive android teaching game for electrostatics and magnetism on bachelor level. Since his graduation he is employed at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB as research associate. His main activities are planning and realisation of machine vision setups and subsequent image processing as applied research.
Sabine Linz-Dittrich completed an apprenticeship as an optician at the company Zeiss in Jena. Afterwards she studied Optical Hardware and Spectroscopy Engineering at the institute LITMO in St. Peterbursburg, Russia. She completed her physics diploma thesis at the TU Berlin with the focus on thin film solar cells. During eleven years she was employed at Balzers Optics in Liechtenstein, there she gained experience as a development engineer in optical coatings as well as leading a production line of optical components. Since 2010 she is employed as a research associate at the Institute for Production Metrology, Materials and Technical Optics (PWO) of NTB.
Prof. Dr. Carlo Bach received his diploma and his PhD in computer science from ETH Zurich. After several years in industry working in the field of artificial intelligence he joined the university of applied sciences NTB where he teaches computer science and image processing courses. Mr. Bach leads the machine vision group of the Institute for Production Metrology, Materials and Technical Optics (PWO). He is interested in all kinds of surface inspection with 2D and 3D methods.
Carsten Ziolek finished his studies in physics at the University of Hanover in 1997 with a work on wavelength selection and Q-switching of erbium lasers. During his doctorate at the Laser Centre Hanover he worked on the laser dynamics of these systems. He earned his PhD in 2000 with a work on particular Erbium-based infrared-lasers for ophthalmology applications. From 2001 until 2015 he worked for the company TRUMPF in different positions, finally for more than 10 years as Head R&D of TRUMPF’s marking lasers. In 2015 he was appointed as professor at the NTB. There he leads the Institute for Production Metrology, Materials and Optics as well as its technical optics and photonics field of competence. Carsten Ziolek is author of multiple technical publications and talks and is also involved in numerous patents.
References
1. Alexander Schöch, Patric Perez, Sabine Linz-Dittrich, Carlo Bach and Carsten Ziolek. Automated surface inspection of small customer-specific optical elements. In Michael Heizmann, Thomas Laengle and Fernando Puente León, editors, Forum Bildverarbeitung 2016, pages 37–48. KIT Scientific Publishing, Karlsruhe, 2016.Search in Google Scholar
2. ISO 14997. Optics and photonics – Test methods for surface imperfections of optical elements, 2017.Search in Google Scholar
3. Daniel Kiefhaber. Automated inspection of optical surface cleanliness. Presented at the EOSAM European Optical Society Biennial Meeting 2018, Delft, Netherlands, 2018.10.1117/12.2526321Search in Google Scholar
4. Ralph Neubecker and Jenny E. Hon. Automatic inspection for surface imperfections: requirements, potentials and limits. In Rolf Rascher, Oliver Fähnle, Christine Wünsche and Christian Schopf, editors, Third European Seminar on Precision Optics Manufacturing, SPIE Proceedings, page 1000907, SPIE, 2016.10.1117/12.2235863Search in Google Scholar
5. Fabian Etzold, Daniel Kiefhaber, Arno F. Warken, Peter Würtz, Jenny Hon and Jean-Michel Asfour. A novel approach towards standardizing surface quality inspection. In Rolf Rascher, Oliver Fähnle, Christine Wünsche and Christian Schopf, editors, Third European Seminar on Precision Optics Manufacturing, SPIE Proceedings, page 1000908, SPIE, 2016.10.1117/12.2235743Search in Google Scholar
6. Alexander Schöch, Carlo Bach, Carsten Ziolek, Patric Perez and Sabine Linz-Dittrich. Automating the surface inspection on small customer-specific optical elements. In Peter Schelkens, Touradj Ebrahimi and Gabriel Cristóbal, editors, Optics, Photonics, and Digital Technologies for Imaging Applications V, SPIE, 2018.10.1117/12.2307454Search in Google Scholar
7. Alexander Schöch, Patric Perez, Sabine Linz-Dittrich, Carlo Bach and Carsten Ziolek. Automated surface inspection of small customer-specific optical elements. tm - Technisches Messen, 84(7–8):502–511, 2017.10.1515/teme-2017-0012Search in Google Scholar
8. Alexander Schöch, Patric Perez, Sabine Linz-Dittrich, Carlo Bach and Carsten Ziolek. Automated classification of imperfections and dust on small optical elements In Thomas Längle, Fernando Puente León and Michael Heizmann, editors, Forum Bildverarbeitung 2018, pages 161–172. KIT Scientific Publishing, Karlsruhe, 2018.Search in Google Scholar
9. Lu Li, Dong Liu, Pin Cao, Shibin Xie, Yang Li, Yangjie Chen and Yongying Yang. Automated discrimination between digs and dust particles on optical surfaces with dark-field scattering microscopy. Applied Optics, 53(23):5131–5140, 2014.10.1364/AO.53.005131Search in Google Scholar PubMed
© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Forum Bildverarbeitung 2018
- Beiträge
- Kamerabasierte Erfassung von Vitalparametern
- Präzise Vermessung von Zuckerrübenkeimlingen mit dem phenoTest
- Kalibrierfreie 3D-Rekonstruktion von gekrümmten Oberflächen mittels orthographischer Projektion unter Verwendung von Reflexionsmarkern
- Stochastische Texturerkennung zur Bildsegmentierung
- Automated discrimination of surface imperfections and adhered particles on customer-specific optical elements
- Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods
- Ein evolutionärer Ansatz für aufgabenspezifische MobileNet-Topologien
- Extended noise equalisation for image compression in microscopical applications
- Subtleties of extrinsic calibration of cameras with non-overlapping fields of view
- Bilderzeugung von virtuellen Szenen mittels vier- und sechseckbasierter oder wahlfreier Rasterung
Articles in the same Issue
- Frontmatter
- Editorial
- Forum Bildverarbeitung 2018
- Beiträge
- Kamerabasierte Erfassung von Vitalparametern
- Präzise Vermessung von Zuckerrübenkeimlingen mit dem phenoTest
- Kalibrierfreie 3D-Rekonstruktion von gekrümmten Oberflächen mittels orthographischer Projektion unter Verwendung von Reflexionsmarkern
- Stochastische Texturerkennung zur Bildsegmentierung
- Automated discrimination of surface imperfections and adhered particles on customer-specific optical elements
- Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods
- Ein evolutionärer Ansatz für aufgabenspezifische MobileNet-Topologien
- Extended noise equalisation for image compression in microscopical applications
- Subtleties of extrinsic calibration of cameras with non-overlapping fields of view
- Bilderzeugung von virtuellen Szenen mittels vier- und sechseckbasierter oder wahlfreier Rasterung
