IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i4d10.1007_s10845-023-02306-x.html
   My bibliography  Save this article

Optimization of image acquisition by automated white-light interferometers during the inspection of object surfaces

Author

Listed:
  • Björn Schwarze

    (Czech Technical University in Prague)

  • Stefan Edelkamp

    (Czech Technical University in Prague
    Charles University in Prague)

Abstract

This paper considers the efficient quality assurance of diverse geometric objects through the use of a white-light interferometer, with a primary focus on minimizing the number of required image captures. The motivation behind such an algorithm stems from the extended recording times associated with various free-form sheet metal parts. Given that capturing images with a microscope typically consumes 30–40 s, maintaining high-quality assurance is imperative. A reduction in the number of images not only expedites part throughput but also enhances the economic efficiency. A unique aspect in this context is the requirement for focus points to consistently align with the part’s surface. We formulate this challenge in a mathematical framework, necessitating a comprehensive literature review to identify potential solutions, and introduce an algorithm designed to optimize the image acquisition process for inspecting object surfaces. The proposed algorithm enables efficient coverage of large surfaces on objects of various sizes and shapes using a minimal number of images. The primary objective is to create the most concise list of points that comprehensively encompass the entire object surface. Subsequently, the paper conducts a comparative analysis of various strategies to identify the most effective approach.

Suggested Citation

  • Björn Schwarze & Stefan Edelkamp, 2025. "Optimization of image acquisition by automated white-light interferometers during the inspection of object surfaces," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2537-2566, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-023-02306-x
    DOI: 10.1007/s10845-023-02306-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02306-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-023-02306-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Foivos Psarommatis & João Sousa & João Pedro Mendonça & Dimitris Kiritsis, 2022. "Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 73-91, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Albus & Timothée Hornek & Werner Kraus & Marco F. Huber, 2025. "Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1175-1199, February.
    2. Devika Kannan & Parvaneh Gholipour & Chunguang Bai, 2023. "Smart manufacturing as a strategic tool to mitigate sustainable manufacturing challenges: a case approach," Annals of Operations Research, Springer, vol. 331(1), pages 543-579, December.
    3. Hasin Md. Muhtasim Taqi & S. M. Shafaat Akhter Nur & Sheak Salman & Tazim Ahmed & Sudipa Sarker & Syed Mithun Ali & Bathrinath Sankaranarayanan, 2023. "Behavioural factors for Industry 4.0 adoption: implications for knowledge-based supply chains," Operations Management Research, Springer, vol. 16(3), pages 1122-1139, September.
    4. Changheon Han & Jiho Lee & Martin B. G. Jun & Sang Won Lee & Huitaek Yun, 2025. "Visual coating inspection framework via self-labeling and multi-stage deep learning strategies," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2461-2478, April.
    5. Bart Meyers & Hans Vangheluwe & Pieter Lietaert & Geert Vanderhulst & Johan Van Noten & Michel Schaffers & Davy Maes & Klaas Gadeyne, 2024. "Towards a knowledge graph framework for ad hoc analysis in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3731-3752, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-023-02306-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.