On-machine focus variation measurement for micro-scale hybrid surface texture machining

Santoso, Teguh; Syam, Wahyudin P.; Darukumalli, Subbareddy; Cai, Yukui; Helmli, Franz; Luo, Xichun; Leach, Richard

Abstract

Fast and accurate in-line areal surface topography measuring instruments are required to control the quality of microscale manufactured components, without significantly slowing down the production process. Full-field areal optical surface topography measurement instruments are promising for in-line or on-machine measurement applications due to their ability to measure quickly, to access small features and to avoid surface damage. This paper presents the development and integration of a compact optical focus variation sensor for on-machine surface topography measurement mounted on to a hybrid ultraprecision machine tool. The sensor development is described and a case study involving the on-machine dimensional measurement of the depth of hydrophobic microscale features, including microchannels and micro-dimples, is presented. Comparisons of results between the on-machine measurements obtained by the developed sensor and a desktop focus variation microscope are presented and discussed. The comparison results show that the developed focus variation sensor is able to perform on-machine dimensional measurement of microscale features within sub-micrometre accuracy.

Citation

Santoso, T., Syam, W. P., Darukumalli, S., Cai, Y., Helmli, F., Luo, X., & Leach, R. (2020). On-machine focus variation measurement for micro-scale hybrid surface texture machining. International Journal of Advanced Manufacturing Technology, 109(9-12), 2353–2364. https://doi.org/10.1007/s00170-020-05767-z

• Journal Article Type: Article
• Acceptance Date: Jul 10, 2020
• Online Publication Date: Jul 30, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Jul 16, 2020
• Publicly Available Date: Jul 30, 2020
• Journal: The International Journal of Advanced Manufacturing Technology
• Electronic ISSN: 0268-3768
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Issue: 9-12
• Pages: 2353–2364
• DOI: https://doi.org/10.1007/s00170-020-05767-z
• Keywords: Control and Systems Engineering; Mechanical Engineering; Industrial and Manufacturing Engineering; Software; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/4770278
• Publisher URL: https://link.springer.com/article/10.1007/s00170-020-05767-z
• Additional Information: Received: 16 March 2020; Accepted: 13 July 2020; First Online: 30 July 2020