IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i3d10.1007_s10845-020-01626-6.html
   My bibliography  Save this article

Impact of the thermomechanical load on subsurface phase transformations during cryogenic turning of metastable austenitic steels

Author

Listed:
  • Hendrik Hotz

    (TU Kaiserslautern)

  • Benjamin Kirsch

    (TU Kaiserslautern)

  • Jan C. Aurich

    (TU Kaiserslautern)

Abstract

When machining metastable austenitic stainless steel with cryogenic cooling, a deformation-induced phase transformation from γ-austenite to α′-martensite can be realized in the workpiece subsurface. This leads to a higher microhardness and thus improved fatigue and wear resistance. A parametric and a non-parametric model were developed in order to investigate the correlation between the thermomechanical load in the workpiece subsurface and the resulting α′-martensite content. It was demonstrated that increasing passive forces and cutting forces promoted the deformation-induced phase transformation, while increasing temperatures had an inhibiting effect. The feed force had no significant influence on the α′-martensite content. With the proposed models it is now possible to estimate the α′-martensite content during cryogenic turning by means of in-situ measurement of process forces and temperatures.

Suggested Citation

  • Hendrik Hotz & Benjamin Kirsch & Jan C. Aurich, 2021. "Impact of the thermomechanical load on subsurface phase transformations during cryogenic turning of metastable austenitic steels," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 877-894, March.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01626-6
    DOI: 10.1007/s10845-020-01626-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01626-6
    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/s10845-020-01626-6?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. Wei Ji & Shubin Yin & Lihui Wang, 2019. "A big data analytics based machining optimisation approach," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1483-1495, March.
    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. Marie-Anne Le-Dain & Lamiae Benhayoun & Judy Matthews & Marine Liard, 2023. "Barriers and opportunities of digital servitization for SMEs: the effect of smart Product-Service System business models," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 359-393, March.
    2. Siti Nurfadilah Binti Jaini & Deug-Woo Lee & Seung-Jun Lee & Mi-Ru Kim & Gil-Ho Son, 2021. "Indirect tool monitoring in drilling based on gap sensor signal and multilayer perceptron feed forward neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1605-1619, August.
    3. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    4. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
    5. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    7. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
    8. Xu Tan & Lining Xing & Zhaoquan Cai & Gaige Wang, 2020. "Analysis of production cycle-time distribution with a big-data approach," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1889-1897, December.
    9. Huidong Sun & Mustafa Raza Rabbani & Muhammad Safdar Sial & Siming Yu & José António Filipe & Jacob Cherian, 2020. "Identifying Big Data’s Opportunities, Challenges, and Implications in Finance," Mathematics, MDPI, vol. 8(10), pages 1-19, October.

    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:32:y:2021:i:3:d:10.1007_s10845-020-01626-6. 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.