Bayesian inference for multi-label classification for root cause analysis and probe card maintenance decision support and an empirical study
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-024-02336-z
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
- Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: an empirically grounded conceptualization," International Journal of Production Economics, Elsevier, vol. 223(C).
- Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
- Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
- Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
- Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
- Wenhan Fu & Chen-Fu Chien & Lizhen Tang, 2022. "Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 785-798, March.
- Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
- Chien, Chen-Fu & Wang, Hung-Ju & Wang, Min, 2007. "A UNISON framework for analyzing alternative strategies of IC final testing for enhancing overall operational effectiveness," International Journal of Production Economics, Elsevier, vol. 107(1), pages 20-30, May.
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.- Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
- Wenhan Fu & Chen-Fu Chien & Lizhen Tang, 2022. "Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 785-798, March.
- Yong Ren & Qian Wang, 2022. "Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2239-2256, December.
- Nduhura Munga, Justin & Dauzère-Pérès, Stéphane & Yugma, Claude & Vialletelle, Philippe, 2015. "A mathematical programming approach for optimizing control plans in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 160(C), pages 213-219.
- Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
- Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
- T. Herzog & M. Brandt & A. Trinchi & A. Sola & A. Molotnikov, 2024. "Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1407-1437, April.
- Jože M. Rožanec & Luka Bizjak & Elena Trajkova & Patrik Zajec & Jelle Keizer & Blaž Fortuna & Dunja Mladenić, 2024. "Active learning and novel model calibration measurements for automated visual inspection in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 1963-1984, June.
- Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
- Abhilash Puthanveettil Madathil & Xichun Luo & Qi Liu & Charles Walker & Rajeshkumar Madarkar & Yukui Cai & Zhanqiang Liu & Wenlong Chang & Yi Qin, 2024. "Intrinsic and post-hoc XAI approaches for fingerprint identification and response prediction in smart manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4159-4180, December.
- Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
- Thi-Tinh Le & Seok-Ju Lee & Minh-Chau Dinh & Minwon Park, 2023. "Design of an Improved Remaining Useful Life Prediction Model Based on Vibration Signals of Wind Turbine Rotating Components," Energies, MDPI, vol. 17(1), pages 1-18, December.
- Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
- Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
- Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
- Vito Introna & Annalisa Santolamazza, 2024. "Strategic maintenance planning in the digital era: a hybrid approach merging Reliability-Centered Maintenance with digitalization opportunities," Operations Management Research, Springer, vol. 17(4), pages 1397-1420, December.
- Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
- Tamie Takeda Yokoyama & Satie Ledoux Takeda-Berger & Marco Aurélio Oliveira & Andre Hideto Futami & Luiz Veriano Oliveira Dalla Valentina & Enzo Morosini Frazzon, 2023. "Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework," Operations Management Research, Springer, vol. 16(1), pages 49-79, March.
- Thirupathi Samala & Vijaya Kumar Manupati & Maria Leonilde R. Varela & Goran Putnik, 2021. "Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review," Future Internet, MDPI, vol. 13(3), pages 1-18, February.
- Jingchang Li & Longchao Cao & Jiexiang Hu & Minhua Sheng & Qi Zhou & Peng Jin, 2022. "A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 687-702, March.
More about this item
Keywords
Semiconductor manufacturing; Data-driven approach; Bayesian network; Probe card; Fault diagnosis;All these keywords.
Statistics
Access and download statisticsCorrections
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:3:d:10.1007_s10845-024-02336-z. 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.