IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i14p3204-d1199408.html
   My bibliography  Save this article

Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models

Author

Listed:
  • You-Shyang Chen

    (College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Jieh-Ren Chang

    (Department of Electronic Engineering, National Ilan University, Yilan City 26047, Taiwan)

  • Ying-Hsun Hung

    (Department of Finance, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Jia-Hsien Lai

    (Department of Electronic Engineering, National Ilan University, Yilan City 26047, Taiwan)

Abstract

Selective laser sintering (SLS) is one of the most popular 3D molding technologies; however, the manufacturing steps of SLS machines are cumbersome, and the most important step is focused on molding testing because it requires a lot of direct labor and material costs. This research establishes advanced hybrid mathematical classification models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN), for effectively identifying the SLS yield of the sintering results from three sintered objects (boxes, cylinders, and flats) to achieve the key purpose of reducing the number of model verification and machine parameter adjustments, thereby saving a lot of manufacturing time and costs. In the experimental process, performance evaluation indicators, such as classification accuracy (CA), area under the ROC curve (AUC), and F1-score, are used to measure the proposed models’ experience with practical industry data. In the experimental results, the ANN gets the highest 0.6168 of CA, and it is found that each machine reduces the average sintering time by four hours when compared with the original manufacturing process. Moreover, we employ an oversampling method to expand the sample data to overcome the existing problems of class imbalance in the dataset collected. An important finding is that the RF algorithm is more suitable for predicting the sintering failure of objects, and its average sintering times per machine are 1.7, which is lower than the 1.95 times of ANN and 2.25 times of SVM. Conclusively, this research yields some valuable empirical conclusions and core research findings. In terms of research contributions, the research results can be provided to relevant academic circles and industry requirements for referential use in follow-up studies or industrial applications.

Suggested Citation

  • You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3204-:d:1199408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/14/3204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/14/3204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
    2. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    3. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    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. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    2. Thinh Quy Duc Pham & Truong Vinh Hoang & Xuan Tran & Quoc Tuan Pham & Seifallah Fetni & Laurent Duchêne & Hoang Son Tran & Anne-Marie Habraken, 2023. "Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1701-1719, April.
    3. Chin Soon Ku & Jiale Xiong & Yen-Lin Chen & Shing Dhee Cheah & Hoong Cheng Soong & Lip Yee Por, 2023. "Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    4. Dongxiang Hou & Xiaodong Wang & Qing Song & Xuesong Mei & Haicheng Wang, 2024. "A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 331-341, January.
    5. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    6. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).
    7. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.
    8. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.
    9. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    10. Agnieszka Wawrzyniak & Andrzej Przybylak & Piotr Boniecki & Agnieszka Sujak & Maciej Zaborowicz, 2023. "Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland," Agriculture, MDPI, vol. 13(7), pages 1-13, July.
    11. Deyuan Ma & Ping Jiang & Leshi Shu & Zhaoliang Gong & Yilin Wang & Shaoning Geng, 2024. "Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 55-73, January.
    12. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).

    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:gam:jmathe:v:11:y:2023:i:14:p:3204-:d:1199408. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.