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Using Taguchi method and gray relational analysis to enhance manufacturing performance in photopolymerization

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Abstract

Digital light processing (DLP) is the most widely used 3D molding technology in the medical industry. DLP machine developers aim at the interactive relationship between the forming separation force during layer-by-layer printing and mechanical printing parameters, which affect the dimensional accuracy of printed materials. Experiments were conducted using the Taguchi method to explore the correlation between four machine-printing parameters: slice thickness, geometric sharpness, exposure time, distance between the printed object and fixed end of the resin tank, and printing accuracy. The results indicated that the exposure time, the slice thickness, and the geometric shapes had the most significant impact on the dimensional accuracies along the X, Y, and Z axes, respectively, and the distance between the printed object and the fixed end of the resin tank had the most significant impact on the separation force generated during the printing process. Then, gray correlation analysis was used to propose the optimal printing parameters for the machine that balance the size accuracy of the printed object and the forming separation force. Machine-printing parameters are offered that can optimize the target characteristics. The Taguchi and the gray relational analysis strategy we adopted in this study can be extended to different DLP manufacturing applications to improve production efficiency.

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References

  1. Laleh M et al (2023) Heat treatment for metal additive manufacturing. Prog Mater Sci 133:101051

    Article  Google Scholar 

  2. Tan C et al (2023) Review on field-assisted metal additive manufacturing. Int J Mach Tools Manuf 189:104032

    Article  Google Scholar 

  3. Bao Y, Paunović N, Leroux JC (2022) Challenges and opportunities in 3D printing of biodegradable medical devices by emerging photopolymerization techniques. Adv Funct Mater 32:2109864

    Article  Google Scholar 

  4. Shaukat U, Rossegger E, Schlögl S (2022) A review of multi-material 3D printing of functional materials via vat photopolymerization. Polym 14:2449

    Article  Google Scholar 

  5. Santoliquido O, Colombo P, Ortona A (2019) Additive Manufacturing of ceramic components by Digital Light Processing: a comparison between the “bottom-up” and the “top-down” approaches. J Eur Ceram Soc 39:2140–2148

    Article  Google Scholar 

  6. Yang CJ (2022) Optimizing manufacturing parameters of DLP machine by taguchi method and desirability approach. Mach 10:901

    Article  Google Scholar 

  7. Kazemi M, Rahimi A (2019) Improving the efficiency of fabrication of AM parts by segmentation design in DLP process. Rapid Prototyping J 25:1155–1168

    Article  Google Scholar 

  8. Yang CJ, Wu SS (2023) Comprehensive performance of a low-cost spring-assisted mechanism for digital light processing. Int J Adv Manuf Technol 125:4099–4118

    Article  Google Scholar 

  9. Wu X et al (2021) Tilting separation simulation and theory verification of mask projection stereolithography process. Rapid Prototyping J 27:851–860

    Article  Google Scholar 

  10. He H et al (2018) Effect of constrained surface texturing on separation force in projection stereolithography. J Manuf Sci Eng 140:091007

    Article  Google Scholar 

  11. Gritsenko D et al (2017) On characterization of separation force for resin replenishment enhancement in 3D printing. Addit Manuf 17:151–156

    Google Scholar 

  12. Jiang Y et al (2020) Constrained window design in projection stereolithography for continuous three-dimensional printing, 3D Print. Addit Manuf 7:163–169

    Google Scholar 

  13. Wu X, Xu C, Zhang Z (2021) Flexible film separation analysis of LCD based mask stereolithography. J Mater Process Technol 288:116916

    Article  Google Scholar 

  14. Yang F et al (2022) Characterizing the separation behavior of photocurable PDMS on a hydrogel film during vat photopolymerization: a benchmark study. Addit Manuf 58:103070

    Google Scholar 

  15. Ye H et al (2017) Investigation of separation force for constrained-surface stereolithography process from mechanics perspective. Rapid Prototyping J 23:696–710

    Article  Google Scholar 

  16. Jumbo-Jaramillo I, Lara-Padilla H (2021) Digital model to predict failures of porous structures in DLP-based additive manufacturing. XV Multidisciplinary International Congress on Science and Technology. Springer International Publishing, Cham, pp 219–228

    Google Scholar 

  17. Kovalenko I et al (2016) Examining the relationship between forces during stereolithography 3D printing and geometric parameters of the model. MATEC Web of Conf 40:02005

    Article  Google Scholar 

  18. Gritsenko D, Paoli R, Xu J (2022) The effect of acceleration on the separation force in constrained-surface stereolithography. Appl Sci 12:442

    Article  Google Scholar 

  19. Pan Y et al (2017) Study of separation force in constrained surface projection stereolithography. Rapid Prototyping J 23:353–361

    Article  Google Scholar 

  20. Yadegari F, Fesharakifard R, Barazandeh F (2021) Numerical and experimental investigation of effective parameters on separation force in bottom-up stereolithography process, AUT. J Mech Eng 5:401–418

    Google Scholar 

  21. Wu X et al (2017) Tilting separation analysis of bottom-up mask projection stereolithography based on cohesive zone model. J Mater Process Technol 243:184–196

    Article  Google Scholar 

  22. Ariyanti I, et al (2020) The effect of parameter process 3D printer technology digital light processing to geometric of shaft. Forum Res Sci Technol:239–243

  23. Seprianto D et al (2019) Optimization of production process parameters of DLP type 3D printer design for product roughness value, Forum Res. Sci Technol 7:179–183

    Google Scholar 

  24. Martínez-Agustín F et al (2022) 3D pattern formation from coupled Cahn-Hilliard and Swift-Hohenberg equations: morphological phases transitions of polymers, bock and diblock copolymers. Comp Mater Sci 210:111431

    Article  Google Scholar 

  25. Morales MA, et al (2023) Design and mathematical modeling of polymeric phases to obtain controlled microporosity materials by 3D printing. Prog Addit Manufact:1–10

  26. Kudo 3D. Available online: https://www.kudo3d.com/. (accessed on 1 September 2023)

  27. 3DM Advanced Materials (2023) Available online: https://www.3dm-shop.com/. (Accessed on 1 September 2023)

  28. ATOS Core (2023) Available online: https://www.gom.com/en/products/3d-scanning. (Accessed on 1 September 2023)

  29. OMEGA Engineering (2023). Available online: https://www.omega.com/pptst/LC201.html. (Accessed on 1 September 2023)

  30. National Instruments (2023) Available online: http://www.ni.com/en-gb/support/model.usb-6002.html. (Accessed on 1 September 2023)

  31. Minitab (2023) Available online: https://www.sfi-minitab.com.tw/. (Accessed on 1 September 2023)

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Acknowledgements

The authors thank the National Science and Technology Council under grant number 112-2221-E-027-051-MY3.

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Authors and Affiliations

  1. Department of Vehicle Engineering, National Taipei University of Technology, Taipei, Taiwan

    Po-Tuan Chen

  2. Program in Interdisciplinary Studies, National Sun Yat-sen University, Kaohsiung, Taiwan

    Cheng-Jung Yang

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  1. Po-Tuan Chen
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Correspondence to Cheng-Jung Yang.

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Chen, PT., Yang, CJ. Using Taguchi method and gray relational analysis to enhance manufacturing performance in photopolymerization. Prog Addit Manuf (2024). https://doi.org/10.1007/s40964-024-00593-1

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