Effect of Hatching Strategy on the Dimensional Accuracy of Components Manufactured Using the Stereolithography (SLA) Process

Authors

https://doi.org/10.48314/ijrceai.vi.64

Abstract

Stereolithography (SLA), the first Rapid Prototyping (RP) technology developed, is an additive manufacturing process that enables the direct fabrication of a resin model from a three-dimensional Computer-Aided Design (CAD) model. In this process, each cross-sectional layer of the model is traced by a Ultraviolet (UV) laser beam on the surface of a photosensitive liquid polymer. During the fabrication of each layer, the contour is scanned first, followed by hatching of the enclosed area. The laser exposure cures the hatched region, thereby solidifying the material and forming the desired layer. The dimensional accuracy of parts manufactured by Stereolithography is influenced by several process parameters, among which the hatching strategy adopted for laser scanning of the resin cross-section is one of the most significant. Various hatching strategies have been proposed to optimize this process. In this paper, different hatching methods are introduced and discussed. Furthermore, based on the results of a previous study, the Stereolithography process is modeled using a Multilayer Perceptron (MLP) Artificial Neural Network (ANN). The modeling results indicate that, after layer thickness, the hatching strategy is the second most influential parameter affecting the dimensional accuracy of fabricated parts.

Keywords:

Stereolithography, Dimensional accuracy, Artificial neural networks, Multilayer Perceptron, Hatching strategy

References

  1. [1] Jacobs, P. F. (1992). Rapid prototyping & manufacturing: Fundamentals of stereolithography. Society of Manufacturing Engineers. https://books.google.com/books/about/Rapid_Prototyping_Manufacturing.html?id=HvcN0w1VyxwC
  2. [2] Jacobs, P. F. (1995). Stereolithography and other RP&M technologies: From rapid prototyping to rapid tooling. Society of Manufacturing Engineers. https://books.google.com/books/about/Stereolithography_and_Other_RP_M_Technol.html?id=3YI0gvTyGtEC
  3. [3] Zhou, J. G., Herscovici, D., & Chen, C. C. (2000). Parametric process optimization to improve the accuracy of rapid prototyped Stereolithography parts. International journal of machine tools and manufacture, 40(3), 363–379. https://doi.org/10.1016/S0890-6955(99)00068-1
  4. [4] Horton, L., Gargiulo, E., & Keefe, M. (1993). An experimental study of the parameters affecting curl in parts created using stereolithography. Proceedings of the fourth solid freeform fabrication symposium (pp. 178–185). Texas Academic Press. https://doi.org/10.15781/T2K35MX4R?utm_source=chatgpt.com
  5. [5] Onuh, S. O., & Hon, K. K. B. (2001). Improving Stereolithography part accuracy for industrial applications. The international journal of advanced manufacturing technology, 17(1), 61–68. https://doi.org/10.1007/s001700170210
  6. [6] Lee, S. H., Park, W. S., Cho, H. S., Zhang, W., & Leu, M. C. (2001). A neural network approach to the modelling and analysis of Stereolithography processes. Proceedings of the institution of mechanical engineers, part b: Journal of engineering manufacture, 215(12), 1719–1733. https://doi.org/10.1177/095440540121501206
  7. [7] Cho, H. S., Park, W. S., Choi, B. W., & Leu, M. C. (2000). Determining optimal parameters for Stereolithography processes via genetic algorithm. Journal of manufacturing systems, 19(1), 18–27. https://doi.org/10.1016/S0278-6125(00)88887-1
  8. [8] Lee, E. D., Sim, J. H., Kweon, H. J., & Paik, I. H. (2004). Determination of process parameters in Stereolithography using neural network. KSME international journal, 18(3), 443–452. https://doi.org/10.1007/BF02996109
  9. [9] Raju, B., Chandrashekar.U, Drakshayani, D., & Kunjan, C. (2010). Determining the influence of layer thickness for rapid prototyping with Stereolithography (SLA) process. International journal of engineering science and technology, 2(7), 3199–3205. https://www.researchgate.net/publication/50315482
  10. [10] Kruth, J.-P., Leu, M. C., & Nakagawa, T. (1998). Progress in additive manufacturing and rapid prototyping. CIRP annals, 47(2), 525–540. https://doi.org/10.1016/S0007-8506(07)63240-5
  11. [11] Chockalingam, K., Jawahar, N., & Chandrasekhar, U. (2006). Influence of layer thickness on mechanical properties in Stereolithography. Rapid prototyping journal, 12(2), 106–113. https://doi.org/10.1108/13552540610652456
  12. [12] Revilla-León, M., & Özcan, M. (2019). Additive manufacturing technologies used for processing polymers: Current status and potential application in prosthetic dentistry. Journal of prosthodontics, 28(2), 146–158. https://doi.org/10.1111/jopr.12801
  13. [13] Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T. Q., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites part b: Engineering, 143, 172–196. https://doi.org/10.1016/j.compositesb.2018.02.012
  14. [14] Bikas, H., Stavropoulos, P., & Chryssolouris, G. (2016). Additive manufacturing methods and modelling approaches: a critical review. The international journal of advanced manufacturing technology, 83(1), 389–405. https://doi.org/10.1007/s00170-015-7576-2
  15. [15] Wang, Y., Ding, Y., Yu, K., & Dong, G. (2024). Innovative polymer-based composite materials in additive manufacturing: A review of methods, materials, and applications. Polymer composites, 45(17), 15389–15420. https://doi.org/10.1002/pc.28854
  16. [16] Tariq, A., Arif, Z. U., Khalid, M. Y., Hossain, M., Rasool, P. I., Umer, R., & Ramakrishna, S. (2023). Recent advances in the additive manufacturing of stimuli-responsive soft polymers. Advanced engineering materials, 25(21), 2301074. https://doi.org/10.1002/adem.202301074

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Published

2025-12-17

Issue

Vol. 2 No. 4 (2025): International Journal of Researches on Civil Engineering with Artificial Intelligence (IJRCEAI)

Section

Articles

How to Cite

Ghadami, F. (2025). Effect of Hatching Strategy on the Dimensional Accuracy of Components Manufactured Using the Stereolithography (SLA) Process. International Journal of Researches on Civil Engineering With Artificial Intelligence , 2(4), 202-210. https://doi.org/10.48314/ijrceai.vi.64

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