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Research Article

Multi-Laser Scan Assignment and Scheduling Optimization for Large Scale Metal Additive Manufacturing

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Received 18 Jan 2024, Accepted 13 Jul 2024, Accepted author version posted online: 06 Aug 2024
Accepted author version

References

  • D Printing Industry (2023). Top 10 largest 3d printers. Accessed: 2024-07-19. https://3dprintingindustry.com/news/top-10-largest-3d-printers-54377/
  • Ahn, K. and Park, J. (2021). Cooperative zone-based rebalancing of idle overhead hoist transportations using multi-agent reinforcement learning with graph representation learning. IISE Transactions, 53(10):1140–1156.
  • Alibabaei, K., Gaspar, P. D., Assunção, E., Alirezazadeh, S., Lima, T. M., Soares, V. N., and Caldeira, J. M. (2022). Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization: A case study at a site in PORTUGAL. Computers, 11(7):104.
  • Chen, C., Chang, S., Zhu, J., Xiao, Z., Zhu, H., and Zeng, X. (2020). Residual stress of typical parts in laser powder bed fusion. Journal of Manufacturing Processes, 59:621–628.
  • Chen, Q., Taylor, H., Takezawa, A., Liang, X., Jimenez, X., Wicker, R., and To, A. C. (2021). Island scanning pattern optimization for residual deformation mitigation in laser powder bed fusion via sequential inherent strain method and sensitivity analysis. Additive Manufacturing, 46:102116.
  • Cokyasar, T. and Jin, M. (2023). Additive manufacturing capacity allocation problem over a network. IISE Transactions, 55(8):807–820.
  • Du, Y. and Arnold, C. B. (2024). Prediction of the inter-track bonding during the dual-laser powder bed fusion. Journal of Manufacturing Processes, 120:911–919.
  • Farrag, A., Yang, Y., Cao, N., Won, D., and Jin, Y. (2024). Physics-informed machine learning for metal additive manufacturing. Progress in Additive Manufacturing, pages 1–15.
  • General Electric Additive (2020). 3d printed jet engine: Meet the team of young engineers who brought 3d printing inside the ge9x, the world’s largest jet engine. Accessed: 2023-10-19. https://www.ge.com/additive/stories/3d- printed- jet- engine-meetteam- young-engineers-brought-3d-printing-inside-ge9x-worlds-largest
  • General Electric Company (2017). M2 cusing multilaser. Accessed: 2023-10-19.
  • Gerstgrasser, M., Cloots, M., Stirnimann, J., and Wegener, K. (2021). Residual stress reduction of LPBF-processed CM247LC samples via multi laser beam strategies. The International Journal of Advanced Manufacturing Technology, 117(7-8):2093–2103.
  • Hassen, A. A., Noakes, M., Nandwana, P., Kim, S., Kunc, V., Vaidya, U., Love, L., and Nycz, A. (2020). Scaling up metal additive manufacturing process to fabricate molds for composite manufacturing. Additive Manufacturing, 32:101093.
  • Jackson, B. (2018). Stelia aerospace use WAAM build an airplane fuselage. Accessed: 2024-07-19.
  • Jia, H., Sun, H., Wang, H., Wu, Y., and Wang, H. (2021). Scanning strategy in selective laser melting (SLM): a review. The International Journal of Advanced Manufacturing Technology, 113:2413–2435.
  • Jin, Y., Pierson, H. A., and Liao, H. (2019). Toolpath allocation and scheduling for concurrent fused filament fabrication with multiple extruders. IISE Transactions, 51(2):192–208.
  • Kessal, B. A., Fares, C., Meliani, M. H., Alhussein, A., Bouledroua, O., and François, M. (2020). Effect of gas tungsten arc welding parameters on the corrosion resistance and the residual stress of heat affected zone. Engineering Failure Analysis, 107:104200.
  • Kim, B., Kim, J. G., and Lee, S. (2023). A multi-agent reinforcement learning model for inventory transshipments under supply chain disruption. IISE Transactions, pages 1–14.
  • Li, C., Fu, C., Guo, Y., and Fang, F. (2016). A multiscale modeling approach for fast prediction of part distortion in selective laser melting. Journal of Materials Processing Technology, 229:703–712.
  • Li, Y., Du, J., and Jiang, W. (2024). Reinforcement learning for process control with application in semiconductor manufacturing. IISE Transactions, 56(6):585–599.
  • Li, Z., Segura, L. J., Li, Y., Zhou, C., and Sun, H. (2023a). Multiclass reinforced active learning for droplet pinch-off behaviors identification in inkjet printing. Journal of Manufacturing Science and Engineering, 145(7):071002.
  • Li, Z., Yao, F., and Sun, H. (2023b). Reinforced active learning for CVD-grown two-dimensional materials characterization. IISE Transactions, pages 1–13.
  • Liu, C.-L., Chang, C.-C., and Tseng, C.-J. (2020). Actor-critic deep reinforcement learning for solving job shop scheduling problems. IEEE Access, 8:71752–71762.
  • Liu, H., Li, Z., Li, W., Zhang, W., Wang, T., and Liu, W. (2023). Influence of arc preheating on stress and strain of additive manufactured components. Journal of Materials Engineering and Performance, 32(14):6550–6563.
  • Lu, Y., Wu, S., Gan, Y., Huang, T., Yang, C., Junjie, L., and Lin, J. (2015). Study on the microstructure, mechanical property and residual stress of SLM INCONEL-718 alloy manufactured by differing island scanning strategy. Optics & Laser Technology, 75:197–206.
  • Malekipour, E., Valladares, H., Shin, Y., and El-Mounayri, H. (2020). Optimization of chessboard scanning strategy using genetic algorithm in multi-laser additive manufacturing process. In ASME International Mechanical Engineering Congress and Exposition, volume 84485, page V02AT02A054. American Society of Mechanical Engineers.
  • Martin, A. A., Calta, N. P., Khairallah, S. A., Wang, J., Depond, P. J., Fong, A. Y., Thampy, V., Guss, G. M., Kiss, A. M., Stone, K. H., et al. (2019). Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nature Communications, 10(1):1987.
  • Masoomi, M., Thompson, S. M., and Shamsaei, N. (2017). Laser powder bed fusion of Ti-6Al-4V parts: Thermal modeling and mechanical implications. International Journal of Machine Tools and Manufacture, 118:73–90.
  • Metal AM (2018). How process parameters drive successful metal am part production. Accessed: 2024-07-19.
  • Montazeri, M., Nassar, A. R., Dunbar, A. J., and Rao, P. (2020). In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy. IISE Transactions, 52(5):500–515.
  • Mugwagwa, L., Dimitrov, D., Matope, S., and Yadroitsev, I. (2019). Evaluation of the impact of scanning strategies on residual stresses in selective laser melting. The International Journal of Advanced Manufacturing Technology, 102:2441–2450.
  • Neves, M. and Neto, P. (2022). Deep reinforcement learning applied to an assembly sequence planning problem with user preferences. The International Journal of Advanced Manufacturing Technology, 122(11-12):4235–4245.
  • Nycz, A., Adediran, A. I., Noakes, M. W., and Love, L. J. (2016). Large scale metal additive techniques review. In 2016 International Solid Freeform Fabrication Symposium. University of Texas at Austin.
  • Parry, L., Ashcroft, I., and Wildman, R. (2019). Geometrical effects on residual stress in selective laser melting. Additive Manufacturing, 25:166–175.
  • Price, S., Lydon, J., Cooper, K., and Chou, K. (2013). Experimental temperature analysis of powder-based electron beam additive manufacturing. In 2013 International Solid Freeform Fabrication Symposium. University of Texas at Austin.
  • Ramani, K. S., Malekipour, E., and Okwudire, C. E. (2021). Toward intelligent online scan sequence optimization for uniform temperature distribution in LPBF additive manufacturing. In International Manufacturing Science and Engineering Conference, volume 85062, page V001T01A023. American Society of Mechanical Engineers.
  • Ramos, D., Belblidia, F., and Sienz, J. (2019). New scanning strategy to reduce warpage in additive manufacturing. Additive Manufacturing, 28:554–564.
  • Ren, K., Chew, Y., Zhang, Y., Fuh, J., and Bi, G. (2020). Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Computer Methods in Applied Mechanics and Engineering, 362:112734.
  • Salem, M., Le Roux, S., Hor, A., and Dour, G. (2020). A new insight on the analysis of residual stresses related distortions in selective laser melting of Ti-6Al-4V using the improved bridge curvature method. Additive Manufacturing, 36:101586.
  • Sewak, M. (2019). Actor-critic models and the A3C: The asynchronous advantage actor-critic model. Deep reinforcement learning: frontiers of artificial intelligence, pages 141–152.
  • Soylemez, E. (2020). High deposition rate approach of selective laser melting through defocused single bead experiments and thermal finite element analysis for Ti-6Al-4V. Additive Manufacturing, 31:100984.
  • Sun, L., Ren, X., He, J., and Zhang, Z. (2021). A bead sequence-driven deposition pattern evaluation criterion for lowering residual stresses in additive manufacturing. Additive Manufacturing, 48:102424.
  • Sutton, R. S. and Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
  • Taminger, K. M. and Domack, C. S. (2020). Challenges in metal additive manufacturing for large-scale aerospace applications. Women in Aerospace Materials: Advancements and Perspectives of Emerging Technologies, pages 105–124.
  • Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019). Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350–354.
  • Wong, H., Dawson, K., Ravi, G., Howlett, L., Jones, R., and Sutcliffe, C. (2019). Multi-laser powder bed fusion benchmarking—initial trials with Inconel 625. The International Journal of Advanced Manufacturing Technology, 105:2891–2906.
  • Yadroitsev, I., Yadroitsava, I., and Du Plessis, A. (2021). Basics of laser powder bed fusion. In Fundamentals of Laser Powder Bed Fusion of Metals, pages 15–38. Elsevier.
  • Zhang, C., Zhu, H., Hu, Z., Zhang, L., and Zeng, X. (2019). A comparative study on single-laser and multi-laser selective laser melting alsi10mg: defects, microstructure and mechanical properties. Materials Science and Engineering: A, 746:416–423.
  • Zhang, W., Abbott, W. M., Sasnauskas, A., and Lupoi, R. (2022). Process parameters optimisation for mitigating residual stress in dual-laser beam powder bed fusion additive manufacturing. Metals, 12(3):420.
  • Zhang, W., Tong, M., and Harrison, N. M. (2020). Scanning strategies effect on temperature, residual stress and deformation by multi-laser beam powder bed fusion manufacturing. Additive Manufacturing, 36:101507.
  • Zhou, L., Zhang, L., and Horn, B. K. (2020). Deep reinforcement learning-based dynamic scheduling in smart manufacturing. Procedia Cirp, 93:383–388.

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