GENETIC ALGORITHM-BASED OPTIMIZATION OF THE LASER-BEAM PATH IN ADDITIVE MANUFACTURING

Primož Potočnik; Andrej Jeromen; Edvard Govekar

doi:10.17222/mit.2023.989

Primož Potočnik University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI-1000 Ljubljana, Slovenia https://orcid.org/0000-0003-1758-2214
Andrej Jeromen University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
Edvard Govekar University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI-1000 Ljubljana, Slovenia

DOI: https://doi.org/10.17222/mit.2023.989

Keywords: additive manufacturing, laser beam path, genetic algorithm, optimization

Abstract

This study presents a methodology of genetic-algorithm-based optimization of the laser-beam path for improving laser-based additive manufacturing (AM). A simple thermal model was developed to simulate the effects of laser-induced heat input on the temperature distribution within the substrate during the fabrication of one layer. The optimization approach aims to find solutions with more homogeneous temperature properties that minimize the thermal gradient on the substrate caused by laser-based AM. The laser beam, i.e., the tool-path planning, is formulated as the search for the optimal sequence of cell depositions that minimize the fitness function, which is composed of two components, i.e., the thermal fitness and process fitness. The thermal fitness is expressed as the average thermal gradient, and the process fitness regulates the suitability of the proposed tool path for the implementation of the AM process. Various tool-path generators are proposed to initialize the initial population of tool-path solutions. Genetic-algorithm-based tool-path optimization is proposed, where custom initialization, crossover and mutation operators are developed for application in laser-based AM. Simulation studies demonstrate the effectiveness of the genetic-algorithm-based optimization in finding solutions that minimize the fitness function and therefore provide both thermally and, for the AM process implementation, more suitable laser-beam-path solutions.

References

1 Blakey-Milner, B.; Gradl, P.; Snedden, G.; Brooks, M.; Pitot, J.; Lopez, E.; Leary, M.; Berto, F.; du Plessis, A. Metal Additive Manufacturing in Aerospace: A Review. Mater. Des. 2021, 209, 110008. https://doi.org/10.1016/j.matdes.2021.110008.
2 Madhavadas, V.; Srivastava, D.; Chadha, U.; Aravind Raj, S.; Sultan, M. T. H.; Shahar, F. S.; Shah, A. U. M. A Review on Metal Additive Manufacturing for Intricately Shaped Aerospace Components. CIRP J. Manuf. Sci. Technol. 2022, 39, 18–36. https://doi.org/10.1016/j.cirpj.2022.07.005.
3 Shamsaei, N.; Yadollahi, A.; Bian, L.; Thompson, S. M. An Overview of Direct Laser Deposition for Additive Manufacturing; Part II: Mechanical Behavior, Process Parameter Optimization and Control. Addit. Manuf. 2015, 8, 12–35. https://doi.org/10.1016/j.addma.2015.07.002.
4 Thompson, S. M.; Bian, L.; Shamsaei, N.; Yadollahi, A. An Overview of Direct Laser Deposition for Additive Manufacturing; Part I: Transport Phenomena, Modeling and Diagnostics. Addit. Manuf. 2015, 8, 36–62. https://doi.org/10.1016/j.addma.2015.07.001.
5 Qi, X.; Chen, G.; Li, Y.; Cheng, X.; Li, C. Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives. Engineering 2019, 5 (4), 721–729. https://doi.org/10.1016/j.eng.2019.04.012.
6 Wang, C.; Tan, X. P.; Tor, S. B.; Lim, C. S. Machine Learning in Additive Manufacturing: State-of-the-Art and Perspectives. Addit. Manuf. 2020, 36 (January), 101538. https://doi.org/10.1016/j.addma.2020.101538.
7 Zheng, Y.; Liu, F.; Gao, J.; Liu, F.; Huang, C.; Zheng, H.; Wang, P.; Qiu, H. Effect of Different Heat Input on the Microstructure and Mechanical Properties of Laser Cladding Repaired 300M Steel. J. Mater. Res. Technol. 2023, 22, 556–568. https://doi.org/10.1016/j.jmrt.2022.11.153.
8 Reda Al-Sayed, S.; Elgazzar, H.; Nofal, A. Metallographic Investigation of Laser-Treated Ductile Iron Surface with Different Laser Heat Inputs. Ain Shams Eng. J. 2023, 14 (10), 102189. https://doi.org/10.1016/j.asej.2023.102189.
9 Ding, D.; Pan, Z.; Cuiuri, D.; Li, H. A Tool-Path Generation Strategy for Wire and Arc Additive Manufacturing. Int. J. Adv. Manuf. Technol. 2014, 73 (1–4), 173–183. https://doi.org/10.1007/s00170-014-5808-5.
10 Ren, K.; Chew, Y.; Zhang, Y. F.; Bi, G. J.; Fuh, J. Y. H. Thermal Analyses for Optimal Scanning Pattern Evaluation in Laser Aided Additive Manufacturing. J. Mater. Process. Technol. 2019, 271 (February), 178–188. https://doi.org/10.1016/j.jmatprotec.2019.03.029.
11 Ren, K.; Chew, Y.; Fuh, J. Y. H.; Zhang, Y. F.; Bi, G. J. Thermo-Mechanical Analyses for Optimized Path Planning in Laser Aided Additive Manufacturing Processes. Mater. Des. 2019, 162, 80–93. https://doi.org/10.1016/j.matdes.2018.11.014.
12 Farias, F. W. C.; da Cruz Payão Filho, J.; Moraes e Oliveira, V. H. P. Prediction of the Interpass Temperature of a Wire Arc Additive Manufactured Wall: FEM Simulations and Artificial Neural Network. Addit. Manuf. 2021, 48 (September). https://doi.org/10.1016/j.addma.2021.102387.
13 Malekipour, E.; Valladares, H.; Shin, Y.; El-Mounayri, H. Optimization of Chessboard Scanning Strategy Using Genetic Algorithm in Multi-Laser Additive Manufacturing Process. ASME Int. Mech. Eng. Congr. Expo. Proc. 2020, 2A-2020 (November). https://doi.org/10.1115/IMECE2020-24581.
14 Sun, L.; Ren, X.; He, J.; Zhang, Z. Numerical Investigation of a Novel Pattern for Reducing Residual Stress in Metal Additive Manufacturing. J. Mater. Sci. Technol. 2021, 67, 11–22. https://doi.org/10.1016/j.jmst.2020.05.080.
15 Ramani, K. S.; He, C.; Tsai, Y. L.; Okwudire, C. E. SmartScan: An Intelligent Scanning Approach for Uniform Thermal Distribution, Reduced Residual Stresses and Deformations in PBF Additive Manufacturing. Addit. Manuf. 2022, 52 (February), 102643. https://doi.org/10.1016/j.addma.2022.102643.
16 Zhou, Z.; Shen, H.; Lin, J.; Liu, B.; Sheng, X. Continuous Tool-Path Planning for Optimizing Thermo-Mechanical Properties in Wire-Arc Additive Manufacturing: An Evolutional Method. J. Manuf. Process. 2022, 83 (September), 354–373. https://doi.org/10.1016/j.jmapro.2022.09.009.
17 Incropera, F. P.; Dewitt, D. P. Fundamentals of Heat and Mass Transfer, 3rd ed.; Wiley, 1990.
18 Patankar, S. Numerical Heat Transfer and Fluid Flow; CRC Press: Boca Raton, 1980. https://doi.org/https://doi.org/10.1201/9781482234213.