Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing
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Wire and arc additive manufacturing (WAAM) is a novel rapid prototyping process that employs gas tungsten arc welding, controlled by a robot, to build complex 3D parts by successive layer deposition technique. Experimental studies on WAAM are useful for understanding the physics of the process however the quantification and optimization of process parameters is difficult due to complex mechanisms involved in WAAM process. In this present work, the measurement of two bead dimensions (bead height and bead width) based on the three inputs (peak current, wire feed speed, and travel speed) is done using the gas tungsten arc welding machine. Experimental study is followed by proposition of two variants of advanced evolutionary algorithms (gene expression programming and multi-gene genetic programming) in formulation of the functional expressions for the two bead dimensions based on the three inputs. The performance analysis of the two proposed models is conducted based on the four statistical error metrics, hypothesis tests and cross-validation. The relationships extracted between the bead dimensions and the three inputs reveals that the peak current influences both the bead height and bead width simultaneously. The findings reported will have a positive implication on the industry in predictive monitoring of the bead dimensions during the WAAM process.
KeywordsWire and arc additive manufacturing Bead geometry Multi-gene genetic programming Modelling
This study was supported by Shantou University Scientific Research Funded Project (Grant No. NTF 16002)
- Baufeld, B., Brandl, E., & van der Biest, O. (2011). Wire based additive layer manufacturing: Comparison of microstructure and mechanical properties of Ti6Al4V components fabricated by laser-beam deposition and shaped metal deposition. The Journal of Materials Processing Technology, 211(6), 1146–1158.CrossRefGoogle Scholar
- Ferreira C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. arXiv preprint arXiv:cs/0102027.
- Garg, A., & Tai, K. (2012). Comparison of Regression Analysis, Artificial Neural Network and Genetic Programming in Handling the Multicollinearity Problem, In Proceedings of International Conference on Modelling, Identification & Control (ICMIC2012), Wuhan, China, 24–26 (pp.353-358).Google Scholar
- Garg, A., Lam, Jasmine Siu Lee, & Savalani, M.M. (2015) Laser power based surface characteristics models for 3-D printing process. Journal of Intelligent Manufacturing, pp. 1–12. doi: 10.1007/s10845-015-1167-9
- Geng, Haibin, et al. (2015). A prediction model of layer geometrical size in wire and arc additive manufacture using response surface methodology. The International Journal of Advanced Manufacturing Technology, pp. 1–12. doi: 10.1007/s00170-015-8147-2
- GEPSOFT, GeneXproTools, Version 5.0, http://www.gepsoft.com (2014).
- Koza, J. R. (1994). Genetic programming II: Automatic discovery of reusable programs. Cambridge, USA: MIT.Google Scholar
- Liang, C., Li, M., Lu, B., Gu, T., Jo, J., & Ding, Y. (2015). Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm. Journal of Intelligent Manufacturing, 1–9, doi: 10.1007/s10845-015-1035-7.
- Panda, B. N., Bahubalendruni, M. V. A. Raju, & Biswal, B. B. (2014). A general regression neural network approach for the evaluation of compressive strength of FDM prototypes. Neural Computing and Applications, 26(5), 1–8.Google Scholar
- Panda, B., Garg, A., Jian, Z., Heidarzadeh, A., & Gao, L. (2016a). Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed. Frontiers of Mechanical Engineering, 11(3), 289–298.Google Scholar
- Panda, B.N., Bahubalendruni, R.M., Biswal, B.B., & Leite, M., (2016b) A CAD-based approach for measuring volumetric error in layered manufacturing. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, doi: 10.1177/0954406216634746.
- Panda, B.N., Shankhwar, K., Garg, A., & Jian, Z. (2016c). Performance evaluation of warping characteristic of fused deposition modelling process. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-016-8914-8.
- Rao, K. V., & Murthy, P. B. G. S. N. (2016). Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. Journal of Intelligent Manufacturing, 1–11, doi: 10.1007/s10845-016-1197-y.
- Sharma, N., Kumar, K., Raj, T., & Kumar, V. (2016) Porosity exploration of SMA by Taguchi, regression analysis and genetic programming. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-016-1236-8.
- Vijayaraghavan, V. et al (2014) Density characteristics of laser-sintered three-dimensional printing parts investigated by using an integrated finite element analysis–based evolutionary algorithm approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture (Imeche), doi: 10.1177/0954405414558131
- Wang, L., Yang, B., Wang, S., & Liang, Z. (2015a). Building image feature kinetics for cement hydration using gene expression programming with similarity weight tournament selection. IEEE Transactions on Evolutionary Computation, 19(5), 679–693.Google Scholar
- Wang, P., Emmerich, M., Li, R., Tang, K., Back, T., & Yao, X. (2015b). Convex hull-based multiobjective genetic programming for maximizing receiver operating characteristic performance. IEEE Transactions on Evolutionary Computation, 19(2), 188–200.Google Scholar