Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing

  • Biranchi Panda
  • K. Shankhwar
  • Akhil Garg
  • M. M. Savalani


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.


Wire 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)


  1. 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
  2. Brandl, E., Achim, S., & Christoph, L. (2012). Morphology, microstructure, and hardness of titanium (Ti-6Al-4V) blocks deposited by wire-feed additive layer manufacturing (ALM). Materials Science and Engineering: A, 532, 295–307.CrossRefGoogle Scholar
  3. Çiçek, A., Kıvak, T., & Ekici, E. (2015). Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing, 26(2), 295–305.CrossRefGoogle Scholar
  4. Ding, D., et al. (2016). Automatic multi-direction slicing algorithms for wire based additive manufacturing. Robotics and Computer-Integrated Manufacturing, 37, 139–150.CrossRefGoogle Scholar
  5. Ding, D., Pan, Z., Cuiuri, D., & Li, H. (2015). A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robotics and Computer-Integrated Manufacturing, 31, 101–110.CrossRefGoogle Scholar
  6. Ferreira C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. arXiv preprint arXiv:cs/0102027.
  7. 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
  8. Garg, A., et al. (2014). A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance. Swarm and Evolutionary Computation, 21, 54–63.CrossRefGoogle Scholar
  9. 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
  10. 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
  11. GEPSOFT, GeneXproTools, Version 5.0, http://www.gepsoft.com (2014).
  12. Gibson, Ian, Rosen, David W., & Stucker, Brent. (2010). Additive manufacturing technologies (Vol. 238). New York: Springer.CrossRefGoogle Scholar
  13. Kazanas, P., Deherkar, P., Almeida, P., et al. (2012). Fabrication of geometrical features using wire and arc additive manufacture. Journal of Engineering Manufacture, 226(6), 1042–1051.CrossRefGoogle Scholar
  14. Koza, J. R. (1994). Genetic programming II: Automatic discovery of reusable programs. Cambridge, USA: MIT.Google Scholar
  15. 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.
  16. Mahapatra, S. S., & Panda, B. N. (2013). Benchmarking of rapid prototyping systems using grey relational analysis. International Journal of Services and Operations Management, 16(4), 460–477.CrossRefGoogle Scholar
  17. Nie, L., Gao, L., Li, P., & Li, X. (2013). A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. Journal of Intelligent Manufacturing, 24(4), 763–774.CrossRefGoogle Scholar
  18. Oshima, K., Xiang, X., & Yamane, S. (2005). Effects of power source characteristic on \(\text{ CO }_{2}\) short circuiting arc welding. Science and Technology of Welding and Joining, 10(3), 281–286.CrossRefGoogle Scholar
  19. Ouyang, J. H., Wang, H., & Kovacevic, R. (2002). Rapid prototyping of 5356-aluminum alloy based on variable polarity gas tungsten arc welding: process control and microstructure. Materials and Manufacturing Processes, 17(1), 103–124.CrossRefGoogle Scholar
  20. 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
  21. 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
  22. 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.
  23. 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.
  24. 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.
  25. Sabar, N. R., Ayob, M., Kendall, G., & Qu, R. (2015). Automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Transactions on Evolutionary Computation, 19(3), 309–325.CrossRefGoogle Scholar
  26. 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.
  27. Sohrabpoor, H., et al. (2016). Analysis of laser powder deposition parameters: ANFIS modeling and ICA optimization. Optik-International Journal for Light and Electron Optics, 127(8), 4031–4038.CrossRefGoogle Scholar
  28. Tay, Y. W., et al. (2016). Processing and properties of construction materials for 3D printing. Materials Science Forum, 861, 177–181.CrossRefGoogle Scholar
  29. 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
  30. Vijayaraghavan, V., Garg, A., Lam, J. S. L., Panda, B., & Mahapatra, S. S. (2015). Process characterization of 3D-printed FDM components using improved evolutionary computational approach. The International Journal of Advanced Manufacturing Technology, 78(5–8), 781–793.CrossRefGoogle Scholar
  31. Wang, F., Williams, S., & Rush, M. (2011). Morphology investigation on direct current pulsed gas tungsten arc welded additive layer manufactured Ti6Al4V alloy. The International Journal of Advanced Manufacturing Technology, 57(5–8), 597–603.CrossRefGoogle Scholar
  32. 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
  33. 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

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Biranchi Panda
    • 1
  • K. Shankhwar
    • 2
  • Akhil Garg
    • 3
  • M. M. Savalani
    • 4
  1. 1.IDMEC, Instituto Superior TécnicoUniversidade de LisboaLisboaPortugal
  2. 2.Department of Mechanical EngineeringKalinga Institute of Industrial TechnologyBhubaneswarIndia
  3. 3.Department of Mechatronics EngineeringShantou UniversityShantouChina
  4. 4.Department of Industrial and Systems EngineeringHongKong Polytechnic UniversityKowloonHongkong

Personalised recommendations