Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy
Source abstract
Abstract
Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, The University of Memphis, Memphis, TN 38152, USA
Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152, USA
References (63)
Show source-provided references
1. A Comprehensive Study on Microstructure and Tensile Behaviour of a Selectively Laser Melted Stainless Steel10.1038/s41598-018-26136-7 ↗
2. Charles, A., Elkaseer, A., Thijs, L., Hagenmeyer, V., and Scholz, S.G. (2019). Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting. Appl. Sci., 9.10.3390/app9061256 ↗
3. Optimisation of Selective Laser Melting Parameters for the Ni-Based Superalloy IN-738 LC Using Doehlert’s Design10.1108/RPJ-04-2016-0063 ↗
4. Effect of Process Parameters on the Surface Roughness of Overhanging Structures in Laser Powder Bed Fusion Additive Manufacturing10.1016/j.procir.2016.02.347 ↗
5. Chen, Z. (2020). Understanding of the Modeling Method in Additive Manufacturing. IOP Conf. Ser. Mater. Sci. Eng., 711.10.1088/1757-899X/711/1/012017 ↗
6. Design of Experiments10.1136/bmj.1.3923.554-a ↗
7. Optimization of Double Diffusive Mixed Convection in a Bfs Channel Filled With Alumina Nanoparticle Using Taguchi Method and Utility Concept10.1038/s41598-019-55897-y ↗
8. Optimization of Parameters in Laser Powder Deposition AlSi10Mg Alloy Using Taguchi Method10.1016/j.optlastec.2018.10.030 ↗
9. Optimisation of Process Parameters for Deposition of Colmonoy Using Directed Energy Deposition Process10.1016/j.matpr.2020.02.222 ↗
10. Parametric Optimization of Laser Additive Manufacturing of Inconel 625 Using Taguchi Method and Grey Relational Analysis10.1155/2020/9176509 ↗
11. Cherkia, H., Kar, S., Singh, S.S., and Satpathy, A. (2020). Fused Deposition Modelling and Parametric Optimization of ABS-M30. Advances in Materials and Manufacturing Engineering, Springer.10.1007/978-981-15-1307-7_1 ↗
12. Dontsov, Y.V., Panin, S., Buslovich, D.G., and Berto, F. (2020). Taguchi Optimization of Parameters for Feedstock Fabrication and FDM Manufacturing of Eear-Resistant UHMWPE-Based Composites. Materials, 13.10.3390/ma13122718 ↗
13. Factor Analysis of Selective Laser Melting Process Parameters With Normalised Quantities and Taguchi Method10.1016/j.optlastec.2019.105592 ↗
14. Taguchi Optimization of the Surface Finish Obtained by Laser Ablation on Selective Laser Molten Steel Parts10.1016/j.procir.2013.09.079 ↗
15. Influence of Process Parameters on Surface Roughness of Aluminum Parts Produced by DMLS10.1007/s00170-012-4688-9 ↗
16. Experimental Study for Effects of Process Parameters of Selective Laser Sintering for alsi10mg
17. Porosity Content Control of CoCrMo and Titanium Parts by Taguchi Method Applied to Selective Laser Melting Process Parameter10.1108/RPJ-09-2013-0092 ↗
18. Sathish, S., Anandakrishnan, V., Dillibabu, V., Muthukannan, D., and Balamuralikrishnan, N. (2019). Optimization of Coefficient of Friction for Direct Metal Laser Sintered Inconel 718. Adv. Manuf. Technol., 371–379.10.1007/978-981-13-6374-0_43 ↗
19. Investigation of Electrochemical Post-Processing Procedure for Ti-6AL-4V Lattice Structure Manufactured by Direct Metal Laser Sintering (DMLS)10.1007/s00170-019-03996-5 ↗
20. Optimization of Direct Metal Printing Process Parameters for Plastic Injection Mold With Both Gas Permeability and Mechanical Properties Using Design of Experiments Approach10.1007/s00170-020-05724-w ↗
21. Fractional Factorial Design10.1002/wics.27 ↗
22. Parametric Optimization of Laser Deposited High Entropy Alloys Using Response Surface Methodology (RSM)10.1007/s00170-020-05781-1 ↗
23. Statistical Modelling and Optimization of Clad Characteristics in Laser Metal Deposition of Austenitic Stainless Steel10.1007/s40430-019-1784-x ↗
24. Selective Laser Melting of ALSi10Mg Alloy: Process Optimisation and Mechanical Properties Development10.1016/j.matdes.2014.09.044 ↗
25. Optimization of SLM Process Parameters for Ti6Al4V Medical Implants10.1108/RPJ-05-2018-0112 ↗
26. Application of Fuzzy Integrated JAYA Algorithm for the Optimization of Surface Roughness of DMLS Made Specimen: Comparison with GA10.1007/978-981-13-8196-6_13 ↗
27. Predictive Models for Physical and Mechanical Properties of Ti6Al4V Produced by Selective Laser Melting10.1016/j.msea.2016.03.113 ↗
28. On the Effect of Process Parameters on Properties of AlSi10Mg Parts Produced by DMLS10.1108/RPJ-03-2013-0028 ↗
29. Design of Experiments Approach in AZ31 Powder Selective Laser Melting Process Optimization10.1016/j.acme.2016.07.007 ↗
30. Data-Driven Stochastic Optimization on Manifolds for Additive Manufacturing10.1016/j.commatsci.2020.109750 ↗
31. Process Optimization and Mechanical Properties of Oxide Dispersion Strengthened Nickel-Based Superalloy by Selective Laser Melting10.1016/j.matdes.2019.108418 ↗
32. Automatic Fault Detection for Laser Powder-Bed Fusion Using Semi-Supervised Machine Learning
33. Optimization of Selective Laser Melting Process Parameters for Ti-6Al-4V Alloy Manufacturing Using Deep Learning10.1016/j.jmapro.2020.04.014 ↗
34. Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives10.1016/j.eng.2019.04.012 ↗
35. Experimental Investigation and Empirical Modelling of FDM Process for Compressive Strength Improvement10.1016/j.jare.2011.05.001 ↗
36. Learning Representations by Back-Propagating Errors10.1038/323533a0 ↗
37. Extraction and Evaluation of Melt Pool, Plume and Spatter Information for Powder-Bed Fusion AM Process Monitoring10.1016/j.matdes.2018.07.002 ↗
38. Paul, A., Mozaffar, M., Yang, Z., Liao, W.-K., Choudhary, A., Cao, J., and Agrawal, A. (2019, January 5–8). A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes. Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA.10.1109/DSAA.2019.00069 ↗
39. Machine Learning-Based Image Processing for on-Line Defect Recognition in Additive Manufacturing10.1016/j.cirp.2019.03.021 ↗
40. Acoustic Emission for in Situ Quality Monitoring in Additive Manufacturing Using Spectral Convolutional Neural Networks
41. Ali, T.K., and Balasubramanian, E. (2020). Study on Compressive Strength Characteristics of Selective Inhibition Sintered UHMWPE Specimens Based on ANN and RSM Approach. CIRP J. Manuf. Sci. Technol.10.1016/j.cirpj.2020.05.016 ↗
42. Experimental Investigating and Numerical Simulations of the Thermal Behavior and Process Optimization for Selective Laser Sintering of PA610.1016/j.jmapro.2020.04.080 ↗
43. Guo, Y., Lu, W.F., and Fuh, J. (2020). Semi-Supervised Deep Learning Based Framework for Assessing Manufacturability of Cellular Structures in Direct Metal Laser Sintering Process. J. Intell. Manuf., 1–13.10.1007/s10845-020-01575-0 ↗
44. High Cycle Fatigue Life Prediction of Laser Additive Manufactured Stainless Steel: A Machine Learning Approach10.1016/j.ijfatigue.2019.105194 ↗
45. Hatch Pattern Based Inherent Strain Prediction Using Neural Networks for Powder Bed Fusion Additive Manufacturing10.1016/j.jmapro.2020.04.030 ↗
46. 3D Printing Optimization Algorithm Based on Back-Propagation Neural Network
47. A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)10.1016/j.promfg.2019.06.214 ↗
48. Systematic Approach for Determining Optimal Processing Parameters to Produce Parts With High Density in Selective Laser Melting Process10.1007/s00170-019-04517-0 ↗
49. Optimized Hatch Space Selection in Double-Scanning Track Selective Laser Melting Process10.1007/s00170-019-04456-w ↗
50. Modelling and Optimization of Laser Engraving Qualitative Characteristics of Al-SiC Composite Using Response Surface Methodology and Artificial Neural Networks10.1016/j.optlastec.2018.10.058 ↗
51. A Prediction Model for Finding the Optimal Laser Parameters in Additive Manufacturing of NiTi Shape Memory Alloy10.1007/s00170-019-04596-z ↗
52. Modelling of Laser Powder Bed Fusion Process and Analysing the Effective Parameters on Surface Characteristics of Ti-6Al-4V10.1016/j.ijmecsci.2019.105299 ↗
53. Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing10.1115/1.4045719 ↗
54. Experimental Investigation and Analysis of Dimensional Accuracy of Laser-Based Powder Bed Fusion Made Specimen by Application of Response Surface Methodology10.1007/s40964-019-00076-8 ↗
55. Process-Property-Geometry Correlations for Additively-Manufactured Ti–6Al–4V Sheets10.1016/j.msea.2019.06.020 ↗
56. Size Effects on Geometrical Accuracy for Additive Manufacturing of Ti-6Al-4V ELI Parts10.1007/s00170-019-04184-1 ↗
57. ASTM B311-17 (2017). Standard Test Method for Density of Powder Metallurgy (PM) Materials Containing Less Than Two Percent Porosity, ASTM International. Available online: www.astm.org.
58. Wu, C.-F., and Hamada, M. (2009). Experiments: Planning, Analysis, and Optimization, Wiley.
59. Beale, H.D., Demuth, H.B., and Hagan, M.T. (1996). Neural Network Design, Pws.
60. The ASA Statement on p-Values: Context, Process, and Purpose10.1080/00031305.2016.1154108 ↗
61. Additive Manufacturing of Metallic Components–Process, Structure and Properties10.1016/j.pmatsci.2017.10.001 ↗
62. Parametric Optimization of Selective Laser Melting for Forming Ti6Al4V Samples by Taguchi Method10.1016/j.optlastec.2012.12.002 ↗
63. A Review on Melt-Pool Characteristics in Laser Welding of Metals10.1155/2018/4920718 ↗