Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model

Abstract

Determining appropriate process parameters in large-scale laser powder bed fusion (LPBF) additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation. This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time. This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models. Specifically, the stacking model utilized artificial neural network (ANN), gradient boosting regressor, kernel ridge regression, and elastic net as base models, with the Lasso model serving as the meta-model. Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set, resulting in higher predictive accuracy compared to traditional artificial neural network model. The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set, with a coefficient of determination value of 0.944, mean absolute percentage error of 2.51%, and root mean squared error of 27.64, surpassing that of the ANN model. All statistical metrics demonstrate superiority over those obtained from the ANN model. These results confirm that by integrating the base models, the stacking model exhibits superior predictive stability compared to individual base models alone, thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

Graphical abstract

Keywords

machine learning / laser powder bed fusion / ensemble learning / stacking algorithm / additive manufacturing

References

[1] Herzog D, Seyda V, Wycisk E, Emmelmann C. Additive manufacturing of metals. Acta Materialia, 2016, 117: 371–392

[2] ZhouK, Han C J. Metal Powder-Based Additive Manufacturing. Hoboken: Wiley, 2023, 75–159

[3] Ma D, Zhu S D, Zhang K, Zhu L J, Zhou Y. Application and research of titanium alloy oil drill pipes. Journal of Physics: Conference Series, 2023, 2639(1): 012059

[4] Perticarini L, Zanon G, Rossi S M P, Benazzo F M. Clinical and radiographic outcomes of a trabecular titanium^TM acetabular component in hip arthroplasty: results at minimum 5 years follow-up. BMC Musculoskeletal Disorders, 2015, 16(1): 375

[5] Nguyen H D, Pramanik A, Basak A K, Dong Y, Prakash C, Debnath S, Shankar S, Jawahir I S, Dixit S, Buddhi D. A critical review on additive manufacturing of Ti-6Al-4V alloy: microstructure and mechanical properties. Journal of Materials Research and Technology, 2022, 18: 4641–4661

[6] Sui S, Chew Y X, Weng F, Tan C L, Du Z L, Bi G J. Study of the intrinsic mechanisms of nickel additive for grain refinement and strength enhancement of laser aided additively manufactured Ti-6Al-4V. International Journal of Extreme Manufacturing, 2022, 4(3): 035102

[7] Wang S Q, Ma T J, Li W Y, Wen G D, Chen D L. Microstructure and fatigue properties of linear friction welded TC4 titanium alloy joints. Science and Technology of Welding and Joining, 2017, 22(3): 177–181

[8] Lei C J, Ren S, Yin C H, Liu X X, Chen M F, Wu J Z, Han C J. Manipulating melt pool thermofluidic transport in directed energy deposition driven by a laser intensity spatial shaping strategy. Virtual and Physical Prototyping, 2024, 19(1): e2308513

[9] Chen W, Gu D D, Yang J K, Yang Q, Chen J, Shen X F. Compressive mechanical properties and shape memory effect of NiTi gradient lattice structures fabricated by laser powder bed fusion. International Journal of Extreme Manufacturing, 2022, 4(4): 045002

[10] Sufiiarov V S, Popovich A A, Borisov E V, Polozov I A, Masaylo D V, Orlov A V. The effect of layer thickness at selective laser melting. Procedia Engineering, 2017, 174: 126–134

[11] Sheng H S, Xu J H, Zhang S Y, Tan J R, Wang K. Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective decision making. Frontiers of Mechanical Engineering, 2023, 18(2): 21

[12] Wang T, Knap J. Stochastic gradient descent for semilinear elliptic equations with uncertainties. Journal of Computational Physics, 2021, 426: 109945

[13] Exterkate P. Model selection in kernel ridge regression. Computational Statistics & Data Analysis, 2013, 68: 1–16

[14] Gao W P, Wang J S, Zhou L, Luo Q Q, Lao Y H, Lyu H J, Guo S W. Prediction of acute kidney injury in ICU with gradient boosting decision tree algorithms. Computers in Biology and Medicine, 2022, 140: 105097

[15] Zhao Y L, Feng Y L. Learning performance of elastic-net regularization. Mathematical and Computer Modelling, 2013, 57(5–6): 1395–1407

[16] Wu J J, Huang Z, Qiao H C, Wei B X, Zhao Y J, Li J F, Zhao J B. Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network. Journal of Central South University, 2022, 29(10): 3346–3360

[17] Tyagi R, Kumar S, Raza M S, Tripathi A, Das A K. Experimental study of laser cladding process and prediction of process parameters by artificial neural network (ANN). Journal of Central South University, 2022, 29(10): 3489–3502

[18] Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2011, 73(3): 273–282

[19] Schmidt J, Marques M R G, Botti S, Marques M A L. Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 2019, 5(1): 83

[20] Johnson N S, Vulimiri P S, To A C, Zhang X, Brice C A, Kappes B B, Stebner A P. Invited review: machine learning for materials developments in metals additive manufacturing. Additive Manufacturing, 2020, 36: 101641

[21] Wang C, Tan X P, Tor S B, Lim C S. Machine learning in additive manufacturing: state-of-the-art and perspectives. Additive Manufacturing, 2020, 36: 101538

[22] Nguyen D S, Park H S, Lee C M. Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning. Journal of Manufacturing Processes, 2020, 55: 230–235

[23] Rankouhi B, Jahani S, Pfefferkorn F E, Thoma D J. Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters. Additive Manufacturing, 2021, 38: 101836

[24] Luo Y W, Zhang B, Feng X, Song Z M, Qi X B, Li C P, Chen G F, Zhang G P. Pore-affected fatigue life scattering and prediction of additively manufactured inconel 718: an investigation based on miniature specimen testing and machine learning approach. Materials Science and Engineering: A, 2021, 802: 140693

[25] Wolpert D H. Stacked generalization. Neural Networks, 1992, 5(2): 241–259

[26] Zhang H C, Zhu T T. Stacking model for photovoltaic-power-generation prediction. Sustainability, 2022, 14(9): 5669

[27] Kim Y T, Kim B J, Kim S W. Multi-level stacked regression for predicting electricity consumption of hot rolling mill. Expert Systems with Applications, 2022, 201: 117040

[28] Djarum D H, Ahmad Z, Zhang J. Reduced bayesian optimized stacked regressor (RBOSR): a highly efficient stacked approach for improved air pollution prediction. Applied Soft Computing, 2023, 144: 110466

[29] Ribeiro M H D M, da Silva R G, Moreno S R, Mariani V C, Coelho L D S. Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting. International Journal of Electrical Power & Energy Systems, 2022, 136: 107712

[30] da Silva R G, Moreno S R, Ribeiro M H D M, Larcher J H K, Mariani V C, Coelho L D S. Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach. International Journal of Electrical Power & Energy Systems, 2022, 143: 108504

[31] Gadgil K, Gill S S, Abdelmoniem A M. A meta-learning based stacked regression approach for customer lifetime value prediction. Journal of Economy and Technology, 2023, 1: 197–207

[32] Shafighfard T, Bagherzadeh F, Rizi R A, Yoo D Y. Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms. Journal of Materials Research and Technology, 2022, 21: 3777–3794

[33] Zhang Z W, Zhang Y Y, Wen Y T, Ren Y X, Liang X, Cheng J X, Kang M Q. An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress. Computers in Industry, 2023, 151: 103986

[34] Bzdok D, Krzywinski M, Altman N. Machine learning: supervised methods. Nature Methods, 2018, 15(1): 5–6

[35] Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electronic Markets, 2021, 31(3): 685–695

[36] Lu C Y, Shi J. Relative density prediction of additively manufactured inconel 718: a study on genetic algorithm optimized neural network models. Rapid Prototyping Journal, 2022, 28(8): 1425–1436

[37] Otchere D A, Arbi Ganat T O, Gholami R, Ridha S. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: comparative analysis of ANN and SVM models. Journal of Petroleum Science Engineering, 2021, 200: 108182

[38] Fernández A, Clavería I, Pina C, Elduque D. Predictive methodology for quality assessment in injection molding comparing linear regression and neural networks. Polymers, 2023, 15(19): 3915

[39] Jeong S S, Park W K, Joh Y D. Construction of full-view data from limited-view data using artificial neural network in the inverse scattering problem. Applied Sciences, 2022, 12(19): 9801

[40] Maalouf M, Homouz D. Kernel ridge regression using truncated newton method. Knowledge-Based Systems, 2014, 71: 339–344

[41] Otchere D A, Ganat T O A, Ojero J O, Tackie-Otoo B N, Taki M Y. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. Journal of Petroleum Science Engineering, 2022, 208: 109244

[42] Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2005, 67(2): 301–320

[43] Zhao Z B, Wu S M, Qiao B J, Wang S B, Chen X F. Enhanced sparse period-group lasso for bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 2019, 66(3): 2143–2153

[44] Ogunsanya M, Isichei J, Desai S. Grid search hyperparameter tuning in additive manufacturing processes. Manufacturing Letters, 2023, 35: 1031–1042

[45] Bergstra J, Bengio Y. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 2012, 13: 281–305

[46] Friedman M. The use of ranks to avoid the assumption normality implicit in the analysis of variance. Journal of the American Statistical Association, 1937, 32(200): 675–701

RIGHTS & PERMISSIONS

Higher Education Press