Skip to main content
SpringerLink
Log in

Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Felice IO, Shen J, Barragan AF, Moura IA, Li B, Wang B et al (2023) Wire and arc additive manufacturing of Fe-based shape memory alloys: microstructure, mechanical and functional behavior. Mater Des 231:112004. https://doi.org/10.1016/j.matdes.2023.112004

    Article  Google Scholar 

  2. Marques DA, Oliveira JP, Baptista AC (2023) A short review on the corrosion behaviour of wire and arc additive manufactured materials. Metals 13(4):641. https://doi.org/10.3390/met13040641

    Article  Google Scholar 

  3. Hamilton RF, Bimber BA, Palmer TA (2018) Correlating microstructure and superelasticity of directed energy deposition additive manufactured Ni-rich NiTi alloys. J Alloys Compd 739:712–722. https://doi.org/10.1016/j.jallcom.2017.12.270

    Article  Google Scholar 

  4. Wang C, Tan XP, Du Z, Chandra S, Sun Z, Lim CWJ et al (2019) Additive manufacturing of NiTi shape memory alloys using pre-mixed powders. J Mater Process Technol 271:152–161. https://doi.org/10.1016/j.jmatprotec.2019.03.025

    Article  Google Scholar 

  5. Li B, Wang L, Wang B, Li D, Oliveira JP, Cui R et al (2022) Electron beam freeform fabrication of NiTi shape memory alloys: crystallography, martensitic transformation, and functional response. Mater Sci Eng A 843:143135. https://doi.org/10.1016/j.msea.2022.143135

    Article  Google Scholar 

  6. Sing SL, Yeong WY (2020) Laser powder bed fusion for metal additive manufacturing: perspectives on recent developments. Virtual Phys Prototyp 15(3):359–370. https://doi.org/10.1080/17452759.2020.1779999

    Article  Google Scholar 

  7. Liverani E, Toschi S, Ceschini L, Fortunato A (2017) Effect of selective laser melting (SLM) process parameters on microstructure and mechanical properties of 316L austenitic stainless steel. J Mater Process Technol 249:255–263. https://doi.org/10.1016/j.jmatprotec.2017.05.042

    Article  Google Scholar 

  8. Liu Y, Li J, Xu K, Cheng T, Zhao D, Li W et al (2022) An optimized scanning strategy to mitigate excessive heat accumulation caused by short scanning lines in laser powder bed fusion process. Addit Manuf 60:103256. https://doi.org/10.1016/j.addma.2022.103256

    Article  Google Scholar 

  9. Tucho WM, Lysne VH, Austbø H, Sjolyst-Kverneland A, Hansen V (2018) Investigation of effects of process parameters on microstructure and hardness of SLM manufactured SS316L. J Alloys Compd 740:910–925. https://doi.org/10.1016/j.jallcom.2018.01.098

    Article  Google Scholar 

  10. Giganto S, Zapico P, Castro-Sastre MÁ, Martínez-Pellitero S, Leo P, Perulli P (2019) Influence of the scanning strategy parameters upon the quality of the SLM parts. Procedia Manuf 41:698–705. https://doi.org/10.1016/j.promfg.2019.09.060

    Article  Google Scholar 

  11. Liu S, Yang W, Shi X, Li B, Duan S, Guo H, Guo J (2019) Influence of laser process parameters on the densification, microstructure, and mechanical properties of a selective laser melted AZ61 magnesium alloy. J Alloys Compd 808:151160. https://doi.org/10.1016/j.jallcom.2019.06.261

    Article  Google Scholar 

  12. Yang J, Zhu Q, Wang Z, Xiong F, Li Q, Yang F et al (2023) Effects of metallurgical defects on magnetic properties of SLM NiFeMo permalloy. Mater Charact 197:112672. https://doi.org/10.1016/j.matchar.2023.112672

    Article  Google Scholar 

  13. Liu W, Chen C, Shuai S, Zhao R, Liu L, Wang X et al (2020) Study of pore defect and mechanical properties in selective laser melted Ti6Al4V alloy based on X-ray computed tomography. Mater Sci Eng A 797:139981. https://doi.org/10.1016/j.msea.2020.139981

    Article  Google Scholar 

  14. Gong H, Rafi K, Gu H, Ram GJ, Starr T, Stucker B (2015) Influence of defects on mechanical properties of Ti–6Al–4 V components produced by selective laser melting and electron beam melting. Mater Des 86:545–554. https://doi.org/10.1016/j.matdes.2015.07.147

    Article  Google Scholar 

  15. Oliveira JP, LaLonde AD, Ma J (2020) Processing parameters in laser powder bed fusion metal additive manufacturing. Mater Des 193:108762. https://doi.org/10.1016/j.matdes.2020.108762

    Article  Google Scholar 

  16. du Plessis A (2019) Effects of process parameters on porosity in laser powder bed fusion revealed by X-ray tomography. Addit Manuf 30:100871. https://doi.org/10.1016/j.addma.2019.100871

    Article  Google Scholar 

  17. Wang D, Song C, Yang Y, Bai Y (2016) Investigation of crystal growth mechanism during selective laser melting and mechanical property characterization of 316L stainless steel parts. Mater Des 100:291–299. https://doi.org/10.1016/j.matdes.2016.03.111

    Article  Google Scholar 

  18. Leicht A, Rashidi M, Klement U, Hryha E (2020) Effect of process parameters on the microstructure, tensile strength and productivity of 316L parts produced by laser powder bed fusion. Mater Charact 159:110016. https://doi.org/10.1016/j.matchar.2019.110016

    Article  Google Scholar 

  19. Chang TW, Liao KW, Lin CC, Tsai MC, Cheng CW (2021) Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning. Int. J. Adv. Manuf. Technol 114:3177–3184. https://doi.org/10.1007/s00170-021-07037-y

    Article  Google Scholar 

  20. Gor M, Dobriyal A, Wankhede V, Sahlot P, Grzelak K, Kluczyński J, Łuszczek J (2022) Density prediction in powder bed fusion additive manufacturing: machine learning-based techniques. Appl Sci 12(14):7271. https://doi.org/10.3390/app12147271

    Article  Google Scholar 

  21. Nguyen DS, Park HS, Lee CM (2020) Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning. J Manuf Process 55:230–235. https://doi.org/10.1016/j.jmapro.2020.04.014

    Article  Google Scholar 

  22. Lee AC, Huang RY, Nguyen TD, Cheng CW, Tsai MC (2020) Laser powder bed fusion of multilayer thin-walled structures based on data-driven model. J Laser Micro Nanoeng 15(1):1–7. https://doi.org/10.2961/jlmn.2020.01.2007

    Article  Google Scholar 

  23. Akhil V, Raghav G, Arunachalam N, Srinivas DS (2020) Image data-based surface texture characterization and prediction using machine learning approaches for additive manufacturing. J Comput Inf Sci Eng 20(2):021010. https://doi.org/10.1115/1.4045719

    Article  Google Scholar 

  24. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621. https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  25. Larasati DA (2021) Application of the K-NN method and GLCM feature extraction in classifying formalin fish images. J Res Comput Sci 1(1):1–13

    Google Scholar 

  26. Singh D, Kaur K (2012) Classification of abnormalities in brain MRI images using GLCM, PCA and SVM. Int J Eng Adv Technol 1(6):243–248

    Google Scholar 

  27. Raut MA, Patil MMA, Dhondrikar MCP, Kamble MSD (2016) Texture parameters extraction of satellite image. Int J Sci Technol Eng 2(11):13–18

    Google Scholar 

  28. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W et al (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30:3149–3157

    Google Scholar 

  29. Mikler CV, Chaudhary V, Borkar T, Soni V, Choudhuri D, Ramanujan RV, Banerjee R (2017) Laser additive processing of Ni-Fe-V and Ni-Fe-Mo permalloys: microstructure and magnetic properties. Mater Lett 192:9–11. https://doi.org/10.1016/j.matlet.2017.01.059

    Article  Google Scholar 

  30. Karna SK, Sahai R (2012) An overview on Taguchi method. Int J Eng Math Sci 1(1):1–7

    Google Scholar 

  31. Cannizzaro D, Varrella AG, Paradiso S, Sampieri R, Macii E, Patti E, Di Cataldo S (2021) Image analytics and machine learning for in-situ defects detection in Additive Manufacturing. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 603-608). IEEE. https://doi.org/10.23919/DATE51398.2021.9474175

  32. Devich RN, Weinhaus FM (1980) Image perspective transformations. In Image Processing for Missile Guidance (Vol. 238, pp. 322-333). SPIE. https://doi.org/10.1117/12.959162

  33. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804. https://doi.org/10.1109/PROC.1979.11328

    Article  Google Scholar 

  34. Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69(6):066138. https://doi.org/10.1103/PhysRevE.69.066138

    Article  MathSciNet  Google Scholar 

  35. Su X, Yan X, Tsai CL (2012) Linear regression. Wiley Interdiscip Rev Comput Stat 4(3):275–294. https://doi.org/10.1002/wics.1198

    Article  Google Scholar 

  36. Awad M, Khanna R, Awad M, Khanna R (2015) Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers, 67-80. https://doi.org/10.1007/978-1-4302-5990-9_4

  37. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). https://doi.org/10.1145/2939672.2939785

  38. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Financial support for this study was provided by the National Science and Technology Council (NSTC), Taiwan, under Grant NSTC 112-2218-E-006-018 and NSTC 112-2221-E-006-116-MY3.

Funding

This work was supported by the National Science and Technology Council (NSTC), Taiwan, under Grant NSTC 112-2218-E-006-018 and NSTC 112-2221-E-006-116-MY3.

Author information

Author notes

  1. Lien-Kai Chang, Ri-Sheng Chen, Mi-Ching Tsai, Rong-Mao Lee, Ching-Chih Lin, Jhih-Cheng Huang and Tsung-Wei Chang contributed equally to this work.

Authors and Affiliations

  1. Electrical Motor Technology Research Center, National Cheng Kung University, Tainan, 700, Taiwan

    Lien-Kai Chang, Mi-Ching Tsai, Jhih-Cheng Huang & Tsung-Wei Chang

  2. Department of Mechanical Engineering, National Cheng Kung University, Tainan, 700, Taiwan

    Lien-Kai Chang, Ri-Sheng Chen, Mi-Ching Tsai, Ching-Chih Lin, Jhih-Cheng Huang & Tsung-Wei Chang

  3. Department of Intelligent Automation Engineering, National Chin-Yi University of Technology, Taichung, 411, Taiwan

    Rong-Mao Lee

  4. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 700, Taiwan

    Ming-Huwi Horng

  5. Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, 700, Taiwan

    Ming-Huwi Horng

Authors
  1. Lien-Kai Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ri-Sheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mi-Ching Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rong-Mao Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ching-Chih Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jhih-Cheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tsung-Wei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ming-Huwi Horng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Conceptualization, writing—original draft, and methodology were done by Lien-Kai Chang, Ri-Sheng Chen, Ming-Huwi Horng, and Mi-Ching Tsai. Writing—review and editing, data curation, and formal analysis were performed by Ri-Sheng Chen and Jhih-Cheng Huang. Resources and funding acquisition were performed by Mi-Ching Tsai and Ming-Huwi Horng. Review, editing, and validation were performed by Ri-Sheng Chen, Rong-Mao Lee, Ching-Chih Lin, and Tsung-Wei Chang. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ming-Huwi Horng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, LK., Chen, RS., Tsai, MC. et al. Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction. Int J Adv Manuf Technol 132, 83–98 (2024). https://doi.org/10.1007/s00170-024-13165-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13165-y

Keywords

Search

Navigation