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Design Feature Assessment for Fused Deposition Modeling Using Supervised Machine Learning Algorithms

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Recent Advances in Operations Management Applications

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

This paper presents the development of a supervised machine learning model to predict dimensional accuracy for different parts fabricated using fused deposition modeling (FDM) technique. Supervised learning models, namely, polynomial regression, decision trees, and random forest regression were used to predict the overall dimensional accuracy as well as that of individual part features. All three selected algorithms performed satisfactorily when random datasets from existing data were provided for predicting the expected accuracy of the parts produced using FDM, which validates the proposed model. The results also show that the polynomial regression model provided the best results by predicting the dimensions most accurately. The proposed machine learning approach could be further implemented to develop models for recommending appropriate additive manufacturing process parameters for attaining best accuracy.

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Abbreviations

AM:

Additive Manufacturing

ANN:

Artificial Neural Network

BP-NN:

Back Propagation Neural Network

CNN:

Convolutional Neural Network

DEM:

Discrete Element Method

FDM:

Fused Deposition Modeling

FF-NN:

Feed Forward Neural Network

GA:

Genetic Algorithm

LPBF:

Laser Powder Bed Fusion

ML:

Machine Learning

PLA:

Polylactic Acid

RP:

Rapid Prototyping

S/N:

Signal-to-Noise

SVM:

Support Vector Machine

References

  1. Mangat AS, Singh P, Singh S (2017) Investigations on dimensional accuracy of FDM based natural fibre embedded poly-lactic-acid structures. Int J Adv Multidiscip Res (IJAMR), 39–42

    Google Scholar 

  2. Standard Terminology for Additive Manufacturing Technologies. Accessed Oct 04, 2020, https://web.mit.edu/2.810/www/files/readings/AdditiveManufacturingTerminology.pdf

  3. Guo N, Leu MC (2013) Additive manufacturing: technology, applications and research needs. Front Mech Eng 8(3):215–243

    Article  Google Scholar 

  4. 3D Printing with FDM Technology. Accessed Oct 04, 2020, https://www.sculpteo.com/en/materials/fdm-material/

  5. Mohamed OA, Masood SH, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3:42–53

    Article  Google Scholar 

  6. Peng A, Xiao X, Yue R (2014) Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. Int J Adv Manuf Technol 73:87–100

    Article  Google Scholar 

  7. Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Wang CCL, Shin YC, Zhang S, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. Comput Aided Des 69:65–89

    Article  Google Scholar 

  8. Vyavahare S, Teraiya S, Panghal D, Kumar S (2019) Fused deposition modelling: a review. Rapid Prototyping J. https://doi.org/10.1108/RPJ-04-2019-0106

    Article  Google Scholar 

  9. Mani M, Lane B, Donmez A, Feng S, Moylan S, Fesperman R (2015) Measurement science needs for real-time control of additive manufacturing powder bed fusion processes. In: National Institute of Standards and Technology, NIST IR 8036

    Google Scholar 

  10. Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45

    Google Scholar 

  11. Machine Learning. Accessed Oct 06, 2020, http://www.contrib.andrew.cmu.edu/~mndarwis/ML.html

  12. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer

    Google Scholar 

  13. Meng L, McWilliams B, Jarosinski W, Park HY, Jung YG, Lee J, Zhang J (2020) Machine learning in additive manufacturing: a review. JOM

    Google Scholar 

  14. Thrimurthulu K, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44:585–594

    Article  Google Scholar 

  15. Lee BH, Abdullah J, Khan ZA (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169:54–61

    Article  Google Scholar 

  16. Zhang Y, Chou K (2008) A parametric study of part distortions in fused deposition modelling using three-dimensional finite element analysis. Proc Inst Mech Eng, J Eng Manuf 222:959–968

    Article  Google Scholar 

  17. Munguía J, Ciurana J, Riba C (2008) Neural-network-based model for build-time estimation in selective laser sintering. Proc Inst Mech Eng, J Eng Manuf 223:995–1003

    Article  Google Scholar 

  18. Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater Des 30:4243–4252

    Article  Google Scholar 

  19. Nancharaiah T, Raju DR, Raju VR (2010) An experimental investigation on surface quality and dimensional accuracy of FDM components. Int J Emerg Technol 1(2):106–111

    Google Scholar 

  20. Jinwen Z, Anhua P (2012) Process-parameter optimization for fused deposition modeling based on Taguchi method. Adv Mater Res 538–541:444–447. https://doi.org/10.4028/www.scientific.net/AMR.538-541.444

  21. Garg A, Tai K (2014) An ensemble approach of machine learning in evaluation of mechanical property of the rapid prototyping fabricated prototype. Appl Mech Mater 575:493–496

    Article  Google Scholar 

  22. Torres J, Cotelo J, Karl J, Gordon AP (2015) Mechanical property optimization of FDM PLA in shear with multiple objectives. JOM 67

    Google Scholar 

  23. Akande SO (2015) Dimensional accuracy and surface finish optimization of fused deposition modelling parts using desirability function analysis. Int J Eng Res Technol (IJERT) 4:196–202

    Google Scholar 

  24. Jaya Christiyan KG, Chandrasekhar U, Venkateswarlu K (2016) A study on the influence of process parameters on the mechanical properties of 3D printed ABS composite. In: IOP conference series: materials science and engineering, vol 114. https://doi.org/10.1088/1757-899X/114/1/012109

  25. Ling J, Hutchinson M, Antono E, DeCost B, Holm EA, Meredig B (2017) Building data-driven models with microstructural images: generalization and interpretability. Mater Discovery 10:19–28

    Article  Google Scholar 

  26. Zhang W, Mehta A, Desai PS, Higgs III CF (2017) Machine learning enabled powder spreading process map for metal additive manufacturing (AM). In: Proceedings of the 28th annual international conference

    Google Scholar 

  27. Liu C, Roberson D, Kong Z (2017) Textural analysis-based online closed-loop quality control for additive manufacturing processes. In: Industrial and systems engineering conference

    Google Scholar 

  28. Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S (2018) Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf 21:517–528

    Google Scholar 

  29. Imani F, Montazeri M, Gaikwad A, Rao P, Yang H, Reutzel EW (2018) Layerwise in-process quality monitoring in laser powder bed fusion. In: ASME, 13th international manufacturing science and engineering conference

    Google Scholar 

  30. Géron A (2017) Hands-on machine learning with Scikit-learn and TensorFlow, 1st edn

    Google Scholar 

  31. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. https://doi.org/10.1007/978-1-4614-7138-7

  32. Bratsas C, Koupidis K, Salanova JM, Giannakopoulos K, Kaloudis A, Aifadopoulou G (2020) A comparison of machine learning methods for the prediction of traffic speed in urban places. MDPI J Sustain 12:142. https://doi.org/10.3390/su12010142

    Article  Google Scholar 

  33. The 7 steps of machine learning. Accessed Oct 08, 2020, https://towardsdatascience.com/the-7-steps-of-machine-learning-2877d7e5548e

  34. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific model development

    Google Scholar 

  35. Root-mean-square error in R programming. Accessed Oct 08, 2020, https://www.geeksforgeeks.org/root-mean-square-error-in-r-programming/

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India

    Rahul Bansal & Sukhdeep Singh Dhami

  2. Department of Mechanical Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

    Jatinder Madan

Authors
  1. Rahul Bansal
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  2. Sukhdeep Singh Dhami
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  3. Jatinder Madan
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Corresponding author

Correspondence to Sukhdeep Singh Dhami .

Editor information

Editors and Affiliations

  1. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anish Sachdeva

  2. Department of Mechanical Engineering, Indian Institute of Technology-Roorkee, Uttarakhand, India

    Pradeep Kumar

  3. Reliability & Maintainability Engineering, North Dakota State University, Fargo, ND, USA

    O. P. Yadav

  4. Department of Industrial and Production Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Mohit Tyagi

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Bansal, R., Dhami, S.S., Madan, J. (2022). Design Feature Assessment for Fused Deposition Modeling Using Supervised Machine Learning Algorithms. In: Sachdeva, A., Kumar, P., Yadav, O.P., Tyagi, M. (eds) Recent Advances in Operations Management Applications. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7059-6_20

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  • DOI: https://doi.org/10.1007/978-981-16-7059-6_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7058-9

  • Online ISBN: 978-981-16-7059-6

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