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Soft computing-based process optimization in laser metal deposition of Ti-6Al-4 V

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Abstract

Parameter optimization is significant to a successful laser metal deposition (LMD). While conventional optimization methods have been used, the prowess of soft computing techniques is still less explored in LMD towards ensuring reduced experimental costs and throughput. This study develops a process optimization and wear volume prediction model for Ti-6Al-4 V using soft computing techniques. The particle swarm optimization (PSO) model was used to optimize a single objective function to determine optimal process parameters. A supervised learning model using artificial neural network (ANN) was developed to predict the wear volume from known process parameters. The model hyperparameters were tuned by several trials until optimal parameters were obtained. The ANN model was trained and tested with 70% and 30% of the dataset, respectively. The ANN model was evaluated using known statistical performance metrics and user-friendly interfaces, where process optimization can be carried out within upper and lower design bounds, which were developed for the two intelligent models. From the model evaluation result, a root mean square error (RMSE) of 0.0052, mean absolute deviation (MAD) of 0.0031, coefficient of determination (R2) of 0.9733, and a mean absolute percentage error (MAPE) of 14.0152 was obtained from the model testing phase. Overall, soft computing techniques prove helpful in ensuring process integrity, efficient, and cost-effective LMD.

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Availability of data and material

The data for this study is available upon reasonable request.

Code availability

The code developed for the soft computing techniques used in this article is available upon reasonable request.

References

  1. Ahuett-Garza H, Kurfess T (2018) A brief discussion on the trends of habilitating technologies for Industry 4.0 and smart manufacturing. Manuf Lett 15:60–63. https://doi.org/10.1016/j.mfglet.2018.02.011

  2. Tamez MBA, Taha I (2021) A review of additive manufacturing technologies and markets for thermosetting resins and their potential for carbon fiber integration. Addit Manuf 37:101748. https://doi.org/10.1016/j.addma.2020.101748

    Article  Google Scholar 

  3. Sun Y, Hao M (2012) Statistical analysis and optimization of process parameters in Ti6Al4V laser cladding using Nd:YAG laser. Opt Lasers Eng 50:985–995. https://doi.org/10.1016/j.optlaseng.2012.01.018

    Article  Google Scholar 

  4. Greulich MD-I (2017) Rapid prototyping and fabrication of tools and metal parts by laser sintering of metal powders. Mater Technol 12:155–159. https://doi.org/10.1080/10667857.1997.11752749

  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. https://doi.org/10.1007/s40436-014-0097-7

    Article  Google Scholar 

  6. Amine T, Newkirk JW, Liou F (2014) An investigation of the effect of direct metal deposition parameters on the characteristics of the deposited layers. Case Stud Therm Eng. https://doi.org/10.1016/j.csite.2014.02.002

    Article  Google Scholar 

  7. Farayibi PK, Folkes JA, Clare AT, Farayibi PK, Folkes JA, Clare AT (2013) Laser deposition of Ti-6Al-4V wire with WC powder for functionally graded components laser deposition of Ti-6Al-4V wire with WC powder for functionally graded components. Mater Manuf Process 28:514–518. https://doi.org/10.1080/10426914.2012.718477

    Article  Google Scholar 

  8. Hu Y, Cong W (2018) A review on laser deposition-additive manufacturing of ceramics and ceramic reinforced metal matrix composites. Ceram Int 44:20599–20612. https://doi.org/10.1016/j.ceramint.2018.08.083

    Article  Google Scholar 

  9. Selcuk C (2011) Laser metal deposition for powder metallurgy parts. Powder Metall 54:94–99. https://doi.org/10.1179/174329011X12977874589924

    Article  Google Scholar 

  10. Dutta B, Froes FHS (2017) The additive manufacturing (AM) of titanium alloys. Met Powder Rep 72:96–106. https://doi.org/10.1016/j.mprp.2016.12.062

    Article  Google Scholar 

  11. Liu S, Shin YC (2019) Additive manufacturing of Ti6Al4V alloy: a review. Mater Des 164:107552. https://doi.org/10.1016/j.matdes.2018.107552

    Article  Google Scholar 

  12. Calignano F, Manfredi D, Ambrosio EP, Biamino S, Lombardi M, Atzeni E et al (2017) Overview on additive manufacturing technologies. Proc IEEE 105:593–612. https://doi.org/10.1109/JPROC.2016.2625098

    Article  Google Scholar 

  13. Majumdar JD, Pinkerton A, Liu Z, Manna I, Li L (2005) Microstructure characterisation and process optimization of laser assisted rapid fabrication of 316L stainless steel. Appl Surf Sci 247:320–327. https://doi.org/10.1016/j.apsusc.2005.01.039

    Article  Google Scholar 

  14. Shamsaei N, Yadollahi A, Bian L, Thompson SM (2015) An overview of direct laser deposition for additive manufacturing; part II: Mechanical behavior, process parameter optimization and control. Addit Manuf 8:12–35. https://doi.org/10.1016/j.addma.2015.07.002

    Article  Google Scholar 

  15. Canel T, Zeren M, Sınmazçelik T (2019) Laser parameters optimization of surface treating of Al 6082–T6 with Taguchi method. Opt Laser Technol 120:105714. https://doi.org/10.1016/j.optlastec.2019.105714

    Article  Google Scholar 

  16. Manikandan N, Raju R, Palanisamy D, Kumar S (2018) Science Direct investigation on Ti6Al4V laser metal deposition using Taguchi based grey approach. Mater Today Proc 5:14375–14383. https://doi.org/10.1016/j.matpr.2018.03.022

    Article  Google Scholar 

  17. Muhammad W, Brahme AP, Ibragimova O, Kang J, Inal K (2021) A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys. Int J Plast 136:102867. https://doi.org/10.1016/j.ijplas.2020.102867

    Article  Google Scholar 

  18. Wang C, Tan XP, Tor SB, Lim CS (2020) Machine learning in additive manufacturing: State-of-the-art and perspectives. Addit Manuf 36:101538. https://doi.org/10.1016/j.addma.2020.101538

    Article  Google Scholar 

  19. Yan J (2016) Optimal design of process parameters during laser direct metal deposition of multi-material parts. All Diss 1813

  20. Zadeh LA (1994) Fuzzy logic, neural networks and soft computing. Commun ACM 37:77–84

    Article  Google Scholar 

  21. Adedeji PA, Akinlabi S, Madushele N, Olatunji OO (2020) Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J Clean Prod 254:1–16. https://doi.org/10.1016/j.jclepro.2020.120135

    Article  Google Scholar 

  22. Choudhury B, Jha RM (2016) Soft computing techniques. Soft Comput Electromagn Methods Appl. Cambridge University Press. pp 9–44. https://doi.org/10.1533/9781782421801.39

  23. Anoune K, Bouya M, Astito A, Abdellah AB (2018) Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: a review. Renew Sustain Energy Rev 93:652–673. https://doi.org/10.1016/j.rser.2018.05.032

  24. Chowdhury S, Mhapsekar K, Anand S (2017) Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. J Manuf Sci Eng 140. https://doi.org/10.1115/1.4038293

  25. Okaro IA, Jayasinghe S, Sutcli C, Black K, Paoletti P, Green PL (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf Manuf 27:42–53. https://doi.org/10.1016/j.addma.2019.01.006

    Article  Google Scholar 

  26. Noriega A, Blanco D, Alvarez BJ, Garcia A (2013) Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manuf Technol 69:2301–2313. https://doi.org/10.1007/s00170-013-5196-2

    Article  Google Scholar 

  27. Tippayawanakorn N, Pichitlamken J (2012) Nelder-Mead method with local selection using neighborhood and memory for optimization via simulation. Adv Methods Tech Appl Model Simul. Springer. pp 134–143

  28. Kotsiopoulos T, Sarigiannidis P, Ioannidis D (2021) Machine learning and deep learning in smart manufacturing: the smart grid paradigm. Comput Sci Rev 40:100341. https://doi.org/10.1016/j.cosrev.2020.100341

    Article  MathSciNet  Google Scholar 

  29. Casalino G, Ludovico A (2002) Parameter selection by an artificial neural network for a laser bending process. Proc Inst Mech Eng Part B J Eng Manuf 216:1517–1520. https://doi.org/10.1243/095440502320783350

    Article  Google Scholar 

  30. Lu ZL, Li DCÃ, Lu BH, Zhang AF, Zhu GX, Pi G (2010) The Prediction of the building precision in the laser engineered net shaping process using advanced networks. Opt Lasers Eng 48:519–525. https://doi.org/10.1016/j.optlaseng.2010.01.002

    Article  Google Scholar 

  31. Fahle S, Prinz C, Kuhlenkötter B (2020) Systematic review on machine learning (ML) methods for manufacturing processes - identifying artificial intelligence (AI) methods for field application. Procedia CIRP 93:413–418. https://doi.org/10.1016/j.procir.2020.04.109

    Article  Google Scholar 

  32. Jin Z, Zhang Z, Demir K, Gu GX (2020) Machine learning for advanced additive manufacturing. Matter 3:1541–1556. https://doi.org/10.1016/j.matt.2020.08.023

  33. Frank AG, Dalenogare LS, Ayala NF (2019) Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ 210:15–26. https://doi.org/10.1016/j.ijpe.2019.01.004

  34. Mahamood RM, Akinlabi ET (2015) Effect of laser power and powder flow rate on the wear resistance behaviour of laser metal deposited TiC/Ti6Al4V composites. Mater Today Proc 2:2679–2686. https://doi.org/10.1016/j.matpr.2015.07.233

    Article  Google Scholar 

  35. Mahamood RM, Akinlabi ET, Shukla M, Pityana S (2013) Laser metal deposition of Ti6Al4V: a study on the effect of laser power on microstructure and microhardness. Lect Notes Eng Comput Sci 2203:994–999

    Google Scholar 

  36. Mahamood RM, Akinlabi ET (2016) Laser power and scanning speed influence on intermetallic and wear behaviour of Laser metal deposited titanium alloy composite. Lect Notes Eng Comput Sci 2226:1037–1040

    Google Scholar 

  37. Housny H, Chater EA, El Fadil H (2020) PSO-based ANFIS for quadrotor system trajectory-tracking control. 2020 1st Int Conf Innov Res Appl Sci Eng Technol IRASET 2020:0–5. https://doi.org/10.1109/IRASET48871.2020.9092015

  38. Adedeji PA, Akinlabi SA, Olatunji OO (2020) Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation. Int J Energy Res 1–16. https://doi.org/10.1002/er.5620

  39. Engelbrecht AP (2007) Computational intelligence: an introduction

  40. van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci (Ny) 176:937–971

    Article  MathSciNet  Google Scholar 

  41. Yu H, Wilamowski B (2011) Levenberg–Marquardt training. Intell Syst 1–16. https://doi.org/10.1201/b10604-15

  42. Costa M, Braga A, Menezes B (2007) Improving generalization of MLPs with sliding mode control and the Levenberg–Marquardt algorithm. Neurocomputing 70:1342–1347. https://doi.org/10.1371/journal.pone.0088408

    Article  Google Scholar 

  43. Içer S, Kara S, Güven A (2006) Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease. Expert Syst Appl 31:406–413. https://doi.org/10.1016/j.eswa.2005.09.037

    Article  Google Scholar 

  44. Demuth H, Beale M (2004). Neural Network Toolbox. https://doi.org/10.1016/j.neunet.2005.10.002

    Article  Google Scholar 

  45. Yavari S, Zoej MJV, Mokhtarzade M, Mohammadzadeh A (2012) Comparison of particle swarm optimization and genetic algorithm in rational function model optimization. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B1:281–4. https://doi.org/10.5194/isprsarchives-xxxix-b1-281-2012

  46. Minitab Express (2019) Interpret all statistics and graphs for Simple Regression. https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/simple-regression/interpret-the-results/all-statistics-and-graphs/#vif. Accessed 13 Feb 2021

  47. Yan L, Chen Y, Liou F (2020) Additive manufacturing of functionally graded metallic materials using laser metal deposition. Addit Manuf 31:100901. https://doi.org/10.1016/j.addma.2019.100901

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

    Chukwubuikem C. Ngwoke, Rasheedat M. Mahamood, Tien-Chen Jen & Paul A. Adedeji

  2. Department of Mechanical Engineering, University of Ilorin, Ilorin, Nigeria

    Rasheedat M. Mahamood

  3. Department of Metallurgy and Materials Engineering, University of Nigeria, Nsukka, Nigeria

    Victor S. Aigbodion

  4. Pan African University for Life and Earth Sciences Institute (PAULESI), Ibadan, Nigeria

    Esther T. Akinlabi

  5. Process Energy and Environmental Technology Station, University of Johannesburg, Johannesburg, South Africa

    Paul A. Adedeji

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  1. Chukwubuikem C. Ngwoke
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Correspondence to Paul A. Adedeji.

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Ngwoke, C.C., Mahamood, R.M., Aigbodion, V.S. et al. Soft computing-based process optimization in laser metal deposition of Ti-6Al-4 V. Int J Adv Manuf Technol 120, 1079–1093 (2022). https://doi.org/10.1007/s00170-022-08781-5

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