Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Progress in Additive Manufacturing
Published in: Progress in Additive Manufacturing 4/2022

16-01-2022 | Full Research Article

Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost

Authors: Ananda Rabi Dhar, Dhrubajyoti Gupta, Shibendu Shekhar Roy, Aditya Kumar Lohar

Published in: Progress in Additive Manufacturing | Issue 4/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

To enhance the automation in additive manufacturing technology, establishment of accurate relationship between the process parameters and responses is extremely important. Direct metal deposition (DMD) or laser metal deposition (LMD) is an ever-emerging field in additive manufacturing spectrum because of its higher build-up rate with flexibility at multiple scales and reduced material wastage. In this work, a feed-forward neural network-based model is proposed for predicting the clad height and capture efficiency while building austenitic steel part through DMD process with variations in input parameters, namely laser power, scan speed, and powder flow rate. With an aim to enhance the prediction performance, the model is hybridized with ancient metaheuristic algorithms, namely genetic algorithm and particle swarm optimization as well as some rare and new metaheuristic algorithms, namely firefly algorithm, bio-geography-based optimization, flower pollination algorithm, dragonfly algorithm, and gray wolf optimization. The backward mapping model is also established along similar lines using the same hybridization schema and all the approaches are comparatively studied. The bi-directional models are further investigated by applying extreme gradient boost (XGBoost), a new and emerging paradigm in the field of machine learning. The comparison is further emphasized by employing a statistical test known as ‘technique for order of preference by similarity to ideal solution (TOPSIS).’ The novelty of the present study lies in utilizing the aforementioned rare and new metaheuristic algorithms for training artificial neural networks in order to develop predictive models in the domain of DMD as well as application of XGBoost for the same. The results clearly recommend the application of hybridized computational intelligence-based approaches in case of the forward mapping model and XGBoost in case of the backward mapping model.
previous article Metal FDM, a new extrusion-based additive manufacturing technology for manufacturing of metallic parts: a review
next article Review of binder jetting 3D printing in the construction industry

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Ansari M, Razavi RS, Barekat M (2016) An empirical-statistical model for coaxial laser cladding of nicraly powder on inconel 738 superalloy. Opt Laser Technol 86:136–144CrossRef
2.
Balu P, Leggett P, Hamid S, Kovacevic R (2013) Multi-response optimization of laser-based powder deposition of multi-track single layer hastelloy c-276. Mater Manuf Processes 28(2):173–182CrossRef
3.
Bhardwaj T, Shukla M (2020) Laser additive manufacturing-direct energy deposition of ti-15mo biomedical alloy: artificial neural network based modeling of track dilution. Lasers Manuf Mater Process 7:245–258CrossRef
4.
Caiazzo F, Caggiano A (2018) Laser direct metal deposition of 2024 al alloy: trace geometry prediction via machine learning. Materials 11(3):444CrossRef
5.
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
6.
Choi J, Chang Y (2005) Characteristics of laser aided direct metal/material deposition process for tool steel. Int J Mach Tools Manuf 45(4–5):597–607CrossRef
7.
De Veaux RD, Ungar LH (1994) Multicollinearity: A tale of two nonparametric regressions. In: Selecting models from data, Springer, pp 393–402
8.
Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int conf Neural Netw Citeseer 4:1942–1948CrossRef
9.
El Cheikh H, Courant B, Branchu S, Hascoët JY, Guillén R (2012) Analysis and prediction of single laser tracks geometrical characteristics in coaxial laser cladding process. Opt Lasers Eng 50(3):413–422CrossRef
10.
Farahmand P, Kovacevic R (2014) Parametric study and multi-criteria optimization in laser cladding by a high power direct diode laser. Lasers Manuf Mater Process 1(1–4):1–20CrossRef
11.
Feenstra D, Molotnikov A, Birbilis N (2021) Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications. Materials & Design 198:109342CrossRef
12.
Frazier WE (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23(6):1917–1928CrossRef
13.
Gibson I, Rosen DW, Stucker B (2010) Printing processes. In: Additive Manufacturing Technologies, Springer, pp 187–222
14.
Guan X, Zhao YF (2020) Modeling of the laser powder-based directed energy deposition process for additive manufacturing: a review. Int J Adv Manuf Technol 107(5):1959–1982CrossRef
15.
Holland JH, et al. (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press
16.
Izadi M, Farzaneh A, Mohammed M, Gibson I, Rolfe B (2020) A review of laser engineered net shaping (lens) build and process parameters of metallic parts. Rapid Prototyping Journal
17.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
18.
Lee HK (2008) Effects of the cladding parameters on the deposition efficiency in pulsed nd: Yag laser cladding. J Mater Process Technol 202(1–3):321–327CrossRef
19.
Lestan Z, Klancnik S, Balic J, Brezocnik M (2015) Modeling and design of experiments of laser cladding process by genetic programming and nondominated sorting. Mater Manuf Processes 30(4):458–463CrossRef
20.
Lewis GK, Schlienger E (2000) Practical considerations and capabilities for laser assisted direct metal deposition. Mater Des 21(4):417–423CrossRef
21.
Liu H, Qin X, Huang S, Jin L, Wang Y, Lei K (2018) Geometry characteristics prediction of single track cladding deposited by high power diode laser based on genetic algorithm and neural network. Int J Precis Eng Manuf 19(7):1061–1070CrossRef
22.
Mahamood RM, Akinlabi ET (2016) Processing parameters optimization for material deposition efficiency in laser metal deposited titanium alloy. Lasers Manuf Mater Process 3(1):9–21CrossRef
23.
Mazumder J (2000) A crystal ball view of direct-metal deposition. JOM 52(12):28–29CrossRef
24.
Mazumder J, Dutta D, Kikuchi N, Ghosh A (2000) Closed loop direct metal deposition: art to part. Opt Lasers Eng 34(4–6):397–414CrossRef
25.
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073MathSciNetCrossRef
26.
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRef
27.
Mondal S, Bandyopadhyay A, Pal PK (2014) Application of artificial neural network for the prediction of laser cladding process characteristics at taguchi-based optimized condition. Int J Adv Manuf Technol 70(9–12):2151–2158CrossRef
28.
Onwubolu GC, Davim JP, Oliveira C, Cardoso A (2007) Prediction of clad angle in laser cladding by powder using response surface methodology and scatter search. Opt Laser Technol 39(6):1130–1134CrossRef
29.
Opricovic S, Tzeng GH (2004) Compromise solution by mcdm methods: A comparative analysis of vikor and topsis. Eur J Oper Res 156(2):445–455CrossRef
30.
Pant P, Chatterjee D, Nandi T, Samanta SK, Lohar AK, Changdar A (2019) Statistical modelling and optimization of clad characteristics in laser metal deposition of austenitic stainless steel. J Braz Soc Mech Sci Eng 41(7):1–10CrossRef
31.
Pinkerton A (2010) Laser direct metal deposition: theory and applications in manufacturing and maintenance. In: Advances in laser materials processing, Elsevier, pp 461–491
32.
Pinkerton AJ, Li L (2004) The significance of deposition point standoff variations in multiple-layer coaxial laser cladding (coaxial cladding standoff effects). Int J Mach Tools Manuf 44(6):573–584CrossRef
33.
Saqib S, Urbanic R, Aggarwal K (2014) Analysis of laser cladding bead morphology for developing additive manufacturing travel paths. Procedia Cirp 17:824–829CrossRef
34.
Sathiya P, Panneerselvam K, Soundararajan R (2012) Optimal design for laser beam butt welding process parameter using artificial neural networks and genetic algorithm for super austenitic stainless steel. Opt Laser Technol 44(6):1905–1914CrossRef
35.
Schmidt M, Merklein M, Bourell D, Dimitrov D, Hausotte T, Wegener K, Overmeyer L, Vollertsen F, Levy GN (2017) Laser based additive manufacturing in industry and academia. CIRP Ann 66(2):561–583CrossRef
36.
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
37.
Shrivastava A, Mukherjee S, Chakraborty SS (2021) Addressing the challenges in remanufacturing by laser-based material deposition techniques. Optics & Laser Technology 144:107404CrossRef
38.
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRef
39.
Singh A, Kapil S, Das M (2020) A comprehensive review of the methods and mechanisms for powder feedstock handling in directed energy deposition. Additive Manufacturing p 101388
40.
Tabernero I, Lamikiz A, Ukar E, Martínez S, Celaya A (2014) Modeling of the geometry built-up by coaxial laser material deposition process. Int J Adv Manuf Technol 70(5–8):843–851CrossRef
41.
Thompson SM, Bian L, Shamsaei N, Yadollahi A (2015) An overview of direct laser deposition for additive manufacturing; part i: Transport phenomena, modeling and diagnostics. Addit Manuf 8:36–62
42.
Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold Co
43.
Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, Springer, pp 169–178
44.
Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation, Springer, pp 240–249
45.
Yildiz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26(1):327–333CrossRef
46.
Yu T, Yang L, Zhao Y, Sun J, Li B (2018) Experimental research and multi-response multi-parameter optimization of laser cladding fe313. Opt Laser Technol 108:321–332CrossRef
Metadata
Title
Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost
Authors
Ananda Rabi Dhar
Dhrubajyoti Gupta
Shibendu Shekhar Roy
Aditya Kumar Lohar
Publication date
16-01-2022
Publisher
Springer International Publishing
Published in
Progress in Additive Manufacturing / Issue 4/2022
Print ISSN: 2363-9512
Electronic ISSN: 2363-9520
DOI
https://doi.org/10.1007/s40964-021-00251-w

Other articles of this Issue 4/2022

Progress in Additive Manufacturing 4/2022 Go to the issue

Review Article

Metal FDM, a new extrusion-based additive manufacturing technology for manufacturing of metallic parts: a review

Full Research Article

The influence of the energy density on dimensional, geometric, mechanical and morphological properties of SLS parts produced with single and multiple exposure types

Full Research Article

Effects of polyurea coating on the elastoplastic behavior of additively manufactured PLA specimens

Full Research Article

Experimental investigation of process-structure effects on interfacial bonding strength of a short carbon fiber/polyamide composite fabricated by fused filament fabrication

Full Research Article

A dimensional assessment of small features and lattice structures manufactured by laser powder bed fusion

Full Research Article

Effective thermal conductivity of 3D-printed continuous wire polymer composites

Premium Partners

    Image Credits