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Inverse analysis and multi-objective optimization of single-point incremental forming of AA5083 aluminum alloy sheet

Soft Computing volume 24, pages 4505–4521 (2020)Cite this article

Abstract

This paper presents soft computing-based modeling and multi-objective optimization of process parameters in single-point incremental forming (SPIF) of aluminum alloy sheet in order to obtain desired deformed shape with optimal formability satisfying multiple objectives. Response surface methodology and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed to predict the responses based on the experimental data collected according to central composite design of experiments considering tool diameter, feed rate and step height as inputs, and outputs, namely forming wall angle, deformed sheet thickness and surface roughness. Inverse analyses were also performed to determine the set of input parameters to achieve desired outputs. Two different algorithms, namely back-propagation and hybrid, were employed to train the ANFIS in batch mode with the help of experimental data. The performances of the developed models were tested through real experimental data and also cross-validation methods. ANFIS trained by hybrid algorithm was found to be slightly better than that trained by the back-propagation algorithm in terms of prediction accuracy. Desirability function and a non-dominated sorting genetic algorithm were utilized for performing multi-objective optimization in SPIF, and the obtained optimal results were found satisfactory compared to the experimental data. The proposed approach could provide a reliable guidance for selection of suitable parameters in SPIF to achieve desired formed parts.

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References

  • Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Stud Comput Intell 2:11–19

    Google Scholar 

  • Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19–28

    Google Scholar 

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2017) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4040–4071

    Article  Google Scholar 

  • Aminian M, Terimouri R (2015) Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process. Soft Comput 19:793–810

    Article  Google Scholar 

  • Baruah A, Pandivelan C, Jeevanantham AK (2017) Optimization of AA5052 in incremental sheet forming using grey relational analysis. Measurement 106:95–100

    Article  Google Scholar 

  • Bhattacharya A, Maneesh K, Reddy NV, Cao J (2011) Formability and surface finish studies in single point incremental forming. J Manuf Sci Eng 133:061020–061021

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6:182–197

    Article  Google Scholar 

  • Esfahani RT, Golabi S, Zojaji Z (2016) Optimization of finite element model of laser forming in circular path using genetic algorithms and ANFIS. Soft Comput 20:2031–2045

    Article  Google Scholar 

  • Fiorentino A, Attanasio A, Marzi R, Ceretti E, Giardini EC (2011) On forces, formability and geometrical error in metal incremental sheet forming. Int J Mater Product Technol 40(3/4):277–295

    Article  Google Scholar 

  • Gupta P, Jeswiet J (2017) Observations on heat generated in single point incremental forming. Procedia Eng 183:161–167

    Article  Google Scholar 

  • Ham M, Jeswiet J (2008) Single point incremental forming. Int Mater Product Technol 32(4):374–387

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Jeswiet J, Hagan E, Szekeres A (2002) Forming parameters for incremental forming of aluminium alloy sheet metal. IMechE Part B J Eng Manuf 216:1367–1371

    Article  Google Scholar 

  • Kurra S, Regalla SP (2014) Experimental and numerical studies on formability of extra-deep drawing steel in incremental sheet metal forming. J Mater Res Technol 3(2):158–171

    Article  Google Scholar 

  • Kurra S, Rahman NH, Regalla SP, Gupta AM (2015) Modeling and optimization of surface roughness in single point incremental forming process. J Mater Res Technol 4(3):304–313

    Article  Google Scholar 

  • Maji K, Pratihar DK, Nath AK (2013) Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy system. Soft Comput 17:849–865

    Article  Google Scholar 

  • Martins PAF, Bay N, Skjoedt M, Silva MB (2008) Theory of single point incremental forming. CIRP Ann Manuf Technol 57:247–252

    Article  Google Scholar 

  • Meyer RK, David DK (2004) A Minitab guide to statistics, 3rd edn. Prentice-Hall Publishing, Upper Saddle River, NJ

    Google Scholar 

  • Montgomery DC (2001) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  • Mostafanezhad H, Menghari HG, Esmaeili S, Shirkharkolaee EM (2018) Optimization of two-point incremental forming process of AA1050 through response surface methodology. Measurement 127:21–28

    Article  Google Scholar 

  • Nejati MR, Gollo MH, Tejdari M, Ghaffarian H (2018) Input value prediction of parameters in laser bending using Fuzzy and PSO. Soft Comput 22:2189–2203

    Article  Google Scholar 

  • Omidvar M, Fard RK, Sohrabpoor H, Terimouri R (2015) Selection of laser bending process parameters for maximal deformation angle through neural network and teaching-learning-based optimization algorithm. Soft Comput 19:609–620

    Article  Google Scholar 

  • Raju C, Haloi N, Narayanan CS (2017) Strain distribution and failure mode in single point incremental forming (SPIF) of multiple commercially pure aluminum sheets. J Manuf Process 30:328–335

    Article  Google Scholar 

  • Senthil R, Gnanavelbabu A (2014) Numerical analysis on formability of AZ61A magnesium alloy by incremental forming. Procedia Eng 97:1975–1982

    Article  Google Scholar 

  • Shanmuganatan SP, Kumar VSS (2014) Modeling of Incremental forming process parameters of Al 3003 (O) by response surface methodology. Procedia Eng 97:346–356

    Article  Google Scholar 

  • Yang Y, Longchao C, Chaochao W, Qi Z, Ping J (2018) Multi-objective process parameters optimization of hot-wire laser welding using ensemble of metamodels and NSGA-II. Robot Comput Integr Manuf 53:141–152

    Article  Google Scholar 

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Acknowledgements

The financial support of the Department of Science and Technology (DST), Science and Engineering Research Board (SERB), India, for funding this research work under Early Career Research (ECR) award with project File No.-ECR/2016/001134 is gratefully acknowledged by the first author.

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Authors and Affiliations

  1. Mechanical Engineering Department, National Institute of Technology Patna, Patna, 800 005, India

    Kuntal Maji & Gautam Kumar

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  1. Kuntal Maji
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  2. Gautam Kumar
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Correspondence to Kuntal Maji.

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Maji, K., Kumar, G. Inverse analysis and multi-objective optimization of single-point incremental forming of AA5083 aluminum alloy sheet. Soft Comput 24, 4505–4521 (2020). https://doi.org/10.1007/s00500-019-04211-z

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Keywords

  • Single-point incremental forming
  • Aluminum alloy sheet
  • Response surface methodology
  • Inverse modeling
  • Multi-objective optimization