Advertisement

Soft Computing

, Volume 23, Issue 21, pp 11015–11034 | Cite as

Development of fuzzy logic-based decision support system for multi-response parameter optimization of green manufacturing process: a case study

  • JagadishEmail author
  • Sumit Bhowmik
  • Amitava Ray
Methodologies and Application
  • 115 Downloads

Abstract

The aim of this paper is to development of decision support system based on fuzzy logic for green manufacturing (GM) process. The fuzzy logic-based decision support system (FLDS) consists of subtractive clustering with Takagi–Sugeno–Kang fuzzy logic (TSK-FL) model which predicts and optimizes the process parameters of GM process. Here, subtractive clustering method is used for extraction of cluster centers which influences the process parameters of GM process, while TSK-FL method is used for prediction and optimization of GM process parameters. An experiment has been performed on machining of natural filler-reinforced polymer composites using abrasive water jet machining process to show the strength and working significance of proposed model. Initially, the historical database has been created using the results of the theoretical experiment of Taguchi (L27) orthogonal array. Second, normalization process has been performed on the historical data to transform original data sequence to comparable sequence data, which then provided as input to the FLDS system for optimization of the process parameters of GM process. In addition, prediction model has been developed for optimum prediction of response parameters for GM process using proposed FLDS system. Finally, the confirmatory and performance analysis has been tested to verify the experimental results. The result shows that predictions through proposed model are comparable with experimental results with accuracies more than 95% and establishes the most optimal combinations of process parameters for GM process which directly or indirectly improves the efficiency as well as performance of GM process. The research suggests that the developed model can be used as systematic approach for prediction and parameter optimization in GM applications.

Keywords

Fuzzy logic (FL) FLDS, AWJM NFRP composites Taguchi method Optimization Green manufacturing 

Notes

Acknowledgements

The authors acknowledge Mr. Vijay Lagad, Managing Director, Supernova Waterjet Cutting (SWC) Systems, Nashik, India, for providing the necessary resources and other facilities during the research work, and also, thanks Prof. Prashant Badgujar, Assistant Professor, Department of Mechanical Engineering, Institute of Technology-Polytechnic, Nashik, for his valuable guidance during experimentation.

Compliance with ethical standards

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Human and animals rights

The author(s) declared that present article does not contain any studies with animals performed by any of the authors.

Informed consent

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

References

  1. Akkurt A, Kulekci MK, Seker U, Ercan F (2004) Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol 147:389–396CrossRefGoogle Scholar
  2. Arola D, Ramulu M (1993) A study of kerf characteristics in abrasive waterjet machining of graphite/epoxy composite. ASME Mach Adv Comput 45(66):125–151Google Scholar
  3. Bortolan G, Degami R (1985) A review of some methods for ranking fuzzy subset. Fuzzy Sets Syst. 15(1):1–19MathSciNetGoogle Scholar
  4. Caydas U, Hascalik A (2008) A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582CrossRefGoogle Scholar
  5. Chakravarthy PS, Babu NR (1999) New approach for selection of optimal process parameters in abrasive water jet cutting. Mater Manuf Proces 14(4):581–600CrossRefGoogle Scholar
  6. Chandramohan D (2014) Studies on natural fiber particle reinforced composite material for conservation of natural resources. Adv Appl Sci Res 5(2):305–315Google Scholar
  7. Chauhan A, Chauhan P, Kaith B (2012) Natural fiber reinforced composite: a concise review article. Chem Eng Process Technol 3(2):1–3Google Scholar
  8. Chen FL, Siores E, Patel K (2002) Improving the cut surface qualities using different controlled nozzle oscillation techniques. Int J Mach Tool Manuf 42:717–722CrossRefGoogle Scholar
  9. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278Google Scholar
  10. Deng W, Chen R, He B, Liu Y, Yin L, Guo J (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16:1707–1722CrossRefGoogle Scholar
  11. Deng W, Zhao H, Liu Z, Yan X, Li Y, Yin L, Ding C (2015) An improved CACO algorithm based on adaptive method and multi-variant strategies. Soft Comput 19:701–713CrossRefGoogle Scholar
  12. Deng W, Yao R, Zhao H, Yang X, Li G (2017a) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput.  https://doi.org/10.1007/s00500-017-2940-9 CrossRefGoogle Scholar
  13. Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017b) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. App Soft Comput 59:288–302CrossRefGoogle Scholar
  14. Deng W, Zhao H, Zou L, Guangyu L, Yang X, Wu D (2017c) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21:4387–4398CrossRefGoogle Scholar
  15. Georgios K, Arlindo S, Mihail F (2013) Green composites: a review of adequate materials for automotive applications. Compos B 44:120–127CrossRefGoogle Scholar
  16. Gu B, Sheng VS (2017) A robust regularization path algorithm for V-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248CrossRefGoogle Scholar
  17. Guharaja S, Noorul Haq A, Karuppannan KM (2006) Optimization of green sand casting process parameters by using Taguchi’s method. Int J Adv Manuf Technol 30:1040–1048CrossRefGoogle Scholar
  18. Haman A, Geogranas ND (2008) Comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In: IEEE international workshop on Haptic Audio Visual environments and their applications, Ottawa, CanadaGoogle Scholar
  19. Hascalik A, Caydas U, Gurun H (2007) Effect of traverse speed on abrasive waterjet machining of Ti–6Al–4V alloy. Mater Des 28:1953–1957CrossRefGoogle Scholar
  20. Hashish M (1991) Advances in composite machining with abrasive-waterjets. Process Manuf Comp Mater 49:93–111Google Scholar
  21. Jagadish, Ray A (2014a) Multi-objective optimization of green EDM: an integrated theory. J Inst Eng India Ser C 9:41–47Google Scholar
  22. Jagadish, Ray A (2014b) Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA). Int J Adv Manuf Technol 87(5):1299–1311Google Scholar
  23. Jagadish, Ray A (2015) A fuzzy muti-criteria decision making model for green electrical discharge machining. Adv Intell Syst Comput 335:33–43Google Scholar
  24. Jagadish, Bhowmik S, Ray A (2015) Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology. Int J Adv Manuf Technol 87(5):1359–1370Google Scholar
  25. Jegaraj JJR, Babu NR (2005) A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter. Int J Mach Tool Manuf 45:1443–1450CrossRefGoogle Scholar
  26. Jung JH, Kwon WT (2010) Optimization of EDM process for multiple performance characteristics using Taguchi method and grey relational analysis. J Mech Sci Technol 24(5):1083–1090CrossRefGoogle Scholar
  27. Komanduri R, Zhang B, Vissa CM (1991) Machining of fibre reinforced composites. ASME Process Manuf Comp Mater 49(27):1–36Google Scholar
  28. Kumar S, Satsangi PS, Prajapati DR (2011) Optimization of green sand casting process parameters of a foundry by using Taguchi’s method. Int J Adv Manuf Technol 55:23–34CrossRefGoogle Scholar
  29. La-Mantia FP, Morreale M (2011) Green composites: a brief review. Compos A 42:579–588CrossRefGoogle Scholar
  30. Lemma E, Chen L, Siores E (2002) Study of cutting fiber-reinforced composites by using abrasive water-jet with cutting head oscillation. Compos Struct 57(1–4):297–303CrossRefGoogle Scholar
  31. Mariajayaprakash A, Senthilvelan T, Gnanadass R (2015) Optimization of process parameters through fuzzy logic and geneticalgorithm—A case study in a process industry. Appl Soft Comput 30:94–103CrossRefGoogle Scholar
  32. Markarian J (2002) Additive developments aid growth in wood-plastic composites. Plast Addit Compd 4:18–21CrossRefGoogle Scholar
  33. MATLAB (2006) Fuzzy logic toolbox. User’s guide. The MathWorks Inc., Natick. https://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy Google Scholar
  34. Minitab 14 (2003) Minitab user manual release 14. State College, PA, USA, ISBN 0-925636-48-7. https://www.addlink.es/images/pdf/agdweb274
  35. Momber AW, Kovacevic R (1992) Principles of abrasive water jet machining. Springer, LondonzbMATHGoogle Scholar
  36. Olsen JH (2008) Green cutting with waterjets. Waterjet Cutting Articles, A publication of the fabricators and manufacturer association. Intl. RockfordGoogle Scholar
  37. Prabhu S, Uma M, Vinayagam BK (2015) Surface roughness prediction using Taguchi-fuzzy logic-neural for network analysis for CNT nanofluids based grinding process. Neural Comput Appl 26:41–55CrossRefGoogle Scholar
  38. Pritchard G (2004) Two technologies merge: wood–plastic composites. Plast Addit Compd 6:18–21CrossRefGoogle Scholar
  39. Ross PJ (1996) Taguchi techniques for quality engineering. McGraw-Hill International Editions, SingaporeGoogle Scholar
  40. Shabgarda MR, Badamchizadehb MA, Ranjbarya G, Amini K (2013) Fuzzy approach to select machining parameters in electrical discharge machining and ultrasonic-assisted EDM processes. J Manuf Systm 32:32–39CrossRefGoogle Scholar
  41. Sheng P, Srinivasan M (1995) Multi-objective process planning in environmentally conscious manufacturing: a feature-based approach. CIRP Ann Manuf Technol 44(1):433–437CrossRefGoogle Scholar
  42. Sivapirakasam SP, Mathew J, Surianarayanan M (2011) Multi-attribute decision making for green electrical discharge machining. Expert Syst Appl 38:8370–8374CrossRefGoogle Scholar
  43. Sugeno M, Kang G (1986) Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets Syst 18:329–346CrossRefzbMATHGoogle Scholar
  44. Taguchi G (1990) Introduction to quality engineering. Asian Productivity Organization, TokyoGoogle Scholar
  45. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132CrossRefzbMATHGoogle Scholar
  46. Tan XC, Liu F, Cao HJ, Zhang H (2002) A decision-making framework model of cutting fluid selection for green manufacturing and a case study. J Matet Process Technol 129:467–470CrossRefGoogle Scholar
  47. Tang L, Du YT (2014) Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization. Int J Adv Manuf Technol 70:469–475CrossRefGoogle Scholar
  48. Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295CrossRefGoogle Scholar
  49. Todkar M, Patkure J (2014) Fuzzy modelling and ga optimization for optimal selection of process parameters to maximize MRR in abrasive water jet machining. Int J Theor Appl Res Mech Eng 3(1):9–16Google Scholar
  50. Vundavilli PR, Parappagoudar MB, Kodali SP, Benguluri S (2012) Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process. Knowl Based Syst 27:456–464CrossRefGoogle Scholar
  51. Wang J, Lian S, Shi YQ (2017) Hybrid multiplicative multi-watermarking in DWT domain. Multidimens Syst Signal Process 28:617–636CrossRefzbMATHGoogle Scholar
  52. Weller EJ (1984) Non-traditional machining processes. SME, DearbornGoogle Scholar
  53. Xiong L, Xu Z, Shi YQ (2017) An integer wavelet transform based scheme for reversible data hiding in encrypted images. Multidimens Syst Signal Process.  https://doi.org/10.1007/s11045-017-0497-5 CrossRefGoogle Scholar
  54. Xue Y, Jiang J, Zhao B, Ma T (2018) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 22:2935–2952CrossRefGoogle Scholar
  55. Yadav SK, Patel SK (2013) Thesis entitled “Optimization of green electro-discharge machining using VIKOR”. Department of Mechanical Engineering, NIT Rourkela, IndiaGoogle Scholar
  56. Yeo SH, New AK (1999) A method for green process planning in EDM. Int J Adv Manuf Technol 15(4):287–291CrossRefGoogle Scholar
  57. Yeo SH, Neo KG, Tan HC (1998) Assessment of health hazard in production of printed paper packages. Int J Adv Manuf Technol 14:376–384CrossRefGoogle Scholar
  58. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefzbMATHGoogle Scholar
  59. Zaina AM, Haronb H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11:5350–5359CrossRefGoogle Scholar
  60. Zhao H, Sun M, Deng W, Yang X (2017a) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 19(14):1–21Google Scholar
  61. Zhao HM, Li DY, Deng W, Yang XH (2017b) Research on vibration suppression method of alternating current motor based on fractional order control strategy. J Process Mech Eng 231(4):786–799CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Institute of TechnologySilcharIndia
  2. 2.Jalpaiguri Government Engineering CollegeJalpaiguriIndia
  3. 3.Department of Mechanical EngineeringNational Institute of TechnologyRaipurIndia

Personalised recommendations