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

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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.

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References

  • 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 147:389–396

    Article  Google Scholar 

  • Arola D, Ramulu M (1993) A study of kerf characteristics in abrasive waterjet machining of graphite/epoxy composite. ASME Mach Adv Comput 45(66):125–151

    Google Scholar 

  • Bortolan G, Degami R (1985) A review of some methods for ranking fuzzy subset. Fuzzy Sets Syst. 15(1):1–19

    MathSciNet  Google Scholar 

  • 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 202:574–582

    Article  Google Scholar 

  • Chakravarthy PS, Babu NR (1999) New approach for selection of optimal process parameters in abrasive water jet cutting. Mater Manuf Proces 14(4):581–600

    Article  Google Scholar 

  • Chandramohan D (2014) Studies on natural fiber particle reinforced composite material for conservation of natural resources. Adv Appl Sci Res 5(2):305–315

    Google Scholar 

  • Chauhan A, Chauhan P, Kaith B (2012) Natural fiber reinforced composite: a concise review article. Chem Eng Process Technol 3(2):1–3

    Google Scholar 

  • Chen FL, Siores E, Patel K (2002) Improving the cut surface qualities using different controlled nozzle oscillation techniques. Int J Mach Tool Manuf 42:717–722

    Article  Google Scholar 

  • Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  • 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 16:1707–1722

    Article  Google Scholar 

  • 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 19:701–713

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 59:288–302

    Article  Google Scholar 

  • 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 21:4387–4398

    Article  Google Scholar 

  • Georgios K, Arlindo S, Mihail F (2013) Green composites: a review of adequate materials for automotive applications. Compos B 44:120–127

    Article  Google Scholar 

  • Gu B, Sheng VS (2017) A robust regularization path algorithm for V-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248

    Article  Google Scholar 

  • 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 30:1040–1048

    Article  Google Scholar 

  • 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, Canada

  • Hascalik A, Caydas U, Gurun H (2007) Effect of traverse speed on abrasive waterjet machining of Ti–6Al–4V alloy. Mater Des 28:1953–1957

    Article  Google Scholar 

  • Hashish M (1991) Advances in composite machining with abrasive-waterjets. Process Manuf Comp Mater 49:93–111

    Google Scholar 

  • Jagadish, Ray A (2014a) Multi-objective optimization of green EDM: an integrated theory. J Inst Eng India Ser C 9:41–47

    Google Scholar 

  • 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):1299–1311

    Google Scholar 

  • Jagadish, Ray A (2015) A fuzzy muti-criteria decision making model for green electrical discharge machining. Adv Intell Syst Comput 335:33–43

    Google Scholar 

  • 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):1359–1370

    Google Scholar 

  • 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 45:1443–1450

    Article  Google Scholar 

  • 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):1083–1090

    Article  Google Scholar 

  • Komanduri R, Zhang B, Vissa CM (1991) Machining of fibre reinforced composites. ASME Process Manuf Comp Mater 49(27):1–36

    Google Scholar 

  • 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 55:23–34

    Article  Google Scholar 

  • La-Mantia FP, Morreale M (2011) Green composites: a brief review. Compos A 42:579–588

    Article  Google Scholar 

  • 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):297–303

    Article  Google Scholar 

  • 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 30:94–103

    Article  Google Scholar 

  • Markarian J (2002) Additive developments aid growth in wood-plastic composites. Plast Addit Compd 4:18–21

    Article  Google Scholar 

  • MATLAB (2006) Fuzzy logic toolbox. User’s guide. The MathWorks Inc., Natick. https://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy

    Google Scholar 

  • Minitab 14 (2003) Minitab user manual release 14. State College, PA, USA, ISBN 0-925636-48-7. https://www.addlink.es/images/pdf/agdweb274

  • Momber AW, Kovacevic R (1992) Principles of abrasive water jet machining. Springer, London

    MATH  Google Scholar 

  • Olsen JH (2008) Green cutting with waterjets. Waterjet Cutting Articles, A publication of the fabricators and manufacturer association. Intl. Rockford

  • 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 26:41–55

    Article  Google Scholar 

  • Pritchard G (2004) Two technologies merge: wood–plastic composites. Plast Addit Compd 6:18–21

    Article  Google Scholar 

  • Ross PJ (1996) Taguchi techniques for quality engineering. McGraw-Hill International Editions, Singapore

    Google Scholar 

  • 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 32:32–39

    Article  Google Scholar 

  • Sheng P, Srinivasan M (1995) Multi-objective process planning in environmentally conscious manufacturing: a feature-based approach. CIRP Ann Manuf Technol 44(1):433–437

    Article  Google Scholar 

  • Sivapirakasam SP, Mathew J, Surianarayanan M (2011) Multi-attribute decision making for green electrical discharge machining. Expert Syst Appl 38:8370–8374

    Article  Google Scholar 

  • Sugeno M, Kang G (1986) Fuzzy modeling and control of multilayer incinerator. Fuzzy Sets Syst 18:329–346

    Article  MATH  Google Scholar 

  • Taguchi G (1990) Introduction to quality engineering. Asian Productivity Organization, Tokyo

    Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  MATH  Google Scholar 

  • 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 129:467–470

    Article  Google Scholar 

  • 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 70:469–475

    Article  Google Scholar 

  • Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295

    Article  Google Scholar 

  • 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):9–16

    Google Scholar 

  • 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 27:456–464

    Article  Google Scholar 

  • Wang J, Lian S, Shi YQ (2017) Hybrid multiplicative multi-watermarking in DWT domain. Multidimens Syst Signal Process 28:617–636

    Article  MATH  Google Scholar 

  • Weller EJ (1984) Non-traditional machining processes. SME, Dearborn

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 22:2935–2952

    Article  Google Scholar 

  • Yadav SK, Patel SK (2013) Thesis entitled “Optimization of green electro-discharge machining using VIKOR”. Department of Mechanical Engineering, NIT Rourkela, India

  • Yeo SH, New AK (1999) A method for green process planning in EDM. Int J Adv Manuf Technol 15(4):287–291

    Article  Google Scholar 

  • Yeo SH, Neo KG, Tan HC (1998) Assessment of health hazard in production of printed paper packages. Int J Adv Manuf Technol 14:376–384

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  • Zaina AM, Haronb H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11:5350–5359

    Article  Google Scholar 

  • 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):1–21

    Google Scholar 

  • 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):786–799

    Article  Google Scholar 

Download references

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.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Silchar, Assam, 788010, India

    Sumit Bhowmik

  2. Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, 735102, India

    Amitava Ray

  3. Department of Mechanical Engineering, National Institute of Technology, Raipur, Chhattisgarh, 492010, India

    Jagadish

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Correspondence to Jagadish.

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Jagadish, Bhowmik, S. & Ray, A. Development of fuzzy logic-based decision support system for multi-response parameter optimization of green manufacturing process: a case study. Soft Comput 23, 11015–11034 (2019). https://doi.org/10.1007/s00500-018-3656-1

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