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


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.


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



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.


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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

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