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Abrasive water jet machining for a high-quality green composite: the soft computing strategy for modeling and optimization

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Recently, the use of green composites in various applications has increased rapidly due their superior properties. Green composite processing is indeed essential to manufacture good quality parts. Conventional machining of green composites is extremely difficult due to their inherent properties and high cost. Therefore, processing of these composites by abrasive water jet machining (AWJM) is a highly relevant topic nowadays, implying a need to minimize the process time and surface roughness simultaneously. In this study, experiments were performed to investigate the AWJM parameters (pressure within pumping system, traverse speed, stand-off distance), concerning the two outputs. The limitations of regression equations to model multiple outputs simultaneously were addressed by developing neural networks. The networks based on genetic algorithm and on back-propagation algorithm were utilized to map the outputs and AWJM parameters, in forward and reverse mode, respectively. The network performances were discussed in detail. Three metaheuristics were implemented to optimize the AWJM parameters, including consideration of different output weight fractions: hybrid spider monkey optimization, grey wolf optimization and teaching–learning-based optimization. Their performances were compared regarding the solution accuracy and convergence rate. The adopted optimal AWJM setting was successfully verified in a confirmation run, clearly demonstrating its benefits.

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Availability of data and materials

The data/material will be available upon request.

Code availability

The codes (for soft computing techniques application) will be available upon request.

Abbreviations

a, b :

Constants of activation function

α :

Momentum constant

\({{\partial E} \mathord{\left/ {\vphantom {{\partial E} {\partial w_{jk} }}} \right. \kern-\nulldelimiterspace} {\partial w_{jk} }}\) :

Chain rule of differentiation

ANNs:

Artificial neural networks

AWJM:

Abrasive water jet machining

BBD:

Box–Behnken design

BPNN:

Back-propagation neural network

DEAR:

Data envelopment analysis-based ranking

FRP:

Fiber reinforced polymer

GA:

Genetic algorithm

GA-NN:

Genetic algorithm neural network

GWO:

Grey wolf optimization

HSMO:

Hybrid spider monkey optimization

H K :

Kth neuron of the hidden layer of network

MAPE:

Mean absolute percent error

MRR:

Material removal rate

MOORA:

Multi-objective optimization method by ratio analysis

MSE:

Mean squared error

NN:

Neural network

η :

Learning rate

NFRPC:

Natural fiber reinforced polymer composites

N H :

Number of hidden neurons

N I :

Number of input neurons

N o :

Number of output neurons

PD:

Percent deviation

PT:

Process time

PwPS:

Pressure within pumping system

Ra:

Surface roughness

RSM:

Response surface methodology

SEM:

Scanning electron microscope

SoD:

Stand-off distance

T i j :

Target output of the network

O i j :

Predicted output of the network

TLBO:

Teaching–learning-based optimization

TRIP:

Transformation-induced plasticity

TS:

Traverse speed

w :

Weights of the network

WPC:

Wood-plastic composites

W PT :

Weight fractions for process time

W Ra :

Weight fractions for surface roughness

X :

Input neuron

Y :

Output neuron

Y K :

Kth neuron of the output layer of network

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Funding

No funding was received for this research.

Author information

Authors and Affiliations

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

    Jagadish

  2. Department of Mechanical Engineering, PES Institute of Technology and Management, Visvesvaraya Technological University, Belagavi, Shimoga, 577204, India

    G. C. Manjunath Patel

  3. Faculty of Information Technology, Faculty of Management, Belgrade Metropolitan University, Tadeusa Koscuska 63, 11000, Belgrade, Serbia

    Tatjana V. Sibalija

  4. College of Mechanical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China

    Jabir Mumtaz

  5. State Key Lab of Engineering Research Centre for Digital Manufacturing and Equipment, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

    Zhang Li

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Contributions

Jagadish involved in conceptualization, methodology, experimentation and validation. Manjunath Patel G C took part in data curation, formal analysis, paper writing (paper draft preparation). Tatjana Sibalija involved in data curation, optimization techniques implementation, paper writing (review and editing). Jabir Mumtaz took part in software and visualization. Zhang Li involved in software and visualization.

Corresponding author

Correspondence to G. C. Manjunath Patel.

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Technical Editor: Adriano Fagali de Souza.

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Jagadish, Patel, G.C.M., Sibalija, T.V. et al. Abrasive water jet machining for a high-quality green composite: the soft computing strategy for modeling and optimization. J Braz. Soc. Mech. Sci. Eng. 44, 83 (2022). https://doi.org/10.1007/s40430-022-03378-1

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