Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

Nayak, Kanhu Charan; Tripathy, Rajesh Kumar

doi:10.9790/1684-0130110

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Shaban et al [17] diagnosed the machining outcome by a logical analysis of data for evaluation of product finishing quality and geometric profile by considering process parameters. Shi et al [18] have studied the characteristics of drilling in real time to detect influx and loss during machining operations using random forests and support vector machine. e most likely used algorithm in these studies is RF and SVR.…”

Section: Grinding Operationmentioning

confidence: 99%

Smart manufacturing through machine learning: A review, perspective and future directions to machining industry

Rajesh,

Prabhuswamy,

Satish

2023

World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium Wmcaus 2022

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Nowadays, to reach progressive growth although being competitive in the market, the manufacturing industries are using advanced technologies such as cloud computing, the Internet of things (IoT), arti cial intelligence, 3D printer, nanotechnology, cryogenics, robotics, and automation in smart manufacturing sectors. One such subclass of arti cial intelligence is machine learning, which uses a computer system for making predictions and performing de nite tasks without any use of speci c instructions to enhance the quality of the product, and rate of production, and to optimize the processes and parameters in machining operations. A broad category of manufacturing that is technology-driven utilizes internet-connected machines to monitor the performances of manufacturing processes referring as smart manufacturing. e current paper presents a comprehensive survey and summary of di erent machine learning algorithms which are being employed in various traditional and nontraditional machining processes, and also, an outlook of the manufacturing paradigm is presented. Subsequently, future directions in the machining industry were proposed based on trends and challenges that are accompanying machine learning.

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Section: Grinding Operationmentioning

confidence: 99%

Smart manufacturing through machine learning: A review, perspective and future directions to machining industry

Rajesh,

Prabhuswamy,

Satish

2023

World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium Wmcaus 2022

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Section: Grinding Operationmentioning

confidence: 99%

Smart Manufacturing through Machine Learning: A Review, Perspective, and Future Directions to the Machining Industry

Rajesh

Prabhuswamy

Krishnasamy

2022

Journal of Engineering

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Nowadays, to reach progressive growth although being competitive in the market, the manufacturing industries are using advanced technologies such as cloud computing, the Internet of things (IoT), artificial intelligence, 3D printer, nanotechnology, cryogenics, robotics, and automation in smart manufacturing sectors. One such subclass of artificial intelligence is machine learning, which uses a computer system for making predictions and performing definite tasks without any use of specific instructions to enhance the quality of the product, and rate of production, and to optimize the processes and parameters in machining operations. A broad category of manufacturing that is technology-driven utilizes internet-connected machines to monitor the performances of manufacturing processes referring as smart manufacturing. The current paper presents a comprehensive survey and summary of different machine learning algorithms which are being employed in various traditional and nontraditional machining processes, and also, an outlook of the manufacturing paradigm is presented. Subsequently, future directions in the machining industry were proposed based on trends and challenges that are accompanying machine learning.

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“…It was concluded that the adopted model would be suitable for prediction of different surface characteristics with minimum mean absolute percentage error (MAPE). Nayak and Tripathy [19] applied multi-layer feed forward neural network (MFNN) and LSSVM techniques to predict MRR and SR values in an ECM process. Based on mean square error (MSE) values, it was propounded that LSSVM with RBF kernel function would outperform MFNN approach with respect to prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Support Vector Machine-based Prediction Model for Electrochemical Machining Process

Mr.¹,

Dandge²,

Prof.³

2020

Karbala International Journal of Modern Science

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Manufacturing of quality products is one of the core measures to address competitiveness in industries. Hence, it is always necessary to accomplish quality prediction at early stages of a manufacturing process to attain high quality products at the minimum possible cost. To achieve this goal, the past researchers have developed and investigated the applications of different intelligent techniques for their effective deployment at various stages of manufacturing processes. In this paper, support vector machine (SVM), a supervised learning system based on a novel artificial intelligence paradigm, is employed for prediction of three responses, like material removal rate, surface roughness and radial overcut during an electrochemical machining (ECM) operation. Gaussian radial basis kernel function is adopted in this algorithm to provide higher prediction accuracy. Regression analyses are also carried out to validate the effectiveness of these prediction models. The SVM-based results show good agreement between the experimental and predicted response values as compared to linear and quadratic models. Among the four ECM process parameters, i.e. applied voltage, tool feed rate, electrolyte concentration and percentage of reinforcement of B 4 C particles in the metal matrix, tool feed rate is identified having the maximum influence on the considered responses.

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Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

Abstract: :The important performance parameter such as material removal rate (MRR)

Cited by 6 publications

References 17 publications

Smart manufacturing through machine learning: A review, perspective and future directions to machining industry

Smart manufacturing through machine learning: A review, perspective and future directions to machining industry

Smart Manufacturing through Machine Learning: A Review, Perspective, and Future Directions to the Machining Industry

A Support Vector Machine-based Prediction Model for Electrochemical Machining Process

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