Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process

Lahoti, Geet; Pratihar, Dilip Kumar

doi:10.1504/ijdats.2017.086629

Cited by 6 publications

(3 citation statements)

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“…If the functions that estimates the network output is differentiable and the target output is well-known, then the BP algorithm is found to be more suitable to train the RNN. The structure of ANNs parameters (such as hidden neurons, weights, alpha, learning rate, sigmoid transfer function and their constants, bias value) is same as that of BPNN, except the intermediate feedback connection in the learning mechanism [26,34,39]. In turning process, the input behaves non-linearly with the output.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

et al. 2020

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Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input-output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with selffeedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

et al. 2020

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“…Applications of data mining techniques encompasses wide variety of domains including credit card use [6], customer relationship management [7], bankruptcy prediction [8,9], bacteriology for bacterial identification [10], MIG welding process [11], detecting blog spam [12], fault diagnosis and condition monitoring [13,14], software fault prediction [15], machining parameter optimization [16], demand forecasting [17], emotional speech analysis [18] and software engineering [19]. Data mining techniques have been used in a wide range of stock market prediction applications.…”

Section: Background Of the Studymentioning

confidence: 99%

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Kaur

Dharni

2019

JEMT

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Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

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“…Data mining techniques have found applications in a range of domains, including the use of credit cards (Kumar & Ravi, 2008; Sun & Vasarhelyi, 2018), customer relationship management (Rygielski et al, 2002), bankruptcy prediction (Paramjeet & Ravi, 2011; Peat & Jones, 2012; Pendharkar, 2011; Ramu & Ravi, 2009), credit risk assessment (Lahmiri, 2016), accounting and finance problems (Coakley & Brown, 2000), financial fraud detection (Fanning & Cogger, 1998), financial risk forecasting (Sun, 2012), bacteriology for bacterial identification (Rahman et al, 2011), metal inert gas welding process (Lahoti & Pratihar, 2017), detecting blog spam (Yang & Kwok, 2017), fault diagnosis and condition monitoring (Muralidharan & Sugumaran, 2016; Saimurugan & Ramachandran, 2014), software fault prediction (Erturk & Sezer, 2016; Singhal et al, 2019), machining parameter optimization (Ahmad et al, 2014), demand forecasting (Tigas et al, 2013), emotional speech analysis (Tuckova & Sramka, 2012), and software engineering (Taylor et al, 2010). Data mining techniques have rapidly found applications in diverse fields, including stock markets.…”

Section: Introductionmentioning

confidence: 99%

Application and performance of data mining techniques in stock market: A review

Kaur

Dharni

2022

Intell Sys Acc Fin Mgmt

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Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process

Cited by 6 publications

References 0 publications

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process

Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Application and performance of data mining techniques in stock market: A review

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