Development of a methodology for training artificial neural networks for intelligent decision support systems

Sova, Oleg; Shyshatskyi, Andrii; Zhuravskyi, Yurii; Salnikova, Olha; Zubov, Oleksandr; Zhyvotovskyi, Ruslan; Romanenko, Igor; Kalashnikov, Yevhen; Shulhin, Artem; Simonenko, Alexander

doi:10.15587/1729-4061.2020.199469

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Optimization algorithms, a subset of intelligent methods, are also utilized to identify the best machining conditions. ANNs are one of the most well-known predictive models, capable of estimating machining operation output(s) in a range of input parameters [27].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Mahdi

Dalef

Hussein

2022

EEJET

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Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Mahdi

Dalef

Hussein

2022

EEJET

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Information and Intelligent Forecasting Systems Based on the Methods of Neural Network Theory

Kostiuk,

Kryvoruchko,

Desyatko

et al. 2023

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST)

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Development of an improved method for finding a solution for neuro-fuzzy expert systems

Salnikova¹,

Cherviakova²,

Sova³

et al. 2020

EEJET

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Nowadays, artificial intelligence has entered into all spheres of human activity. However, there are some problems in the analysis of objects, for example, there is a priori uncertainty about the state of objects and the analysis takes place in a difficult situation against the background of intentional (natural) interference and uncertainty. The best solution in this situation is to integrate with the data analysis of information systems and artificial neural networks. This paper develops an improved method for finding solutions for neuro-fuzzy expert systems. The proposed method allows increasing the efficiency and reliability of making decisions about the state of the object. Increased efficiency is achieved through the use of evolving neuro-fuzzy artificial neural networks, as well as an improved procedure for their training. Training of evolving neuro-fuzzy artificial neural networks is due to learning their architecture, synaptic weights, type and parameters of the membership function, as well as the application of the procedure of reducing the dimensionality of the feature space. The analysis of objects also takes into account the degree of uncertainty about their condition. In the proposed method, when searching for a solution, the same conditions are calculated once, which speeds up the rule revision cycle and instead of the same conditions of the rules, references to them are used. This reduces the computational complexity of decision-making and does not accumulate errors in the training of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the radio-electronic environment. This example showed an increase in the efficiency of assessment at the level of 20-25 % by the efficiency of information processing

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Development of a methodology for training artificial neural networks for intelligent decision support systems

Cited by 6 publications

References 27 publications

Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Information and Intelligent Forecasting Systems Based on the Methods of Neural Network Theory

Development of an improved method for finding a solution for neuro-fuzzy expert systems

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