ANN-Based Hybrid Algorithm Supporting Composition Control of Casting Slip in Manufacture of Ceramic Insulators

Kowalski, Arkadiusz; Rosienkiewicz, Maria

doi:10.1007/978-3-319-47364-2_34

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…These examples indicate that machine learning methods can be used as effective predictive tools in controlling machining processes. Optimization methods can also be successfully used as components of production process control, as evidenced by various publications [ 6 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Świć

Wołos

Gola

et al. 2020

Sensors

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The article presents an original machine-learning-based automated approach for controlling the process of machining of low-rigidity shafts using artificial intelligence methods. Three models of hybrid controllers based on different types of neural networks and genetic algorithms were developed. In this study, an objective function optimized by a genetic algorithm was replaced with a neural network trained on real-life data. The task of the genetic algorithm is to select the optimal values of the input parameters of a neural network to ensure minimum deviation. Both input vector values and the neural network’s output values are real numbers, which means the problem under consideration is regressive. The performance of three types of neural networks was analyzed: a classic multilayer perceptron network, a nonlinear autoregressive network with exogenous input (NARX) prediction network, and a deep recurrent long short-term memory (LSTM) network. Algorithmic machine learning methods were used to achieve a high level of automation of the control process. By training the network on data from real measurements, we were able to control the reliability of the turning process, taking into account many factors that are usually overlooked during mathematical modelling. Positive results of the experiments confirm the effectiveness of the proposed method for controlling low-rigidity shaft turning.

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Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Świć

Wołos

Gola

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Introduction to the organization of the processes of business continuity management reduces the impact of potential failures / disturbances on the activities of the organization. These disturbances can affect different areas of the company's activities, leading to turbulences and reinforcing management and structural changes in companies [1] [2].…”

Section: Introductionmentioning

confidence: 99%