Neural-network diagnostics of electrochemical machining

Masalimov, K. A.; Munasypov, Rustem

doi:10.3103/s1068798x17090179

Cited by 5 publications

(1 citation statement)

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“…In this algorithm, the determination of the defect during processing is based on a comparison of the current indicators of the state of the technological process (current, position of the processing elements) with the reference ones recorded in the neural network model. The disadvantage of this method is the limited use of the neural network architecture used to form a reference model based on a multilayer perceptron [11]. The information capacity of the multilayer perceptron is limited, which leads to significant limitations when trying to implement a reference model of the amplitude-frequency characteristics of the vibration indicators.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks For Diagnostics Of Metal Cutting Machine Modules

Masalimov¹,

Munasypov²

2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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The work is devoted to solving the problem online diagnostics of machine tools modules using data-based models. The authors propose a diagnostic method that includes models based on long short-term neural memory networks as a repository of frequency reference values. Data for training neural networks is a frequency spectrum reflecting the oscillations of the tool and the workpiece normal to surfaces caused by the presence of a manufacturing defect in the module element of a metalworking machine. Neural network model with long short-term memory are used for approximation the nonlinear frequency characteristics. For classification of module defects proposed a second neural network that compare the neural network model of the reference spectrum with the spectrum obtained from the actual quality parameters of the part in real time, determine the sources of defects. To evaluate the effectiveness of the method, a series of experiments were carried out with the definition of defective machine modules. An experimental result of the application of proposed method is given. Keywords-cutting machining, operational diagnostic, long short term memory, deep neural networksI.

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Section: Introductionmentioning

confidence: 99%