Neural network classification of surface quality after hard turning of 105WCr6 steel

The paper presents the results of a surface quality study after hard turning on a CNC lathe. Ring workpieces made of 105WCr6 steel and hardened to HRC 55 are used in this work. Data was obtained on surface quality and type of chips in a three-factor experiment for end face cutting. In order to asses...

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Veröffentlicht in:IOP conference series. Materials Science and Engineering 2019-05, Vol.537 (3), p.32056
Hauptverfasser: Rastorguev, D A, Sevastyanov, A A
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description The paper presents the results of a surface quality study after hard turning on a CNC lathe. Ring workpieces made of 105WCr6 steel and hardened to HRC 55 are used in this work. Data was obtained on surface quality and type of chips in a three-factor experiment for end face cutting. In order to assess the surface quality, it was photographed on an optical microscope with 4, 10, 40 times magnification. The surface quality was evaluated by traces of processing and divided into three types: the absence of moire, a clear moire, and an intermediate type of surface. The chip morphology was divided into the following categories: discontinuous, snarled and ribbon chips. To predict both parameters for different cutting conditions artificial neural networks (ANNs) were used. Different ANNs are applied to achieve the best classification results. In this work probabilistic neural network (PNN), feedforward network and learning vector quantization (LVQ) network are used. The results of modeling all networks are similar and can be used for technological preparation of production.
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subjects Artificial neural networks
Chips
Classification
Morphology
Neural networks
Optical microscopes
Quality assessment
Rapid prototyping
Surface properties
Turning (machining)
Vector quantization
Workpieces
title Neural network classification of surface quality after hard turning of 105WCr6 steel
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