Study of Intelligent Prediction and Control of Workpiece Size in Traverse Grinding

Ning Ding; Long Shan Wang; Guang Fu Li; J.Z. Wang; X.W. Chen

doi:10.4028/www.scientific.net/KEM.304-305.191

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Study on Precision Grinding Technique of PCD Tool's Cutting Edge
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Study of Intelligent Prediction and Control of Workpiece Size in Traverse Grinding
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Study on Grinding Technology of Electrical Discharge Grinding of Curve Edge Polycrystalline Diamond
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Study of Intelligent Prediction and Control of Workpiece Size in Traverse Grinding

Abstract:

A size intelligent prediction control model during traverse grinding is constructed. The model is composed of the neural network prediction model, the deformation optimal adaptive control system and fuzzy control model. Dynamic Elman network is used in the prediction model. The first and the second derivative of the actual amount removed from the workpiece are added into the network input, which can greatly improve the prediction accuracy. The flexible factor is introduced to the fuzzy control model, which can self-adapt and adjust the quantification factor and scale factor in the fuzzy control. Simulation and experiment verify that the developed prediction control model is feasible and has high prediction and control precision.