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Optimization of Tensile Residual Stress on Machined Surface in MQL Turning
Abstract:
Producing good surface integrity is one of the main challenges of the machining industry. The increase of the utilization of minimum quantity lubrication (MQL) in order to reduce the amount of lubrication induced a lack of understanding of the physics behind the residual stress generation. Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry. These residual stresses could affect the service qualify and component life significantly. Residual stress can be determined by empirical or numerical experiments for selected configurations, even if both are expensive procedures. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction after the MQL face turning process accurately. First, SA is used to train the weight and bias values of the ANN after which LM is used to fine tune the values trained by SA. Then, based on the predictions, an optimization procedure, using Genetic Algorithm (GA), is applied in order to find the best cutting conditions. At each generation, GA suggests a population of inputs that are then sent to the trained ANN in order to predict the residual stresses. The objective is to find the optimal inputs that minimize the tensile stress on the machined surface.
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Pages:
209-212
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Online since:
January 2015
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