In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feedforward multilayered artificial neural network (ANN) roughness prediction model, using the LevenbergMarquardt backpropagation training algorithm ...
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WhatsApp: +86 18203695377In the face milling process, a deep neural network as advanced machine learning system is used to automatically detect tool wear during chip formation process [70]. Drill wear tolerance analysis and optimization is implemented utilizing an adaptive neuro fuzzy genetic algorithm approach for longterm usage of cutting tool to maximize cutting ...
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WhatsApp: +86 18203695377Azlan Mohd Zain et al. presented artificial neural network (ANN) model for predicting the surface roughness performance measure in the milling process . The Bayesian linear regression is utilized to construct a surface roughness predictive model in the milling process conducted by Kong et al. . Massive research has been done and succeeded in ...
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WhatsApp: +86 18203695377In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a ) values in Al alloy 7075T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a highspeed ...
WhatsApp: +86 18203695377Experimental Setup. Aluminum alloy (AA 5083) material has been selected for the end milling operations. Tables 1 and 2 contain specific information on the AA 5083 alloy's chemical content and mechanical characteristics. The CNC milling machine used for the investigation is the Intelys C3000 CNC milling machine, a threeaxis, highspeed machine.
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WhatsApp: +86 18203695377This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVDTiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed ...
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WhatsApp: +86 18203695377This study aims to find the optimized parameters for surveying the milling process of S50C steel in a minimum quantity lubrication (MQL) environment using a support vector machinegenetic algorithm (SVMGA). Based on the experimental matrix designed by the Taguchi method, surface roughness and cutting force data were collected corresponding to each experiment with changes in input parameters ...
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WhatsApp: +86 182036953771. Sand making. In crushed aggregates production, up to 30% (rock dependent) of materials acquired from the bedrock are reduced to sizes smaller than 4 mm, meaning they end up as waste. These stockpiles are a perfect feed for the production of manufactured sand. 2.
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