Papers by Author: Bo Di Cui

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Authors: Bo Di Cui
Abstract: Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.
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Authors: Bo Di Cui
Abstract: Surface roughness is one of the most important product quality characteristics. In this paper, experimental investigation of surface roughness was performed in high speed turning of hardened AISI P20 steel with CBN tool based on design of experiment. The influence of cutting speed, feed rate, depth of cut and nose radius on surface roughness were assessed using analysis of variance (ANOVA). Optimal cutting parameters were found to improve the machining performance. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to study the relationship between surface roughness and cutting parameters based on the proposed model.
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Authors: Bo Di Cui
Abstract: An experimental investigation was conducted to determine the effects of cutting conditions on surface roughness in finish hard turning of GCr15 steel with mixed ceramic inserts based on design of experiment. The influence of cutting speed, feed rate and nose radius on surface roughness was assessed using analysis of variance (ANOVA). The result indicated that the feed is the dominant factor on surface roughness followed by nose radius. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to describe the relationship between surface roughness and cutting parameters based on the proposed model.
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Authors: Bo Di Cui, Jian Liang Guo
Abstract: Accurate predictive modeling is an essential prerequisite for optimization and control of production in modern manufacturing environments. For slender bar turning operations, dimensional deviation is one of the most important product quality characteristics due to the low stiffness of part. In this study, radial basis function neural network is employed to investigate dimensional errors in slender bar turning. The relationship between cutting parameters and dimensional errors is firstly described by the proposed model. Simulation is provided to investigate the effects of cutting parameters on dimensional errors. Further, real-time predictive model based on radial basis function neural network is developed to perform the dimensional error monitoring during slender bar turning process. Experiments verify that the proposed in-process predictive system has the ability to monitor efficiently dimensional errors within the range that they have been trained.
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