The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimenta...The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimental investigations, neural network modelling and various statistical analysis tools is designed to produce the most accurate, practical and cost-effective prediction model. The modeling procedure begins by exploring the relationships between cutting parameters known to have an influence on quality characteristics of machined parts, such as dimensional errors, form errors and surface roughness, as well as their sensitivity to the process conditions. Based on these explorations and using numerous statistical tools, the most relevant variables to include in the prediction model are identified and fused using several artificial neural network architectures. An application on CNC turning of cantilever bars demonstrates that the proposed modeling procedure can be effectively and advantageously applied to quality characteristics prediction due to its simplicity, accuracy and efficiency. The experimental validation reveals that the resulting prediction model can correctly predict the quality characteristics of machined parts under variable machining conditions.展开更多
Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob le...Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob lem, an expert system approach is proposed, wherein it will be possible to predi ct the surface finish from limited experiments. The expert system contains a kno wledge base prepared from machining data handbooks and number of experiments con ducted by turning steel rods, over a wide range of cutting parameters. With this knowledge base, the expert system predicts surface finish for different tool-w ork-piece combinations, by carrying out few experiments for each case. The prop osed expert system model is validated by carrying out a number of experiments.展开更多
文摘The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimental investigations, neural network modelling and various statistical analysis tools is designed to produce the most accurate, practical and cost-effective prediction model. The modeling procedure begins by exploring the relationships between cutting parameters known to have an influence on quality characteristics of machined parts, such as dimensional errors, form errors and surface roughness, as well as their sensitivity to the process conditions. Based on these explorations and using numerous statistical tools, the most relevant variables to include in the prediction model are identified and fused using several artificial neural network architectures. An application on CNC turning of cantilever bars demonstrates that the proposed modeling procedure can be effectively and advantageously applied to quality characteristics prediction due to its simplicity, accuracy and efficiency. The experimental validation reveals that the resulting prediction model can correctly predict the quality characteristics of machined parts under variable machining conditions.
文摘Prediction of surface finish in turning process is a difficult but important task. Artificial Neural Networks (ANN) can reliably pred ict the surface finish but require a lot of training data. To overcome this prob lem, an expert system approach is proposed, wherein it will be possible to predi ct the surface finish from limited experiments. The expert system contains a kno wledge base prepared from machining data handbooks and number of experiments con ducted by turning steel rods, over a wide range of cutting parameters. With this knowledge base, the expert system predicts surface finish for different tool-w ork-piece combinations, by carrying out few experiments for each case. The prop osed expert system model is validated by carrying out a number of experiments.
