Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificia...Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.展开更多
The objectives of this paper are to （I） quantify the effects of age and other key factors on bridge deterioration rates, and （2） provide bridge managers with strategic forecasting tools. A model for forecasting su...The objectives of this paper are to （I） quantify the effects of age and other key factors on bridge deterioration rates, and （2） provide bridge managers with strategic forecasting tools. A model for forecasting substructure conditionisestimated from the National Bridge Inventory that includes the effects of bridge material, design load, structural type, operating rating, average daily traffic, water, and the state where the bridge is located. Bridge age is the quantitative independent variable. The relationship between age and substructure condition is a fourth-order polynomial. Some of the key findings are： （I） a bridge substructure is expected to lose from 0.52 to 0.11 rating points per decade as it ages from 10 to 70 years; （2） levels of deterioration increase significantly as the material changes from concrete, to steel, to timber; （3） slab bridges have lower levels of deterioration than other structures; （4） bridges that span water have lower condition ratings; （5） bridges with higher operating ratingshave higher condition ratings; and （6） substructure condition ratings vary significantly among states.展开更多
文摘Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.
文摘The objectives of this paper are to （I） quantify the effects of age and other key factors on bridge deterioration rates, and （2） provide bridge managers with strategic forecasting tools. A model for forecasting substructure conditionisestimated from the National Bridge Inventory that includes the effects of bridge material, design load, structural type, operating rating, average daily traffic, water, and the state where the bridge is located. Bridge age is the quantitative independent variable. The relationship between age and substructure condition is a fourth-order polynomial. Some of the key findings are： （I） a bridge substructure is expected to lose from 0.52 to 0.11 rating points per decade as it ages from 10 to 70 years; （2） levels of deterioration increase significantly as the material changes from concrete, to steel, to timber; （3） slab bridges have lower levels of deterioration than other structures; （4） bridges that span water have lower condition ratings; （5） bridges with higher operating ratingshave higher condition ratings; and （6） substructure condition ratings vary significantly among states.
