The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sens...The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.展开更多
The present document describes the experience of using a Geography Information System (GIS) tool to map the spatial distribution of the population of the State of Mexico, Mexico, for 2010. Prior to the development of ...The present document describes the experience of using a Geography Information System (GIS) tool to map the spatial distribution of the population of the State of Mexico, Mexico, for 2010. Prior to the development of the experience, dif- ferent projects, tools and products, which offered cartographic representations of the population, were presented, se- lecting one that was developed to function in Idrisi Taiga GIS. Likewise, the GIS tool and the process followed to apply it to a case study in the State of Mexico are described. The result is a raster image that shows the distribution of the population on the analyzed state, offering the possibility to identify the zones where the population concentrates and disperses and to produce an opinion from a geographic point of view on what the origin of these distribution patterns is. Among the conclusions from the analysis of results, distinguishable are the applications that the obtained image can have;for instance, the population can be estimated by basins or risk zones by only adding the values of the cells inside these zones. It is also concluded that the degree of detail expressed by the spatial distribution image of the population will depend on several factors, among them the scale and resolution of the input information and the resolution fixed for the output information.展开更多
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
文摘The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.
文摘The present document describes the experience of using a Geography Information System (GIS) tool to map the spatial distribution of the population of the State of Mexico, Mexico, for 2010. Prior to the development of the experience, dif- ferent projects, tools and products, which offered cartographic representations of the population, were presented, se- lecting one that was developed to function in Idrisi Taiga GIS. Likewise, the GIS tool and the process followed to apply it to a case study in the State of Mexico are described. The result is a raster image that shows the distribution of the population on the analyzed state, offering the possibility to identify the zones where the population concentrates and disperses and to produce an opinion from a geographic point of view on what the origin of these distribution patterns is. Among the conclusions from the analysis of results, distinguishable are the applications that the obtained image can have;for instance, the population can be estimated by basins or risk zones by only adding the values of the cells inside these zones. It is also concluded that the degree of detail expressed by the spatial distribution image of the population will depend on several factors, among them the scale and resolution of the input information and the resolution fixed for the output information.
