A mathematical model of resin flow and temperature variation in the filling stage of the resin transfer molding (RTM) is developed based on the control volume/finite element method (CV/FEM). The effects of the heat tr...A mathematical model of resin flow and temperature variation in the filling stage of the resin transfer molding (RTM) is developed based on the control volume/finite element method (CV/FEM). The effects of the heat transfer and chemical reaction of the resin on the flow and temperature are considered. The numerical algorithm of the resin flow and temperature variation in the process of RTM are studied. Its accuracy and convergence are analyzed. The comparison of temperature variations between experimental results and model predictions is carried out for two RTM cases. Result shows that the model is efficient for evaluating the flow and temperature variation in the filling stage of RTM and there is a good coincidence between theory and experiment.展开更多
Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them conside...Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.展开更多
With the rapid development of smart wearable devices, flexible and biodegradable sensors are in urgent needs. In this study, ‘‘green" electrically conductive Ag nanowire (Ag NW)/cellulose nanofiber (CNF) hybrid...With the rapid development of smart wearable devices, flexible and biodegradable sensors are in urgent needs. In this study, ‘‘green" electrically conductive Ag nanowire (Ag NW)/cellulose nanofiber (CNF) hybrid nanopaper was fabricated to prepare flexible sensors using the facial solution blending and vacuum filtration technique. The amphiphilic property of cellulose is beneficial for the homogeneous dispersion of Ag NW to construct effective electrically conductive networks. Two different types of strain sensors were designed to study their applications in strain sensing. One was the tensile strain sensor where the hybrid nanopaper was sandwiched between two thermoplastic polyurethane (TPU) films through hot compression, and special micro-crack structure was constructed through the pre-strain process to enhance the sensitivity. Interestingly, typical pre-strain dependent strain sensing behavior was observed due to different crack densities constructed under different pre-strains. As a result, it exhibited an ultralow detection limit as low as 0.2%, good reproducibility under different strains and excellent stability and durability during 500 cycles (1% strain, 0.5 mm/min). The other was the bending strain sensor where the hybrid nanopaper was adhered onto TPU film, showing stable and recoverable linearly sensing behavior towards two different bending modes (tension and compression). Importantly, the bending sensor displayed great potential for human motion and physiological signal detection. Furthermore, the hybrid nanopaper also exhibited stable and reproducible negative temperature sensing behavior when it was served as a temperature sensor. This study provides a guideline for fabricating flexible and biodegradable sensors.展开更多
文摘A mathematical model of resin flow and temperature variation in the filling stage of the resin transfer molding (RTM) is developed based on the control volume/finite element method (CV/FEM). The effects of the heat transfer and chemical reaction of the resin on the flow and temperature are considered. The numerical algorithm of the resin flow and temperature variation in the process of RTM are studied. Its accuracy and convergence are analyzed. The comparison of temperature variations between experimental results and model predictions is carried out for two RTM cases. Result shows that the model is efficient for evaluating the flow and temperature variation in the filling stage of RTM and there is a good coincidence between theory and experiment.
基金Supported by Guangzhou Nansha District Bureau of Economy & Trade, Science & Technology, Information, Project (201103003)the Fundamental Research Funds for the Central Universities (2012QNA5012)+1 种基金Project of Education Department of Zhejiang Province (Y201223159)Technology Foundation for Selected Overseas Chinese Scholar of Zhejiang Province (J20120561)
文摘Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.
基金supported by the National Natural Science Foundation of China(51803191)the China Postdoctoral Science Foundation(2018M642782)the 111 project(D18023)
文摘With the rapid development of smart wearable devices, flexible and biodegradable sensors are in urgent needs. In this study, ‘‘green" electrically conductive Ag nanowire (Ag NW)/cellulose nanofiber (CNF) hybrid nanopaper was fabricated to prepare flexible sensors using the facial solution blending and vacuum filtration technique. The amphiphilic property of cellulose is beneficial for the homogeneous dispersion of Ag NW to construct effective electrically conductive networks. Two different types of strain sensors were designed to study their applications in strain sensing. One was the tensile strain sensor where the hybrid nanopaper was sandwiched between two thermoplastic polyurethane (TPU) films through hot compression, and special micro-crack structure was constructed through the pre-strain process to enhance the sensitivity. Interestingly, typical pre-strain dependent strain sensing behavior was observed due to different crack densities constructed under different pre-strains. As a result, it exhibited an ultralow detection limit as low as 0.2%, good reproducibility under different strains and excellent stability and durability during 500 cycles (1% strain, 0.5 mm/min). The other was the bending strain sensor where the hybrid nanopaper was adhered onto TPU film, showing stable and recoverable linearly sensing behavior towards two different bending modes (tension and compression). Importantly, the bending sensor displayed great potential for human motion and physiological signal detection. Furthermore, the hybrid nanopaper also exhibited stable and reproducible negative temperature sensing behavior when it was served as a temperature sensor. This study provides a guideline for fabricating flexible and biodegradable sensors.
