To obtain the form error of micro-structured surfaces robustly and accurately, a form er- ror evaluation method was developed based on the real coded genetic algorithm （RCGA）. The meth- od employed the average squar...To obtain the form error of micro-structured surfaces robustly and accurately, a form er- ror evaluation method was developed based on the real coded genetic algorithm （RCGA）. The meth- od employed the average squared distance as the matching criterion. The point to surface distance was achieved by use of iterative method and the modeling of RCGA for the surface matching was also presented in detail. Parameter selection for RCGA including the crossover rate and population size was discussed. Evaluation results of series simulated surfaces without form error show that this method can achieve the accuracy of root mean square deviation （ Sq ） less than 1 nm and surface pro- file error （ St ） less than 4 nm. Evaluation of the surfaces with different simulated errors illustrates that the proposed method can also robustly obtain the form error with nano-meter precision. The e- valuation of actual measured surfaces further indicates that the proposed method is capable of pre- cisely evaluating micro-structured surfaces.展开更多
A fast tool servo (FTS) system is developed for the fabrication of non-rotationally symmetric micro-structured surfaces using single-point diamond turning machines.The constructed FTS employs a piezoelectric tube actu...A fast tool servo (FTS) system is developed for the fabrication of non-rotationally symmetric micro-structured surfaces using single-point diamond turning machines.The constructed FTS employs a piezoelectric tube actuator (PZT) to actuate the diamond tool and a capacitive probe as the feedback sensor.To compensate the inherent nonlinear hysteresis behavior of the piezoelectric actuator,Proportional Integral (PI) feedback control is implemented.Besides,a feed-forward control based on a simple feed-forward predictor has been added to achieve better tracking performance.Experimental results indicate that error motions in the performance of the system caused by hysteresis can be reduced greatly and the micro-structured surface is successfully fabricated by implementing the FTS.展开更多
Microplastics are a growing global concern,particularly in drinking water,due to their potential negative impacts on human health.To effectively monitor,quantify and understand the sources and implications of micropla...Microplastics are a growing global concern,particularly in drinking water,due to their potential negative impacts on human health.To effectively monitor,quantify and understand the sources and implications of microplastics in water,it is critical to identify their physical and chemical properties.However,existing laboratory-based methods popularly used for characterising microplastics have several limitations.Using a novel method,this study explored the feasibility of quantifying the physical properties of microplastics in water.Specifically,we utilised a portable holographic camera to record digital holograms of commercial microplastics floating in water.Furthermore,we developed a simple Python algorithm to determine the size of the microplastics from the particle images.This study also evaluated and compared the performance of two deep-learning architectures,MobileNetV2 and ResNet101,in classifying the shapes of the microplastic particles into spherical and hemispherical shapes.Findings from this study demonstrate the capability of the proposed holographic system to rapidly and automatically produce particle images of microplastics while simultaneously measuring their sizes.Performance metrics,including accuracy,precision,recall,F1 score,confusion matrix and training time,showed that MobileNetV2 achieved the best performance despite being a more lightweight model with fewer parameters than ResNet101.Therefore,MobileNetV2 was recommended for classifying the shapes of microplastics from particle images.The time and cost-effectiveness of the proposed digital holographic method make it suitable for large-scale monitoring of microplastics in water.This will be significant in identifying the sources,understanding their behaviour and reducing the associated health risks to humans.展开更多
基金Supported by the Programme of Introducing Talents of Discipline to Universities (B07018)
文摘To obtain the form error of micro-structured surfaces robustly and accurately, a form er- ror evaluation method was developed based on the real coded genetic algorithm （RCGA）. The meth- od employed the average squared distance as the matching criterion. The point to surface distance was achieved by use of iterative method and the modeling of RCGA for the surface matching was also presented in detail. Parameter selection for RCGA including the crossover rate and population size was discussed. Evaluation results of series simulated surfaces without form error show that this method can achieve the accuracy of root mean square deviation （ Sq ） less than 1 nm and surface pro- file error （ St ） less than 4 nm. Evaluation of the surfaces with different simulated errors illustrates that the proposed method can also robustly obtain the form error with nano-meter precision. The e- valuation of actual measured surfaces further indicates that the proposed method is capable of pre- cisely evaluating micro-structured surfaces.
基金Funded by the National High-tech R&D Program ("863" Program) of China (No.2006AA04Z314)
文摘A fast tool servo (FTS) system is developed for the fabrication of non-rotationally symmetric micro-structured surfaces using single-point diamond turning machines.The constructed FTS employs a piezoelectric tube actuator (PZT) to actuate the diamond tool and a capacitive probe as the feedback sensor.To compensate the inherent nonlinear hysteresis behavior of the piezoelectric actuator,Proportional Integral (PI) feedback control is implemented.Besides,a feed-forward control based on a simple feed-forward predictor has been added to achieve better tracking performance.Experimental results indicate that error motions in the performance of the system caused by hysteresis can be reduced greatly and the micro-structured surface is successfully fabricated by implementing the FTS.
基金supported by Defence and Security Accelerator(DASA),United Kingdom[Contracts DSTL0000005130 and DSTLX100013220].
文摘Microplastics are a growing global concern,particularly in drinking water,due to their potential negative impacts on human health.To effectively monitor,quantify and understand the sources and implications of microplastics in water,it is critical to identify their physical and chemical properties.However,existing laboratory-based methods popularly used for characterising microplastics have several limitations.Using a novel method,this study explored the feasibility of quantifying the physical properties of microplastics in water.Specifically,we utilised a portable holographic camera to record digital holograms of commercial microplastics floating in water.Furthermore,we developed a simple Python algorithm to determine the size of the microplastics from the particle images.This study also evaluated and compared the performance of two deep-learning architectures,MobileNetV2 and ResNet101,in classifying the shapes of the microplastic particles into spherical and hemispherical shapes.Findings from this study demonstrate the capability of the proposed holographic system to rapidly and automatically produce particle images of microplastics while simultaneously measuring their sizes.Performance metrics,including accuracy,precision,recall,F1 score,confusion matrix and training time,showed that MobileNetV2 achieved the best performance despite being a more lightweight model with fewer parameters than ResNet101.Therefore,MobileNetV2 was recommended for classifying the shapes of microplastics from particle images.The time and cost-effectiveness of the proposed digital holographic method make it suitable for large-scale monitoring of microplastics in water.This will be significant in identifying the sources,understanding their behaviour and reducing the associated health risks to humans.
