A compound multi-functional sensor was designed by the study on the on-line testing technology of wood-based panels, and its properties of shape, functions, size, resistance to special environment were studied in deta...A compound multi-functional sensor was designed by the study on the on-line testing technology of wood-based panels, and its properties of shape, functions, size, resistance to special environment were studied in details. The operational principles of different sensors, technical flow of manufacturing, development of software systems of special functions, and the assessments of technical specification were also be introduced. This sensor adopted many new technologies, such as the applications of piezoresistant effect and heat sensitive effect can effectively measure the pressure and temperature, digital signal processing technology was used to extract and treat signals, and resist interference, encapsulation technology was used to keep the normal run of sensor under a harsh environment. Thus, the on-line compound multi-functional temperature/pressure sensor can be applied better to supervise the production of wood-based panels. All technical specifications of the compound multi-functional sensor were tested and the results met the requirements of the equipments.展开更多
Taking CPU time cost and analysis accuracy into account, dynamic explicit finite ele- ment method is adopted to optimize the forming process of autobody panels that often have large sizes and complex geometry. In this...Taking CPU time cost and analysis accuracy into account, dynamic explicit finite ele- ment method is adopted to optimize the forming process of autobody panels that often have large sizes and complex geometry. In this paper, for the sake of illustrating in detail how dynamic explicit finite element method is applied to the numerical simulation of the autobody panel forming process,an example of optimization of stamping process pain meters of an inner door panel is presented. Using dynamic explicit finite element code Ls-DYNA3D, the inner door panel has been optimized by adapting pa- rameters such as the initial blank geometry and position, blank-holder forces and the location of drawbeads, and satisfied results are obtained.展开更多
In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flo...In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flow,peening distance and peening angle are determined according to the empirical and machine type.Then,the optimal value of air pressure for the whole shot peening is selected by the experimental data.Finally,the feeding speed for every shot peening path is predicted by regression equation.The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object,and four experiments are conducted.In order to design specimens for acquiring the forming data,one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens,which were peened along the middle of the two stringers.The most striking finding of this experiment is that the outer shape error range is below 3.9%,so the plate specimens can be used in predicting feeding speed of the integral panel.The second experiment is performed and results show that when the coverage reaches the limit of 80%,the minimum feeding speed is 50 mm/s.By this feeding speed,the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object,and the optimal air pressure is 0.15 MPa.Then,the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment,and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method.The algorithm is validated by forming a test part and the average deviation is 0.496 mm.It is shown that the approach can realize the forming of the integral panel precisely.展开更多
Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-err...Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-error.Despite the finite element method’s ability to replace some experimentation,it still cannot realize the design of shot peen forming processes parameters of an aircraft panel based on a known contour.This study uses an innovative model-based deep learning approach to predict aircraft panel deformation and active design the shot peening parameters.The prediction time is less than 1 second,resulting in a significant reduction in computational time.The shot peen forming process parameters and the geometric structure characteristics of the aircraft panel are divided into independent channels to establish a high-dimensional feature map,which are used to train the deep learning model.The forming contours of the 2024-T351 high-strength aluminum alloy panel are predicted under different shot peening processes.In addition,the process parameters are designed according to the known contour of the forming process.To verify the precision of the proposed method,the designed shot peen forming process is used to manufacture a single curvature aircraft panel with a curvature radius of 3500 mm.There is good agreement between the forming contour and the theoretical design contour.The maximum deformation error is less than 1 mm and its mean error is 7.8%.The mean curvature radius error is 5.668%.The proposed method provides a new and practical reference to the precise design of the shot peen-forming process.展开更多
Tubular hydroforming has attracted increased attention in the vehicle industry recently. This paper covers a complete hydroforming process design for an instrum ent panel frame by finite element simulation using the e...Tubular hydroforming has attracted increased attention in the vehicle industry recently. This paper covers a complete hydroforming process design for an instrum ent panel frame by finite element simulation using the explicit code LS-DYNA. The manufacturing process for the instrument panel frame consists of tube pre-be nding and final hydroforming. To accomplish hydroforming process design successf ully, a thorough investigation of proper combination of process parameters such as internal hydraulic pressure and axial feeding is carried out by finite element simulation to predict the tube wall thickness and shape. An optimized process parameter combination is obtained and verified by the instrument panel frame hyd roforming experiment. The experiment shows that designed process parameters can be used in real production through FEA simulation, but tubular thinned amplitu de by FEA is less than that with the experiment.展开更多
This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV tech...This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.展开更多
Since bamboo has the advantages of straight grain, beautiful color, high strength and toughness, and excellent abrasion resistance, bamboo-based panels have been widely used in the fields of vehicle, construction, shi...Since bamboo has the advantages of straight grain, beautiful color, high strength and toughness, and excellent abrasion resistance, bamboo-based panels have been widely used in the fields of vehicle, construction, ship building, furniture, and decoration to partly take the place of wood, steel, plastic etc in China. This paper briefly described the basic component units, including strip, sliver, and particle, of bamboo-based panel and pointed out that to design the structure of bamboo-based panels should follow the principle of symmetric structure, surface forming method, and structuring principle of equalizing stress. According to the processing methods and formation of component units, the authors classified the bamboo-based panels in China into 13 types and presented the manufacturing technique and uses of the bamboo products, such as plybamboo, bamboo flooring, and bamboo-wood composite products in detail. In the last part of the paper, much information were offered on the output, market, and selling prospect of each type of bamboo-based panels.展开更多
基金This project was supported by China Postdoctoral Science Funds, Jiangsu Planned Projects for Postdoctoral Research Funds and Northeast Forestry University Research Funds.
文摘A compound multi-functional sensor was designed by the study on the on-line testing technology of wood-based panels, and its properties of shape, functions, size, resistance to special environment were studied in details. The operational principles of different sensors, technical flow of manufacturing, development of software systems of special functions, and the assessments of technical specification were also be introduced. This sensor adopted many new technologies, such as the applications of piezoresistant effect and heat sensitive effect can effectively measure the pressure and temperature, digital signal processing technology was used to extract and treat signals, and resist interference, encapsulation technology was used to keep the normal run of sensor under a harsh environment. Thus, the on-line compound multi-functional temperature/pressure sensor can be applied better to supervise the production of wood-based panels. All technical specifications of the compound multi-functional sensor were tested and the results met the requirements of the equipments.
文摘Taking CPU time cost and analysis accuracy into account, dynamic explicit finite ele- ment method is adopted to optimize the forming process of autobody panels that often have large sizes and complex geometry. In this paper, for the sake of illustrating in detail how dynamic explicit finite element method is applied to the numerical simulation of the autobody panel forming process,an example of optimization of stamping process pain meters of an inner door panel is presented. Using dynamic explicit finite element code Ls-DYNA3D, the inner door panel has been optimized by adapting pa- rameters such as the initial blank geometry and position, blank-holder forces and the location of drawbeads, and satisfied results are obtained.
基金supported by the National Level Project of China。
文摘In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flow,peening distance and peening angle are determined according to the empirical and machine type.Then,the optimal value of air pressure for the whole shot peening is selected by the experimental data.Finally,the feeding speed for every shot peening path is predicted by regression equation.The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object,and four experiments are conducted.In order to design specimens for acquiring the forming data,one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens,which were peened along the middle of the two stringers.The most striking finding of this experiment is that the outer shape error range is below 3.9%,so the plate specimens can be used in predicting feeding speed of the integral panel.The second experiment is performed and results show that when the coverage reaches the limit of 80%,the minimum feeding speed is 50 mm/s.By this feeding speed,the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object,and the optimal air pressure is 0.15 MPa.Then,the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment,and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method.The algorithm is validated by forming a test part and the average deviation is 0.496 mm.It is shown that the approach can realize the forming of the integral panel precisely.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0231)Jiangsu Provincial Key Research and Development Program(No.BE2021060)The authors thank the Key Projects of Scientific Research in Colleges and Universities of Anhui Provincial Department of Education(No.KJ2021A0367).
文摘Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-error.Despite the finite element method’s ability to replace some experimentation,it still cannot realize the design of shot peen forming processes parameters of an aircraft panel based on a known contour.This study uses an innovative model-based deep learning approach to predict aircraft panel deformation and active design the shot peening parameters.The prediction time is less than 1 second,resulting in a significant reduction in computational time.The shot peen forming process parameters and the geometric structure characteristics of the aircraft panel are divided into independent channels to establish a high-dimensional feature map,which are used to train the deep learning model.The forming contours of the 2024-T351 high-strength aluminum alloy panel are predicted under different shot peening processes.In addition,the process parameters are designed according to the known contour of the forming process.To verify the precision of the proposed method,the designed shot peen forming process is used to manufacture a single curvature aircraft panel with a curvature radius of 3500 mm.There is good agreement between the forming contour and the theoretical design contour.The maximum deformation error is less than 1 mm and its mean error is 7.8%.The mean curvature radius error is 5.668%.The proposed method provides a new and practical reference to the precise design of the shot peen-forming process.
文摘Tubular hydroforming has attracted increased attention in the vehicle industry recently. This paper covers a complete hydroforming process design for an instrum ent panel frame by finite element simulation using the explicit code LS-DYNA. The manufacturing process for the instrument panel frame consists of tube pre-be nding and final hydroforming. To accomplish hydroforming process design successf ully, a thorough investigation of proper combination of process parameters such as internal hydraulic pressure and axial feeding is carried out by finite element simulation to predict the tube wall thickness and shape. An optimized process parameter combination is obtained and verified by the instrument panel frame hyd roforming experiment. The experiment shows that designed process parameters can be used in real production through FEA simulation, but tubular thinned amplitu de by FEA is less than that with the experiment.
文摘This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.
基金This study was supported by National 9th-Five-Year Plan Project (No. 96-011-02-07-02).
文摘Since bamboo has the advantages of straight grain, beautiful color, high strength and toughness, and excellent abrasion resistance, bamboo-based panels have been widely used in the fields of vehicle, construction, ship building, furniture, and decoration to partly take the place of wood, steel, plastic etc in China. This paper briefly described the basic component units, including strip, sliver, and particle, of bamboo-based panel and pointed out that to design the structure of bamboo-based panels should follow the principle of symmetric structure, surface forming method, and structuring principle of equalizing stress. According to the processing methods and formation of component units, the authors classified the bamboo-based panels in China into 13 types and presented the manufacturing technique and uses of the bamboo products, such as plybamboo, bamboo flooring, and bamboo-wood composite products in detail. In the last part of the paper, much information were offered on the output, market, and selling prospect of each type of bamboo-based panels.
