Process Parameter Optimization for Incremental Forming of Aluminum Alloy 5052-H32 Sheets Using Back-Propagation Neural Network

Aiming at the shortcomings of the low efficiency optimization algorithm and the tendency to fall into the local optimal solution in the existing shallow sea geoacoustic parameters inversion research, a shallow sea geoacoustic parameters inversion method based on the Back Propagation (BP) neural network model is proposed. First, the theoretical prediction value of the shallow sea sound pressure field is obtained through the Fast Field Method (FFM). Then the mapping relationship between the predicted sound pressure field and the geoacoustic parameters value to be inverted is established according to the BP neural network model. Finally, the measured sound pressure field data is brought into the neural network to obtain the inversion results. The experimental results show that, compared with the classic simulated annealing (Simulated Annealing, SA) algorithm, the BP neural network model converts the data matching process of the optimization algorithm into the construction of a relational model, avoiding multiple matching optimization processes for the inversion results, and the inversion results can be output directly, accurately, and efficiently. Under the premise of setting the same accuracy, the calculation time of the BP neural network model for the inversion result is shortened, and the number of iterations is reduced to 5% of the SA algorithm.

In this paper, the effectiveness of the genetic algorithm-based back propagation (GABP) neural network model and its application to the breakout prediction in the continuous casting process are investigated. The formation of the sticking-type breakouts and the prediction principle of thermocouple thermometry method are analysed firstly. Then the genetic algorithm-based back propagation neural network model is proposed by fusing genetic algorithm (GA), and error back propagation neural network to offset the demerits of one paradigm by the merits of another. Finally, the GABP neural network model is applied to the breakout prediction in the continuous casting process; and the feasibility of the model is verified by the testing result with the accuracy rate of 97.56% and the prediction rate of 100%.

PDF HTML阅读 XML下载 导出引用 引用提醒 1988-2013年基于BP神经网络的植被叶面积指数遥感定量反演 DOI: 10.5846/stxb201703290547 作者: 作者单位: 南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学,南京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划资助项目（2017YFC0505505）；国家自然科学基金项目（31200534）；江苏高校优势学科建设工程资助项目；2014年江苏省普通高校学术学位研究生科研创新计划项目 Quantitative inversion of long sequential leaf area index using remote sensing based on BP neural network from 1988 to 2013 Author: Affiliation: Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing,Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province,Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province,Nanjing Forestry University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数（Leaf Area Index，LAI）高度综合了植被水平覆盖状况和垂直结构，以及枯枝落叶层厚薄和地下生物量多少，是植被影响土壤侵蚀的主要方面。区域尺度的时间序列叶面积指数揭示了区域土壤侵蚀的演化过程。因此，及时准确地掌握区域尺度上长时间序列的植被LAI，对研究土壤侵蚀动态变化与植被的关系至关重要。选择南京市1988-2013年10期遥感影像，基于反向传播（Back Propagation，BP）神经网络构建LAI反演模型，进行了长时间序列的叶面积指数反演。结合2009和2010年LAI实测值，验证与探讨了该模型的评价精度与适应性。结果表明：（1）该模型拟合度较高，2009和2010年平均相对误差、均方根误差、相关系数分别是0.2395和0.2174，0.2962和0.2581，0.7713和0.6844，各项精度评价指标均较好；（2）统计分析去除耕地后全市LAI变化，低植被覆盖（LAI<2）面积不断增加，高植被覆盖区（LAI>3）面积先减少后增加，耕地面积不断减少，符合南京市的发展变化规律；（3）主城区LAI年际变化与其他学者得到的南京市植被盖度变化趋势一致，反演结果的时序性较高。本文提出的基于反向传播神经网络模型反演长时间序列LAI是可行的，为区域尺度土壤侵蚀定量遥感监测提供新途径。 Abstract:Leaf Area Index(LAI) can reflect the horizontal coverage, vertical structure of vegetation, the thickness of the litter layer and the amount of underground biomass, which is the main aspect of vegetation affecting soil erosion. It is very important to monitor the changes in the amount of soil erosion, for useful information to guide the planning of soil and water conservation, protect the soil and water resources and control the soil erosion. Therefore, the method by which we obtain high quality and long sequential LAI at a regional scale is very important for analyzing the relationship between the dynamic changes in soil erosion and vegetation. Previous studies showed that the neural network had an incomparable superiority in terms of complex, nonlinear data fitting and pattern recognition, and had been successfully applied to inverse the LAI in Nanjing based on the multi-spectral remote sensing data derived from the Landsat 8 Operational Land Imager(OLI), four types of vegetation indices(Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI; Soil-adjusted Vegetation Index, SAVI; Modified Soil adjusted Vegetation Index, MSAVI), and measured LAI data. The results showed that the accuracy of retrieval was good. In this paper, we used the Back Propagation(BP) neural network model to inverse the LAI in Nanjing during 1988-2013 based on the data derived from Landsat 8 OLI and Landsat 5 Thematic Mapper(TM). Based on the measured values of LAI in 2009 and 2010, the evaluation accuracy and adaptability of the model were verified and discussed. The results showed that:(1) The model had a fitting of higher degree, and average relative errors(MAPE), root mean square errors(RMSE), and correlation coefficients(R) in 2009 and 2010 of 0.2395 and 0.2174, 0.2962 and 0.2581, and 0.7713 and 0.6844, respectively. Each accuracy evaluation index was good.(2) Following statistical analysis, we found that the low vegetation coverage area(LAI < 2) exhibited an increasing trend, the high vegetation coverage area(LAI > 3) presented a first decreasing and then increasing trend, while the cultivated land area decreased with the rapid development in Nanjing.(3) To analyze accurately the LAI, we extracted the LAI in the main urban area, and found that there was a relatively high inversion value, and the inter-annual change in LAI was consistent with the change in vegetation coverage in Nanjing reported by other studies. Therefore, we could see that the BP neural network model had a high accuracy for the time series LAI inversion. It provides a new way for quantitative remote sensing monitoring of regional soil erosion. Moreover, because of other potential limiting factors, such as the errors produced by the BP neural network model, the large area of the inversion area, the complexity of vegetation types and community structure, etc., the inversion accuracy of LAI through remote sensing still needs to be explored, and the inversion method improved. We will try to establish a multi-angle LAI inversion method to construct the coupling model of LAI and soil erosion or quantitative fusion of multi-source remote sensing images in the future study. 参考文献 相似文献 引证文献

In view of emerging applications of alpha+beta titanium alloys in aerospace and defense, we have aimed to develop a Back propagation neural network (BPNN) model capable of predicting the properties of these alloys as functions of alloy composition and/or thermomechanical processing parameters. The optimized BPNN model architecture was based on the sigmoid transfer function and has one hidden layer with ten nodes. The BPNN model showed excellent predictability of five properties: Tensile strength (r: 0.96), yield strength (r: 0.93), beta transus (r: 0.96), specific heat capacity (r: 1.00) and density (r: 0.99). The developed BPNN model was in agreement with the experimental data in demonstrating the individual effects of alloying elements in modulating the above properties. This model can serve as the platform for the design and development of new alpha+beta titanium alloys in order to attain desired strength, density and specific heat capacity.

Quantitative analysis of fatty acid value during rice storage based on olfactory visualization sensor technology

Process feasibility and material formability are two of the most concerning factors which are studied in Incremental Sheet Forming (ISF) process. They are two important matters under study of the scientific community with the final goal of improving the process performances and to make it reliable to allow its use for rapid prototyping and for small batch production. In recent years, the application of neural network techniques to forming processes has been a research topic for optimizing and predicting process parameters and ISF process has not been an exception. Unfortunately, there are some drawbacks in using Backpropagation (BP) neural networks, such as easily falling into local minimum point. Because of that, recently Genetic Algorithms (GA) are applied to BP networks in order to optimize them. This paper describes the application of BP networks and GA applied to BP networks for the prediction of the forming force in ISF process. These models have been developed using experimental data from several tests. The two point incremental forming (TPIF) tests were performed on a horizontal CNC milling machine and DC04 deep drawing sheets steel 0.8 mm thick were worked. The reliability of the proposed approach has been evaluated by testing the trained networks with unseen experimental data and the comparison between the two BP and GA-BP models are presented. All the details are accurately discussed in the paper.

Improving the resilience of enterprise safety production is one of the important ways to deal with the frequency of safety accidents. Based on the definition of enterprise safety production resilience, we fully consider the impacts of recovery resilience, self-organizing resilience, and learning resilience as the three dimensions of enterprise safety production resilience. We build a back propagation (BP) neural network model that analyzes the main factors of enterprise safety production resilience using the results of gray relational analysis as an input that can assess the resilience of enterprise safety production and provide a valuable reference for the improvement of an enterprise’s safety production level. The results show that the resilience of production safety obviously increased after the Chinese enterprises with low resilience (as predicted by the model) adopted the corresponding early warning methods. The gray relational degree analysis method can incorporate well the variables for the establishment of the BP neural network prediction model.

To study drivers’ driving behaviour in dilemma zones at signalized intersections, dilemma zones at signalized intersections are taken as the research objects. Based on analyzing the driving characteristics of motor vehicles. And collect the driving behavior parameters of video observations and road parameters of field observations, take these parameters as the factors which influence the drivers’ decision, conduct the significance analysis with SPSS(Statistical Product Service Solutions) for significant influencing factors, and then take these factors as the input value of BP(Back Propagation) neural network model, BP neural network model is set up and tested based on TensorFlow in Python. The prediction model of drivers’ decision-making in dilemma zone of signalized intersections under different speed limits is obtained, the research shows that the lower the speed limit, the better the accuracy of the intersection prediction. Further verified by comparison with the prediction model of drivers’ decision-making based on binary logistic, the accuracy of different prediction models is analyzed based on actual driver decisions.

To reduce the impact of wildfires on the operation of power systems, a back-propagation neural network (BPNN) model is used to evaluate the wildfire risk distribution after feature selection. Data from 14 types of wildfire-related features, including anthropogenic, geographical, and meteorological factors, were collected from public data websites and local departments. The weight ranking was calculated using filtering and wrapper methods to form five feature subsets. These are used as the input sets of the BPNN model training, and network parameters are optimized by genetic algorithm (GA). Finally, the optimal feature subset is chosen to establish the optimal BPNN model. With the optimal model, the prediction results are graded to draw a wildfire risk distribution map. Situated in medium-, high-, and very-high-risk zones are 90.26% of new fire incidents, indicating the applicability of the proposed BPNN model.

Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST.

Amidst increasing governmental regulations and global initiatives to decarbonize manufacturing, there is a growing emphasis on environmentally sustainable, energy-efficient, and cost-effective production methods. Sheet-forming processes have made it possible to manufacture a variety of products we use and depend on in today’s globalized world, in the automotive, aerospace, medical, and food industries. Stamping or deep drawing are the preferred sheet-forming methods for mass-producing thin sheet parts due to its high production rate. However, the process involves high costs associated with the fabrication of specific dies for each part. Incremental sheet forming (ISF) has emerged as a promising technique and has gained widespread recognition as an alternative to traditional sheet forming. Researchers have studied ISF for the past decades, addressing and exploring alternatives to optimize the technique. Single-point incremental forming (SPIF) is one of the most popular ISF approaches because it can be accomplished using a CNC machine, a sheet clamp, and a small forming tool to make a wide variety of products of different shapes and materials. However, the technology’s cost-saving potential needs to be better understood, and predictive models must be improved to assess the process flows required to make parts using ISF. The cost prediction model presented in this work is intended to break down the cost dependencies of the ISF process to estimate a predetermined cost for each part before it is manufactured, considering material preparation, forming, and post-processing operations. A step forward in sheet forming research will be to provide a process-based cost model that can be flexible and adaptable to different types of machinery and tooling utilized. Cost calculations of a part made with ISF require knowledge of the systema as whole, such as forming process parameters, equipment characteristics, and forming geometry capabilities. The model relies on gathering process-based costs, including facilities, labor, equipment, materials, tools, consumables, and utilities, for all factors necessary to produce parts using ISF. The process-based cost model in this work involves an analytical bottom-up cost calculation methodology that is flexible and adaptable to various machine types. The ISF process is flexible, which means that the cycle time for different parts can vary by a considerable amount depending on the process parameters chosen (e.g., step down and feed rate) and the fixture and tools used. The aim of the present work is to lay the foundation for predicting the cost-effectiveness of products produced via the ISF process by considering the key processes involved. By gaining deeper insights into all costs involved in ISF and other innovative, environmentally friendly production methods, more sustainable manufacturing practices can be facilitated. These findings will contribute to global efforts to reduce carbon emissions, support responsible consumption and production, and benefit people, economy, and environment.

With the rapid development of communication technology as well as a rapid rise in the usage of electronic devices, a growth of concerns over unintentional electromagnetic interference emitted by these devices has been witnessed. Pioneer researchers have deeply studied the relationship between the shielding effectiveness and a few mixed design parameters for cementitious composites incoporating carbon fibres by conducting physical experiments. This paper, therefore, aims to develop and propose a series of prediction models for the shielding effectiveness of cementitious composites involving carbon fibres using frequency and mixed design parameters, such as the water-to-cement ratio, fibre content, sand-to-cement ratio and aspect ratio of the fibres. A multi-variable non-linear regression model and a backpropagation neural network (BPNN) model were developed to meet the different accuracy requirements as well as the complexity requirements. The results showed that the regression model reached an R2 of 0.88 with a root mean squared error (RMSE) of 2.3 dB for the testing set while the BPNN model had an R2 of 0.96 with an RMSE of 2.64 dB. Both models exhibited a sufficient prediction accuracy, and the results also supported that both the regression and the BPNN model are reasonable for such estimation.

Mining-induced ground fissures are the main type of geological disasters found on the Loess Plateau, China, and cause great impacts on the soil properties around ground fissures. However, little research has been conducted on the quantitative relationship between ground fissures and changes in soil properties. To address this, 40 ground fissures in the Yungang mining area, Datong City, Shanxi Province, China, were investigated, and changes in soil properties (soil organic matter, soil moisture, field capacity, bulk density, soil porosity, and grain compositions) were revealed by the difference in soil properties between the edge and contrast points around ground fissures. Redundancy analyses were used to illustrate the relationships between the value (Si_DV) and percentage (Si_DP) of the difference in soil properties between the edge and contrast points, as well as the ground fissures. The characteristics of ground fissures that had a significant correlation according to Pearson correlation analysis with Si_DP were selected and analyzed via multivariate linear fitting model, random forest model, and Back Propagation (BP) neural network model, respectively. Results show that soil organic matter, soil moisture content, bulk density, field capacity, and the content of clay at the edge points were significantly less than those at the contrast points; conversely, soil porosity at the edge points was significantly greater. The average percentage of the difference between the edge points and contrast points of ground fissures in these six properties was 15.27%, while soil moisture content showed the greatest change (20.65%). The Si_DP was significantly correlated with the width, slope, and vegetation coverage of ground fissures; however, the vegetation coverage was the determining factor. BP neural network model had the greatest performance in revealing the relationships between ground fissures and changes in soil properties. The model for soil organic matter had the highest accuracy (R2 = 0.89), and all others were above 0.5. This research provides insights into the quantitative relationship between ground fissures and their impacts on soil physical properties, which can be used in conjunction with remote sensing images to rapidly assess soil erosion risks caused by mining on a large scale, given that soil physical properties are closely related to topsoil stability.

Based on statistical data from 2005 to 2019, we used the back propagation (BP) neural network model to predict the production amount of plastic waste in Chengdu. In addition to the amount of waste produced we wanted to achieve an understanding of its composition and environmental impacts. Compositions of plastic waste were analyzed by sampling. Particulate matter in the air and greenhouse gas emissions (GHGs) from plastic waste incineration, bisphenol A (BPA) from plastic waste landfills, were also evaluated. Results indicated that (a) economic development, urban construction level, and residents’ consumption were pusitively correlated to different degrees to plastic waste production; (b) the production of plastic waste in Chengdu in 2025 and 2030 will reach 865.3 and 931 kilotons (Kt), respectively; (c) high density polyethylene (HDPE) and polypropylene (PP) are the two main components of plastic waste in Chengdu and accounted for 40.17% and 24.96%, respectively; (d) different degrees of environmental impacts occurred during plastic waste incineration and landfill (taking 2019 as an example, the incineration of plastic waste in Chengdu produced between 2874.82 and 4711.73 tons of inhalable particulate matter (PM) and emitted between 725.4 and 867.4 Kt of CO2, and between 65.02 and 910.27 kg of bisphenol A (BPA) leached from sanitary landfills); (e) positive policies and measures from the beginning to the end-of-life of plastics should be carried out in the future, which would improve the level of plastic waste management in Chengdu and mitigate the side-impacts from plastic waste treatment and disposal. Implications: The implications of this article are Generation trends of plastic waste were revealed by a BP neural network model, which provided essential data for authorities to make decisions on waste management. Influencing factors affecting plastic waste generation were analyzed, which will strongly support policy considerations regarding plastic waste control. This investigation first explored and reported the compositions of plastic waste mixed with municipal solid waste (MSW), which yielded valuable information concerning plastic waste and details concerning the impacts of plastic waste disposal processes. Those results of this investigation, being published here for the first time, will guide plastic waste management in Chengdu and could also provide useful information to other cities regarding that issue.

Evaluation index system for carbon information disclosure quality in China's electric power sector based on a mutual information and back propagation neural network model