Continuously rising demands of legislators require a significant reduction of CO2-emission and thus fuel consumption across all vehicle classes. In this context, lightweight construction materials and designs become a...Continuously rising demands of legislators require a significant reduction of CO2-emission and thus fuel consumption across all vehicle classes. In this context, lightweight construction materials and designs become a single most important factor. The main engineering challenge is to precisely adapt the material and component properties to the specific load situation. However, metallic car body structures using “Tailored blanks” or “Patchwork structures” meet these requirements only insufficiently, especially for complex load situations (like crash). An innovative approach has been developed to use laser beams to locally strengthen steel crash structures used in vehicle bodies. The method tailors the workpiece hardness and thus strength at selected locations to adjust the material properties for the expected load distribution. As a result, free designable 3D-strengthening-patterns surrounded by softer base metal zones can be realized by high power laser beams at high processing speed. The paper gives an overview of the realizable process window for different laser treatment modes using current high brilliant laser types. Furthermore, an efficient calculation model for determining the laser track properties (depth/width and flow curve) is shown. Based on that information, simultaneous FE modelling can be efficiently performed. Chassis components are both statically and cyclically loaded. Especially for these components, a modulation of the fatigue behavior by laser-treated structures has been investigated. Simulation and experimental results of optimized crash and deep drawing components with up to 55% improved level of performance are also illustrated.展开更多
The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road ...The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road segments,intersections)and then prioritising these sites to identify a subset of high priority sites(e.g.hotspots)for additional safety audits/improvements.In contrast to the conventional EB approach,which employs a statis tical model such as the negative binomial model(NB-EB)to model crash frequency data,the recently developed CGAN-EB approach uses a conditional generative adversarial net work,a form of deep neural network,that can model any form of distributions of the crash frequency data.Previous research has shown that the CGAN-EB performs as well as or bet ter than NB-EB,however that work considered only a small range of crash data character istics and did not examine the spatial and temporal transferability.In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB.The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model(i.e.data conform to the assumptions of the NB model)and outperforms NB-EB in experi ments reflecting conditions frequently encountered in practice(i.e.low sample mean crash rates,and when crash frequency does not follow a log-linear relationship with covariates).Also,temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.展开更多
The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-param...The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-parametric EB method for modeling crash frequency data based on Conditional Generative Adversarial Networks(CGAN)is proposed and evaluated over a real-world crash data set.Unlike parametric approaches,there is no need for a pre-specified underlying relationship between dependent and independent variables in the proposed CGAN-EB and they are able to model any types of distributions.The proposed methodology is applied to real-world and simulated crash data sets.The performance of CGAN-EB in terms of model fit,predictive performance and network screening outcomes is compared with the conventional approach(NB-EB)as a benchmark.The results indicate that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction power and hotspot identification tests.展开更多
文摘Continuously rising demands of legislators require a significant reduction of CO2-emission and thus fuel consumption across all vehicle classes. In this context, lightweight construction materials and designs become a single most important factor. The main engineering challenge is to precisely adapt the material and component properties to the specific load situation. However, metallic car body structures using “Tailored blanks” or “Patchwork structures” meet these requirements only insufficiently, especially for complex load situations (like crash). An innovative approach has been developed to use laser beams to locally strengthen steel crash structures used in vehicle bodies. The method tailors the workpiece hardness and thus strength at selected locations to adjust the material properties for the expected load distribution. As a result, free designable 3D-strengthening-patterns surrounded by softer base metal zones can be realized by high power laser beams at high processing speed. The paper gives an overview of the realizable process window for different laser treatment modes using current high brilliant laser types. Furthermore, an efficient calculation model for determining the laser track properties (depth/width and flow curve) is shown. Based on that information, simultaneous FE modelling can be efficiently performed. Chassis components are both statically and cyclically loaded. Especially for these components, a modulation of the fatigue behavior by laser-treated structures has been investigated. Simulation and experimental results of optimized crash and deep drawing components with up to 55% improved level of performance are also illustrated.
文摘The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road segments,intersections)and then prioritising these sites to identify a subset of high priority sites(e.g.hotspots)for additional safety audits/improvements.In contrast to the conventional EB approach,which employs a statis tical model such as the negative binomial model(NB-EB)to model crash frequency data,the recently developed CGAN-EB approach uses a conditional generative adversarial net work,a form of deep neural network,that can model any form of distributions of the crash frequency data.Previous research has shown that the CGAN-EB performs as well as or bet ter than NB-EB,however that work considered only a small range of crash data character istics and did not examine the spatial and temporal transferability.In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB.The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model(i.e.data conform to the assumptions of the NB model)and outperforms NB-EB in experi ments reflecting conditions frequently encountered in practice(i.e.low sample mean crash rates,and when crash frequency does not follow a log-linear relationship with covariates).Also,temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.
文摘The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-parametric EB method for modeling crash frequency data based on Conditional Generative Adversarial Networks(CGAN)is proposed and evaluated over a real-world crash data set.Unlike parametric approaches,there is no need for a pre-specified underlying relationship between dependent and independent variables in the proposed CGAN-EB and they are able to model any types of distributions.The proposed methodology is applied to real-world and simulated crash data sets.The performance of CGAN-EB in terms of model fit,predictive performance and network screening outcomes is compared with the conventional approach(NB-EB)as a benchmark.The results indicate that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction power and hotspot identification tests.
