Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a...Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a quarter of all combined greenhouse gas emissions (GHG) are associated with road transport vehicles. All these facts in association with heightened consumer awareness and energy security issues have led to automotive lightweighting as a major research theme across the globe. Almost all North American and European original equipment manufacturers (OEMs) related to automotive industry have chalked out ambitious weight reduction plans in response to stricter environmental regulations. This review entails main motives and current legislation which has prompted major OEMs to have drastic measures in bringing down vehicle weight to suggested limits. Also discussed are recent advances in developing advanced composites, and cellulose-enabled light weight automotive composites with special focus on research efforts of Center for Biocomposites and Biomaterials Processing (CBBP), University of Toronto, Canada.展开更多
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
Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible reg...Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution.展开更多
文摘Owing to unprecedented climate change issues in recent times, global automotive industry is striving hard in developing novel functional materials to improve vehicle’s fuel efficiency. It is believed that more than a quarter of all combined greenhouse gas emissions (GHG) are associated with road transport vehicles. All these facts in association with heightened consumer awareness and energy security issues have led to automotive lightweighting as a major research theme across the globe. Almost all North American and European original equipment manufacturers (OEMs) related to automotive industry have chalked out ambitious weight reduction plans in response to stricter environmental regulations. This review entails main motives and current legislation which has prompted major OEMs to have drastic measures in bringing down vehicle weight to suggested limits. Also discussed are recent advances in developing advanced composites, and cellulose-enabled light weight automotive composites with special focus on research efforts of Center for Biocomposites and Biomaterials Processing (CBBP), University of Toronto, Canada.
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.
基金Foundation item. the National Natural Science Foundation of China （No. 50875164）
文摘Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution.
