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
A typical problem in Visual Analytics(VA)is that users are highly trained experts in their application domains,but have mostly no experience in using VA systems.Thus,users often have difficulties interpreting and work...A typical problem in Visual Analytics(VA)is that users are highly trained experts in their application domains,but have mostly no experience in using VA systems.Thus,users often have difficulties interpreting and working with visual representations.To overcome these problems,user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps.Different types of user assistance can be applied to extend the power of VA,enhance the user’s experience,and broaden the audience for VA.Although different approaches to visualization onboarding and guidance in VA already exist,there is a lack of research on how to design and integrate them in effective and efficient ways.Therefore,we aim at putting together the pieces of the mosaic to form a coherent whole.Based on the Knowledge-Assisted Visual Analytics model,we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction.As a result,we clarify and discuss the commonalities and differences between visualization onboarding and guidance,and discuss how they benefit from the integration of knowledge extraction and exploration.Finally,we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance,and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.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.
基金the Austrian Science Fund(FWF)as part of the projects VisOnFire and KnoVA(#P27975-NBL,#P31419-N31)the Vienna Science and Technology Fund(WWTF)via the grant ICT19-047(GuidedVA)+1 种基金the Austrian Ministry for Transport,Innovation and Technology(BMVIT)under the ICT of the Future program via the SEVA project(#874018)the FFG,Contract No.854184:“Pro2Future”is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry for Transport,Innovation and Technology,the Austrian Federal Ministry for Digital and Economic Affairs,and of the Provinces of Upper Austria and Styria.COMET is managed by the Austrian Research Promotion Agency FFG.
文摘A typical problem in Visual Analytics(VA)is that users are highly trained experts in their application domains,but have mostly no experience in using VA systems.Thus,users often have difficulties interpreting and working with visual representations.To overcome these problems,user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps.Different types of user assistance can be applied to extend the power of VA,enhance the user’s experience,and broaden the audience for VA.Although different approaches to visualization onboarding and guidance in VA already exist,there is a lack of research on how to design and integrate them in effective and efficient ways.Therefore,we aim at putting together the pieces of the mosaic to form a coherent whole.Based on the Knowledge-Assisted Visual Analytics model,we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction.As a result,we clarify and discuss the commonalities and differences between visualization onboarding and guidance,and discuss how they benefit from the integration of knowledge extraction and exploration.Finally,we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance,and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.
