技术
- 传感器 - 自动驾驶传感器
适用行业
- 汽车
- 教育
适用功能
- 产品研发
用例
- 智能停车
- 车辆到基础设施 (V2I)
服务
- 培训
关于客户
Scuderia Mensa 是一支来自德国莱茵美因应用科技大学的学生方程式赛车队。该团队自2006年以来一直参加国际学生方程式比赛,设计和制造赛车。该团队由 Viola Mc Kearney 管理,负责该项目的实施、管理和融资。该团队的目标是制造一辆能够在世界各地的学生方程式比赛中与其他车队竞争的汽车。为了实现这一目标,团队依赖于 Bricsys 和 MERViSOFT 等合作伙伴的支持。
挑战
Scuderia Mensa 是来自德国莱茵美因应用科技大学的学生方程式赛车队,自 2006 年以来一直为国际学生方程式比赛制造赛车。该团队面临的挑战是为其 2020 年赛车设计单体壳。考虑到项目的复杂性,设计过程需要一种简单且易于使用的工具。该团队还需要确保学生能够轻松采用该工具,以平衡学习与参与学生方程式计划。此外,该团队依赖合作伙伴的支持来建造车辆,这给项目增加了另一层复杂性。
解决方案
2020 年,Scuderia Mensa 与 Bricsys 合作,使用 BricsCAD® Mechanical 进行汽车零部件设计。 BricsCAD® Mechanical 被认为是该团队项目的一个简单直观的工具。为了促进该软件的采用,BRICSYS 当地合作伙伴 MERViSOFT 为学生提供了参加 BricsCAD 培训课程的机会。这种培训对于团队来说至关重要,因为它使他们能够在学习之外的项目中发展技能。培训确保了软件的采用既快速又简单,使团队能够专注于汽车的设计和构造。
运营影响
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Monitoring of Pressure Pumps in Automotive Industry
A large German/American producer of auto parts uses high-pressure pumps to deburr machined parts as a part of its production and quality check process. They decided to monitor these pumps to make sure they work properly and that they can see any indications leading to a potential failure before it affects their process.