数字孪生技术可减少汽车制造中的浪费并提高效率
技术
- 分析与建模 - 机器学习
- 传感器 - 加速度计
适用行业
- 汽车
- 金属
适用功能
- 产品研发
- 质量保证
用例
- 数字孪生
- 制造过程模拟
服务
- 测试与认证
关于客户
Patrone and Mongiello 是一家领先的一级汽车供应商，总部位于意大利波坦察蒂托。该公司成立于 1985 年，营业额持续增长，并投资于一流的工业技术来支持其汽车、农业和通用机械、冷金属成型制造业务。该公司致力于提高产品质量和减少生产浪费，并寻求一种能够加强对其钣金成型过程的监控的解决方案。
挑战
Patrone 和 Mongiello 是一家总部位于意大利的领先一级汽车供应商，他们正在寻求一种解决方案来增强对其钣金成型过程的监控。该公司旨在提高产品质量并减少生产浪费。该解决方案需要考虑应力、应变和弹性等金属板材特性，并涵盖垫块力和模具摩擦等设备操作条件。我们面临的挑战是找到一种能够准确模拟公司现有钣金成型工艺的解决方案，包括机器冲压和钣金行为、系统变量和操作条件。
解决方案
Patrone 和 Mongiello 选择由 Advanced Engineering (AE) Solutions 实施的 Altair 数字孪生解决方案来应对他们的挑战。该解决方案涉及创建一个全面的数字双胞胎来模拟公司现有的钣金成型工艺。这包括机器压力机、钣金行为、系统变量和操作条件。数字孪生使用了来自实体印刷机上的加速计和 AutoGrid 传感器的模拟数据和真实数据。 Altair 使用 Altair® Inspire™ Form 通过有限元分析 (FEA) 生成模拟数据。他们还使用 Altair® HyperStudy® 进行了实验设计 (DoE) 研究，以揭示钣金属性和设备操作设置对成形工艺性能的影响。该团队使用 Altair® romAI™ 创建高效的降阶模型 (ROM)，支持 AI 并实现非常快的仿真运行时间。 ROM 揭示了参数变化对成形过程的最终产品质量的影响。然后，该团队使用 romAI 用模拟数据训练机器学习模型。所有数据模型均通过 Altair® Panopticon™ 部署，这是一个基于云的仪表板，成型机操作员可以根据关键性能指标 (KPI) 的预期行为直观地监控实际传感器数据，并在整个钣金成型过程中部署纠正措施。
运营影响
数量效益
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