利用人工智能模拟工程专业知识：宝马的耐撞性优化
技术
- 分析与建模 - 机器学习
适用行业
- 汽车
- 教育
适用功能
- 人力资源
- 产品研发
用例
- 预测性维护
- 时间敏感网络
服务
- 数据科学服务
关于客户
宝马集团是全球领先的高档汽车和摩托车制造商，在15个国家拥有31个生产和装配工厂以及全球销售网络。该公司还是优质金融和移动服务的提供商。作为创新领导者，宝马通过智能材料组合、数字化技术转型和资源节约型生产引领了生产技术和可持续发展的趋势。该公司还通过价值链的灵活性和持续优化来确保竞争力。
挑战
宝马是一家领先的高档汽车和摩托车制造商，在确保碰撞事件中乘客的安全方面面临着重大挑战。在碰撞期间协调一系列事件的复杂性，包括螺栓断裂或零件接触等离散事件的时间安排，是一项艰巨的任务。关键性能指标，包括能量吸收、失效前的峰值力水平、局部位移和重量，要么过于复杂，要么导致过度约束的优化问题。这使得在快节奏的产品开发过程中自信地验证耐撞性变得困难。我们面临的挑战是找到一种解决方案来简化这一过程，减少所需的设计迭代次数，并确保乘客的最高安全水平。
解决方案
BMW 采用 HyperWorks 中的 Altair 集成机器学习解决方案来生成模仿工程专业知识的优化约束。该解决方案涉及使用聚类（一种无监督机器学习算法）来帮助工程师了解碰撞运动学如何影响关键性能指标。然后，通过使用模拟工程决策的分类器，在优化过程中实施有利的碰撞运动学。这种方法简化了优化问题的表述，并减少了开发复杂汽车碰撞结构所需的设计迭代次数。由机器学习驱动的工作流程增强并扩展了宝马现有的工程专业知识，使他们能够更有效地将计算和人力资源分配给高价值的模拟、分析和验证工作。
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