AI-powered设计

AI-powered设计

人工智能(AI)和机器学习(ML)领域的进展, 结合鲁棒仿真的增加可用性, 测试, and field data sets has made engineering data science a critical component of the modern product development lifecycle. Computer-aided engineering (CAE) augmented by AI is offering manufacturers the ability to discover machine learning-guided insights, 通过物理和人工智能驱动的工作流程,探索复杂设计问题的新解决方案, 通过合作和设计融合实现更大的产品创新.

设计一代

设计一代

Augment current product development practices and multiply the productivity of engineering teams with AI technology to explore a broader population of customer pleasing, 高性能, 以及可制造的新产品设计替代品.

通过应用从概念到设计的相同的基于物理的工具来验证, 并通过ML使用组织特定约束进行引导, ope苹果客户端®DesignAI™ enables faster design convergence by confidently rejecting low-potential designs earlier in development cycles.

设计探索

设计探索

增加合作, 加速设计收敛, 并利用人工智能驱动的设计工具推动产品创新.

对于复杂几何形状的高保真建模,分析师可以使用Altair®HyperWorks® 设计资源管理器,用于实时性能预测和评估的端到端工作流. 使用ML自动化重复的任务, 设计资源管理器可以直观地为几何图形的创建和编辑执行直接建模, mid-surface提取, 表面和mid-meshing, 网格质量校正, 结合有效的装配管理和工艺指导.

优化设计

优化设计

从设计微调到设计综合, 包括复杂的多物理项目或数据集的研究, ope苹果客户端®HyperStudy® 帮助多学科团队从复杂的模型中获得洞察力, 通过各种输入来探索和创造新的概念, 确定最佳妥协, 和支持决策.

Simulation technology combined with design exploration and ML enables engineers to meet time-to-market challenges effectively, and helps teams deliver higher performing products that consider more design dimensions throughout the development process.

客户的故事

福特汽车公司

Ford used ope苹果客户端工作室®®知识 to train a classification algorithm with field data to predict the correct stamping process accurately and consistently for each new part.

读故事

AI让高保真建模变得容易 

HyperWorks shapeAI 使模型内的模式和形状识别自动化成为可能, 使用户能够选择所有相似的形状并同时编辑它们. 它使用集群对部件进行分组, allowing the user to model a small number of groups rather than a large number of individual parts.

shapeAI contains automatic feature extraction for the specified geometry itself without any additional input or intervention. Combining these features with ML algorithms in HyperWorks’ matching tools puts the power of geometric ML at the fingertips of every user. shapeAI can be used to organize components of complex models by geometric similarity so that modifications to one part can be synchronized to all.

多做健身运动
人工智能异常检测和测试平台分析

人工智能异常检测和测试平台分析

ope苹果客户端®®作曲 是一个进行数学计算、操作和可视化数据的环境吗, as well as programming and debugging scripts useful for repeated computations and process automation. Compose allows users to perform a wide variety of math operations including signal processing.

signalAI是一个增强ML信号处理能力的库. signalAI可以在时域和频域进行数据准备. 然后,它可以自动训练异常检测模型来识别异常行为. 除了, 标签数据, it can auto-train classification models to predict signal signature and to identify test or operation environment.

更多ope手机客户端signalAI
基于人工智能的动态降阶模型生成

基于人工智能的动态降阶模型生成

Reduced-order models (ROMs) are useful for incorporating detailed 3D simulation into a more computationally efficient 1D environment for system-level study. 这样的仿真工具 ope苹果客户端®EDEM™ or ope苹果客户端CFD™ 允许对时变非线性系统进行详细的研究, 但由于模拟运行时间较长, 分析通常集中在组件或子系统上. 然而,在一个完整的系统模拟的情况下, it is often sufficient to reduce component behavior to its interaction with the complete system, 改进求解器运行时间,同时仍然提供足够准确的结果.

利用ope苹果客户端的romAI人工智能工具, 三维模拟可以作为生成动态rom的训练数据. 只需要少量的3D模拟运行, 因为这种方法比传统的数据驱动方法需要更少的训练数据. romAI can work with any solver and produces highly accurate results when operating within the training space and is even stable and useful for extrapolation outside the space. The same ML technique can also be used for system identification purposes when starting from test data.

更多ope手机客户端romAI
利用现场数据进行预测分析

利用现场数据进行预测分析

Engineering data scientists and analysts use Altair to generate actionable insight from their data. ope苹果客户端工作室®®知识 is a market-leading easy-to-use ML and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code.

Engineering data science has practical applications across a wide range of product design and manufacturing problems. Sheet-metal stamping is one of the most common manufacturing processes in the automotive industry, yet it requires extensive experience and manual effort to sort out the most adequate and cost-efficient sub-process for every part.

阅读福特客户故事

模拟和数据驱动的数字双胞胎

数字双胞胎帮助组织优化产品性能, 了解产品的使用寿命, 知道何时何地进行预测性维护, 了解如何延长产品的剩余使用寿命(RUL). The Altair digital twin integration platform blends physics- and data-driven twins to support optimization throughout the products lifecycle. 我们有一个完整的, 开放, 灵活的方法,使您的数字转型愿景符合您的条件.

物理基础, 模拟驱动的数字孪生杠杆标准化, 工具独立的接口,如功能模拟接口(FMI), 基于几何的三维CAE工具的联合仿真方法, and reduced order modeling approaches to derive low fidelity models from detailed simulations. 数据驱动的双胞胎使用ML算法和数据科学优化产品性能. 从这个角度看问题能让你更快, real-time insights about the status of the product then make the appropriate operational adjustments to improve the life of the product and avoid failures.

更多ope手机客户端数码双胞胎的资料

特色资源

劳斯莱斯:工程与数据科学的融合

Looking at the traditional product life cycle we see that important design decisions tend to be made early during the concept design phase before detailed analysis or test data are available. Data analysis techniques in combination with classical engineering tools can practically help to resolve that conflict by making more useful information available earlier in the process. 因此,整个过程可以变得更加有效.

演讲

如何制造负责任的人工智能

行业领袖和如今的年轻人如何看待道德AI? 本文来自工程学.com poses some tough questions about the role AI will play in our future and how we can plan to deploy these powerful tools responsibly. The panel of industry leaders and up-and-coming engineers interviewed for this article include:

  • 詹姆斯·斯卡帕,ope苹果客户端董事长、创始人兼首席执行官
  • Carsten Buchholz,罗尔斯-罗伊斯公司结构系统设计能力主管
  • 利普森, 哥伦比亚大学研究机器人技术的教授, AI, 数字化设计与制造
  • John Estrada, a student that produced an AI model for drought stress assessments in plants
  • 孙天兰是一名学生,他发明了一种人工智能模型来检测眼睛内的疾病

技术文件

利用机器学习改进制造过程

Renishaw uses Altair signalAI to deliver advanced digital gauging with real-time melt-pool analytics. This AI-driven quality assurance process helps Renishaw identify manufacturing anomalies earlier, 开发部分更快, 并实现了稳定的生产.

技术文件

人工智能在产品设计中的未来

The panel explores the current state of the art of engineering data science and the adoption of augmented similaiotn, ai-powered设计, 以及预测数据分析.

小组讨论