Advancements in the fields of artificial intelligence (AI) and machine learning (ML), combined with the increased availability of robust simulation, testing, 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, explore new solutions to complex design problems through physics and AI-driven workflows, and achieve greater product innovation through collaboration and design convergence.
Augment current product development practices and multiply the productivity of engineering teams with AI technology to explore a broader population of customer pleasing, high performing, and manufacturable new product design alternatives.
By applying the same physics-based tools used for verification from concept to design, and through to sign-off and guided by ML using organizational specific constraints, 米乐体育m6官网® DesignAI™ enables faster design convergence by confidently rejecting low-potential designs earlier in development cycles.
Increase collaboration, speed up design convergence, and drive product innovation with AI-powered design tools.
For high-fidelity modeling of complex geometries, analysts can use 米乐体育m6官网® HyperWorks® Design Explorer an end-to-end workflow for real time performance prediction and evaluation. Automating repetitive tasks using ML, Design Explorer intuitively performs direct modeling for geometry creation and editing, mid-surface extraction, surface and mid-meshing, mesh quality correction, combined with efficient assembly management and process guidance.
From design fine-tuning through to design synthesis, including complex multiphysics projects or the study of sets of data, 米乐体育m6官网® HyperStudy® helps multidisciplinary teams gain insight from complex models, explore and create new concepts with a variety of inputs, determine best compromises, and support decision-making.
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.