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, Altair® 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 Altair® 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, Altair® 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.
The traditional engineering processes ensure that safe and robust products are being developed. However, the growing demand for innovative solutions in short time scales drives the need for a cultural change in the way we work in engineering. 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. Consequently the entire process can become more effective.
How do industry leaders and today's young minds look at ethical AI? This article from Engineering.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:
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, develop parts quicker, and realizes a stable production.
The simulation-driven design changed product development forever, enabling engineers to reduce design iterations and prototype testing. Increasing scientific computing power expanded the opportunity to apply analysis, making large design studies possible within the timing constraints of a program. Now engineering data science is transforming product development again. Augmented simulation features inside CAE tools are accelerating the design decision process with machine learning. The power of ML-based AI-powered design combined with physics-based simulation-driven design leveraging the latest in high-performance computing is just being realized. While predictive data analytics techniques long associated with business-centric data are being aggressively deployed on asset-centric data to enhance manufacturing, warranty, and testing performance. The panel explores the current state of the art of engineering data science and the adoption of augmented simulation, AI-powered design, and predictive data analytics. Moderated by Dr. Carsten Bange, Founder and CEO of Business Application Research Center (BARC) GmbH, the panel includes Dr. Fatma Kocer, VP of Engineering Data Science, Anthony McLoughlin, VP of Sales Data Analytics, Christian Kehrer, Business Development Manager System Modeling, and Marco Fliesser of Altair. The discussion is about 35 minutes long, and was aired during Future.AI in June 2021. Ready to see how your company can drive innovation with AI-powered design? Contact our solutions experts today. View all Future.AI 2021 Presentations