Altair Engineering (Altair)
Canvas Category Software : Engineering : Simulation
Business is complex. But in complexity, there is opportunity for innovative solutions. Our comprehensive, open-architecture solutions for data analytics & AI, computer-aided engineering, and high-performance computing (HPC), enable design and optimization for high performance, innovative, and sustainable products and processes in an increasingly connected world.
Assembly Line
AI predicts head-impact crash results at blazing speed
Feel The Hit: Pushing the boundaries of tennis racket manufacturing with 3D printing
Additive Appliances’ tennis racket dampener is additively manufactured using HP’s Multi Jet Fusion technology, with the build volume of the 5200 platform said to be capable of processing thousands of parts at once. The parts, printed in BASF’s Ultrasint TPU material, measure between around 15 to 20 millimetres, and weigh less than 1 gram – up to 70% lighter than the minimal mass requirement of a traditional dampener.
For the design of the components, Additive Appliances has leant on a set of internally developed equations that are transformed into CAD designs through implicit modelling software, such as Altair’s Sulis platform, with the equations being validated using advanced simulation techniques like Optimad Engineering’s proprietary software, before extensive in-house testing is performed with vibrometers and sound spectrum analysers. Post-print, chemical smoothing can help to enhance the aesthetics of the part but has no impact on the mechanical properties and so it can be quicker and cheaper to forego this step.
Altair Expands Digital Engineering Technology with Acquisition of OmniV
Altair (Nasdaq: ALTR), a global leader in computational science and artificial intelligence (AI), acquired OmniV, a technology out of XLDyn, a product development software company based in southeast Michigan. OmniV empowers open model-based systems engineering (MBSE) practice across systems, simulation, test, product development, and controls engineering by formalizing the development, integration, and use of models to inform enterprise and program decision-making.
OmniV eliminates the silos that occur between high-level system modeling and simulation, as well as detailed, domain-specific modeling and simulation. OmniV is vendor agnostic and can connect to various enterprise data stores and verification and validation methods – including those from third-party vendors – to support program goals. OmniV brings together cross-domain product development activities using the MBSE methodology in a fully integrated and easy-to-use tool.
Assystem Creates a Digital Twin for Nuclear Plants with Altair
Meet the organization helping aviation companies harness digital twins
NIAR works with government agencies, eVTOL manufacturers, and commercial aircraft OEMs like Boeing to test parts for compliance with FAA regulations, and with the FAA itself on certification by analysis methodologies for airframe crashworthiness and ditching, according to Gerardo Olivares, senior research scientist and director at NIAR. The industry has outsourced parts of these processes to organizations like NIAR in an effort to lower costs.
Olivares told Emerging Tech Brew that NIAR uses digital twins for flight testing, design, and test safety in devices like pilot seats, and to assist in FAA certification. He said its digital twin tech is developed with the help of Altair, a tech company that specializes in simulation software, among other things.
LG, Altair build AI-powered validation platform for automotive parts
LG Electronics Inc., an industry frontrunner in applying artificial intelligence to home appliances, said on Wednesday it has joined forces with Altair Engineering Inc., a US tech firm, in developing an AI-powered validation platform for automotive parts.
Integrating AI technology into the vehicle component development process will provide LG’s clients with more reliable and high-quality solutions for products, including infotainment systems, LG said. The South Korean electronics company said the new platform leverages a machine learning algorithm to accurately predict and measure product performance from an early stage of the design validation process.
Ford's Vijayakumar Kempuraj on Digital Twin Adoption | Future Says
Use Machine Learning to Implement Effective Predictive Maintenance
Demystifying Industry 4.0
Build a Complete Analytics Pipeline to Optimize Smart Factory Operations
Rolls-Royce Finds New-Engine Benefits in Old Test Data
The goal, according to Peter Wehle, head of innovation, research and testing at RRD, is to use this information to reduce new-engine weight and mass, while maintaining structural integrity.
Both parties are hopeful that using ML and AI will significantly reduce the number of sensors needed to obtain present and future data, thereby saving RRD millions of euros annually. According to Mahalingam, the software lets engineers choose the data they want from a data silo, select the algorithms they want to employ and decide whether or not they want to use a neural network to train an ML model.
Wehle notes that the disruptive tool is based on the interaction between a communication endpoint of the engine simulation and neighboring points. It carefully analyzes the effects of loads on physical structures.
How Machine Learning Techniques Can Help Engineers Design Better Products
By leveraging field predictive ML models engineers can explore more options without the use of a solver when designing different components and parts, saving time and resources. This ultimately produces higher quality results that can then be used to make more informed decisions throughout the design process.