A Concept-to-Digital-Twin Product Development Demonstration

The benefits of launching a smart product line are unquestioned – recurring revenue, data-driven product decisions, post-launch feature updates, new user experiences, and many more –

Presentation by Ichiro Shibata, Director of Analytics Solutions at Altair.

New design concept for predictive maintenance using IoT, CAE and AI. Through the development process of an apparatus demonstration, you will learn how to utilize topology optimization and 3D-printing to improve the accuracy of deep learning inference. The combination of deductive approach by CAE and inductive approach by AI makes it possible to see target areas that have been impossible so far. You will also learn how Raspeberry Pi device can be implemented as a deep learning technology for real-time machine condition monitoring.

Presentation by Alvaro Everlet, VP Business Development at Altair. Learn how Altair's IoT stack and architecture contributes to your value chain, IoT enables Smart Things and Processes in support of Simulation Driven Innovation. See real use cases examples and hands on demonstration.

Presentation by Rocco Cappiello, IOT Business Developer at Altair.

Altair Engineering Analytics empowers Engineers and Executives to make better decisions faster by managing reporting and analytics on the main engineering processes in one openly architected and highly configurable tool to visualize, analyse and report at any point in time during the entire product lifecycle.

The Digital Twin and IoT seminar features the key ideas behind the concept of the Digital Twin, predictive maintenance, and its practical relevance especially for product engineering and simulation.

Presentations recorded at the Global ATC in Paris, France on October 16, 2018.

Presentation by Alvaro Everlet, VP Business Development at Altair.

Digital Twins of test equipment help align physical simulation (lab testing) and virtual simulation (CAE). While typical CAE models make assumptions about the physical simulation initial conditions and the operational environment during testing, a Digital Twin accurately reproduces test activity and can predict future states to determine fixture or product failures. The twin will also ensure test set up is repeatable and robust through an improved understanding of system sensitivities.

Presentation by Vincent Brunetta, Chief Digital Officer at Socomec Holding.

In this presentation you will learn how Socomec has leveraged Altair’s full IoT stack and architecture to build an end to end IoT solution. You will also learn via use case how a broad eco-system of partners with complementary offering was integrated to deliver innovative IoT powered solution. We will take you through the challenges and workaround that had to be done to offer seamless solution.

Presentation by Oliver Breuer, Head of Software Engineering at FLUIDON.

There is a life for simulation models beyond the development process. Despite the large amount of data that can be collected from industrial facilities, the real benefit is behind colorful graphics and charts. To get there, the data provided by the connected components of an IoT capable system has to be analysed and put into context. So, the question is not what can be done with all the collected data but how to generate useful information. Using a simulation model in automated analysis allows estimating the condition of a subsystem or component, even though it is not directly monitored by dedicated sensors. Models created with DSHplus by FLUIDON (APA) can be exported to run as processing modules within IoT Platforms such as SmartWorks by Altair. They can be conditioned with the collected data and put it into the context of system behavior in order to make concrete statements about the current state of the system and its components. These digital twins turn the previous service life of simulation models into a closed cycle that includes product development, commissioning and operation and starts again when revisions and new developments are made on the basis of the existing system.