System Simulation for humanoid robots – Turning complexity into competitive advantage with Simcenter Amesim

This blog introduces System Simulation for designing Humanoid Robots virtually with digital twins in Simcenter Amesim. It’s mechatronics with multiphysics to virtually size and assess the performances of Humanoid Robots with simulations executed in few seconds.

Simcenter closed-loop simulator in action

Excavator closed-loop simulator: From fuel to soil

Can you improve excavator productivity without increasing fuel consumption or mechanical failures? Anyone involved in excavator design knows the dilemma….

Simcenter PhysicsAI Geometric Deep Learning

Beyond the solver: How Geometric Deep Learning is reshaping CAE

Discover how Geometric Deep Learning powers Simcenter PhysicsAI to deliver fast, accurate CFD & FEA surrogate models – cutting simulation time from days to seconds.

Close-up of a CNC milling tool actively cutting a metal workpiece, with metal shavings and coolant fluid spraying outward during high-speed machining

The artisan’s ear in the digital age: Reimagining tool life with AI and Executable Digital Twins

From shop floor intuition to AI-powered foresight – discover how Simcenter Reduced Order Modeling uses Bayesian Neural Networks to predict CNC tool wear and remaining useful life.

What’s new in Simcenter 3D Rotor Dynamics 2606 – Postprocess modes at critical speeds and efficiency updates

Why do we need rotor dynamics analysis? Modern turbomachinery, from jet engines to industrial compressors, runs at high speeds and…

Illustration of executable digital twin for a wind turbine

Simcenter Executable Digital Twin: 2604 release updates

High-fidelity digital twins are transforming how products are designed and validated. But for many organizations, their true value stops at…

Simcenter Day BeNeLux in Eindhoven: from Benchmarking to Physics-AI

A day of real-world CAE stories, practical workflows, and a glimpse at what’s next for simulation Yesterday I joined a…

E-drivetrain measurements to insights

A 4-step approach to transform multiphysics e-drivetrain measurements into meaningful initial engineering insights

Electric drivetrain programs generate massive amounts of multiphysics data — yet many teams still struggle to turn it into trusted engineering decisions. This article presents a proven 4‑step approach to transform raw electrical, mechanical, and vibration measurements into validated, correlated, and repeatable insights, using a unified Simcenter Testlab workflow from measurement to decision.

Test data consolidation

Streamlining data: Efficient test data consolidation with Simcenter Testlab

As test engineers, we gather immense amounts of information from proving grounds and public roads alike. While acquiring this data is crucial, the real challenge often emerges afterward: data consolidation. Traditionally, this means manually sifting through hours of recordings, segmenting them, and wrestling with inconsistent naming conventions. This isn’t just tedious; it’s a significant bottleneck, consuming valuable engineering time and introducing errors that hinder analysis.
What if you could automate this entire process? Imagine long recordings intelligently segmented with GPS precision, and runs automatically named with clear, descriptive conventions, even incorporating dynamic suffixes for specific driving maneuvers. Read how Simcenter Testlab allows you to do test data consolidation efficiently.