Siemens Digital Industries Software Blogs

Explore how to use Process Simulate Copilot to streamline the creation of a pass template, a structured list of standard robot actions used to configure trajectories. Designed to help simplify complex robotic simulations, Process Simulate Copilot empowers engineers by accelerating decision-making, enhancing simulation workflows, all while distilling simulation data into concise study information summaries. Whether it’s optimizing robot paths or solving collision issues faster than ever before, the Copilot provides intelligent guidance, unlocking new levels of efficiency in digital manufacturing.

Explore how an end-to-end solution like NX for Manufacturing helps power an additive manufacturing machine shop, Big Metal Additive (BMA). Explore the importance of hybrid manufacturing and learn how BMA exemplifies the shift to hybrid manufacturing, specializing in metal 3D printing and integrating welders with 5-axis CNC mills to explore new frontiers in manufacturing. 

Physical testing alone can’t keep up with today’s certification demands. This blog explores how linking digital simulations with real-world testing helps aerospace companies validate earlier, reduce risk and accelerate certification with greater efficiency and confidence.

Wow, so cool that we had a chance to sit down and talk with Bala Kondur of Siemens. One of...

On the Industry Forward Podcast, Dale and I spoke with Hendrik Lange, Director of the Heavy Equipment Industry at Siemens...

AI has become a ubiquitous force in the engineering world. In this blog, we explore how Simcenter Engineering Services is using AI to deliver faster solutions to common CFD challenges.

Siemens Process Preparation optimizes test programming efficiency for Engent Inc., transforming workflows and enhancing overall productivity with seamless CAD import, BOM integration, and automated probe placement.

Explore the potential of large language models (LLMs) generating SimTalk code, the scripting language in Siemens Plant Simulation software. Learn how Retrieval-Augmented Generation (RAG) is a powerful technique that significantly enhances the performance of LLMs by dynamically integrating external knowledge sources into the generation process. This integration allows models to access up-to-date, domain-specific information that may not be captured in their pre-training, making them much more versatile and reliable in responding to complex queries. Learn how Siemens extensive SimTalk documentation serves as a powerful foundation for contextual code generation.

When Metal Design started looking for ways to handle their growing production demands, they found an unexpected solution that changed everything.