The Working Wonders blog

Revolutionizing Engineering Design with AI

Author: Marco Panzeri, R&D Manager, Noesis Solutions

The world of engineering designs and product development has been evolving at a rapid pace. Simulations and 3D models have been a cornerstone of engineering design, allowing for the virtual replication of real-world systems for decades now. Engineers of today, have spent years immersed in the world of simulations, stress analyses, and complex product development processes. The thing is, while digital engineering has made incredible progress over the years, there's always been one persistent challenge: time. Simulations are critical, yes, but they are also expensive in terms of resources and, more crucially, time. This is where AI has come into play.

Drive Innovation with AI

On the hunt for innovation, AI and ML stand at the forefront to reshape the way how products are designed virtually. Looking at the 2023 Gartner Hype Cycle™, AI in engineering is located in the innovation trigger phase, with increasingly high expectations on revolutionizing virtual product design. Today, AI in Engineering integrates into different stages of the design cycles with a multitude of applications. Companies all around the globe implement this technology to improve speed, quality and cost efficiency of their product design.

Balancing Speed and Accuracy with the power of AI

Engineers use simulations to understand, analyze and predict how their complex systems behave in the real world, and improves the quality and accuracy of their designs, from the earliest stages of design.

The idea behind id8

Having the customers at the centre of innovation has driven successful business and product roadmaps at Noesis Solutions for many years. In the last two decades of pioneering innovation, we have an in-depth understanding of the integration and automation of engineering workflows and its role to obtain conceptual knowledge in the early stages of the design process. But this understanding also led us to grasp the fact that evolving alongside industry trends is no longer a choice to thrive in the ever-changing landscape of digital engineering.

Fast Tracking Next Generation Product Development

With virtually limitless computational resources, organizations can execute complex simulation analyses at an unparalleled speed, enabling rapid iterations and significantly reducing time-to-insight - an interaction with David Franke, CEO, Noesis Solutions.

Exploiting engineering data to build decision metrics (2/2)

In our previous blog post, we’ve discussed Cluster Analysis in detail. Cluster Analysis (or clustering) is a so-called unsupervised machine learning approach available to Optimus users to help them structure their engineering data sets. In this post, we’re digging deeper into another unsupervised machine learning technique available in Optimus, the Self-Organizing Maps (SOM).

Exploiting engineering data to build decision metrics

Engineers regularly get buried under massive amounts of data generated through simulation and physical testing. As a result they spend a lot of time and effort to access and identify the data that matter most – assuming they have sufficient time to exploit all the data and turn these into decision metrics.