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.
Engineering organizations, from automotive, electronics or aerospace, are consistently exploring Artificial Intelligence (AI) and Machine Learning (ML), to enable them to innovate faster, streamline processes, and drive substantial cost savings. By bringing an outsider’s perspective free of human biases, AI/ML is viewed as a valuable “member” of their innovation team, reshaping the way engineers ideate, create, and bring to life competitive products with reduced use of resources, saving cost and time, and being more sustainable. From automated design processes to predictive analytics and collaborative tools, let’s discuss how Noesis Solutions is not only accelerating the pace of innovation but also redefining the very essence of engineering designs in the modern age, with the power of AI.
Digital Technology in Engineering
Digital engineering plays an increasingly important role in engineering design. Broadly used 3D modeling and the fast adoption of simulation software has encouraged companies to invest in making these computationally and data-intensive solutions more efficient. With increasingly powerful computers, engineering teams are now enabled to develop and optimize their increasingly complex models, that can replicate and predict a product’s characteristics and expected behaviors.
Optimization and automation of engineering processes enable the engineers to run hundreds or thousands of simulations, tweaking designs to find optimal solutions, often surpassing the capabilities of human designers. However, as the complexity of the products and simulations increase, these traditional simulation methods face limitations of time. In a world where competitive advantage depends upon both speed and innovation, engineering companies are beginning to think beyond traditional methods, and embrace new technologies and innovative approaches to stay ahead.
Value of Time
In a previous blog post, by my colleague and the Chief Product Officer of Noesis Solutions, Georgios Papantonakis, titled Balancing Speed and Accuracy with the power of AI, he discusses the increasing importance of speed over accuracy while engineers develop products, and that they are increasingly adapting to new technologies for this purpose. This is why we started exploring new technologies such as AI/ML as an alternative or a catalyst to enable our partners to reduce time-to-market and increase effectiveness of product development, with the possibility of extending its application to larger and more complex engineering problems.
nvision, our AI-powered surrogate modeling tool, balances accuracy and computational efficiency to approximate high-fidelity models closely while managing approximation errors. These models can perform evaluations much faster and with significantly fewer resources than real simulations. nvision enables real-time decision-making by allowing engineers to quickly evaluate multiple design alternatives and potential outcomes. While not always as accurate as traditional time-consuming simulation, nvision’s AI-powered models provide valuable insights and clarity, enhancing understanding and exploration within your design space. This results in a competitive advantage for organizations by reducing time to market and reducing costs.
Quality Data Drives Superior Models
To train a surrogate modelling, engineers start with a good and enriched database, as it is crucial for a good and reliable surrogate model. For this process, we start with Optimus, our automation and optimization tool that enables engineers and the design teams to create trustworthy data, where they define the constraints and desired performance characteristics of the product. Besides the quality of this data, the number of required experiments plays a central role in unleashing the full potential of AI technology. Optimus can adaptively scan the design space with a minimum of simulation runs needed, helping them to train good quality models with minimum cost.
To train a surrogate modelling, engineers start with a regular digital design optimization approach, they define the constraints and desired performance characteristics of the product, and the computer runs a few conventional simulations on different design options. For this process, we rely on Optimus, our automation and optimization tool that enables engineers and the design teams to create trustworthy data to train their model with, as we know that a good and an enriched database is crucial for a good and a reliable surrogate model. Besides the quality of this data, the number of required experiments to train a model plays a central role in unleashing the full potential of AI technology. Optimus can adaptively scan the design space with a minimum of simulation runs needed. This helps to train good quality models with minimum cost.
The data from these initial simulations are used to train a surrogate model, which is set up to take the same inputs and attempts to evaluate the outputs of the simulation system. When training is complete, this AI model will work just like the conventional simulation, but much, much faster. This speed increase is a game changer, enabling the engineering teams to explore much more of the design space than with conventional simulations. An enriched database enhances the model's accuracy and robustness. By incorporating a diverse set of historical data and real-world performance metrics, the surrogate model becomes even more reliable and versatile.
Insights from the past
AI-based surrogate models can be trained with new or historical data to gain insights into previous simulations, and to design the next generation of products in a faster way. These models can be used in a template-based manner: Engineers and design teams build AI-powered libraries of pre-built models and templates, that streamline the product development process by offering ready-made solutions for common design challenges.
These libraries enable fast engineering and problem-solving, significantly reducing the time and effort required to develop new designs from scratch. By providing standardized, tested solutions, they help maintain consistency and quality across projects, allowing engineers to focus on innovation and optimization rather than reinventing the wheel for every new project.
Replacing time consuming simulations and MDAO workflow branches
Another advantage is to replace time consuming simulations with AI based models once a certain accuracy of the model is reached. But it doesn’t stop there. Besides AI in Engineering, companies focus on gaining a holistic view on their product design by implementing MDAO (Multidisciplinary Design Analysis and Optimization) workflows. Imagine having such a workflow, for example combining structural analysis with CFD (Computational fluid dynamics) simulation. Once you have executed some simulations, you can gradually train an AI model on the CFD workflow branch. Once it has reached a certain quality, you can replace the often time consuming CFD simulations with predictions from your model – directly in your Optimus workflow.
Accessibility to everyone, not just experts
AI helps to democratize access to engineering knowledge and simulation tools, empowering not only experienced engineers but also designers, managers, and professionals from other fields to actively engage in the design process. This enhances the pool of innovation, fosters collaboration, and accelerates the development of leading-edge products. With nvision, we envision a future for engineering design that is more inclusive, efficient, and capable of addressing increasingly complex challenges.
At Noesis Solutions, we are consistently evolving our product and service portfolio to adapt to the latest market and customer needs, and how our customers can leverage the use of AI/ML to gain competitive advantage. For more information, review the latest announcement, webinars, case studies and stay up to date on the latest products and case studies at Noesis Solutions. Watch a free demonstration of the product here and to discover how nvision can accelerate innovation at your organization, request a free non-obligatory consultation here.
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