Artificial Intelligence

Design decisions in minutes – how AI supports product development

Artificial intelligence (AI) is a hot topic and increasingly important in product development. But how can this technology be effectively integrated into development projects? Together with our client Audi, we put it to the test and examined the potential and challenges of a machine learning (ML) application – a subset of AI – in a real project. For this purpose, we chose a crash management system (CMS). It is both simple enough to achieve a meaningful result and complicated enough to adequately test the general applicability of the ML method.

Expertise as the Key

ML can only be effectively utilized to the extent the underlying data foundation allows. Therefore, the expertise of the professionals involved plays a critical role. For example, design engineers enter their knowledge of manufacturing and spatial constraints, usable materials, and dependencies into the CAD model. Calculating engineers share their expertise on the simulation process, while data scientists assist with sampling and evaluation. The creation of thousands of design and simulation models required for ML is only achievable with appropriate automation across all process steps. A CATIA plug-in developed for this purpose enables all parameters and their dependencies as well as connection techniques to be integrated quickly and efficiently.

A specially developed CATIA plug-in enables the quick and efficient integration of all parameters, their dependencies, and connection techniques. Additionally, it embeds all simulation-relevant information (material, properties, solver, and more) directly into the CAD model. The fully automatic translation into a simulation file is done via tools such as ANSA or Hypermesh.

Automated process: Sampling, DoE, model creation, simulation, evaluation with subsequent training of the ML models. (© CONTACT Software]

Precise Linking of Parameters and Results

Our approach ensures that the relationship between the CAD model and the simulation model is fully preserved. The automated calculation and evaluation of the models based on specific results create an excellent data foundation for the ML process. The vectors of input parameters with corresponding result values form the basis for the ML approach—clear and comprehensive.

Input parameters (blue) identified based on constrained result vectors (red) that meet the requirements. (© CONTACT Software)

With the trained models and their known accuracy, parameter variations can be quickly tested, and the impact on behavior can be derived—literally within minutes. Once the optimal parameters are identified, they are automatically transferred to the CAD model and the design process can continue.

Conclusion

Our project demonstrated that ML is a valid method for design engineering. The combination of parametric CAD models, simulation, and machine learning provides an efficient approach to making design decisions quickly and accurately. The prerequisite for this is a robust database and the collaboration of the relevant experts on the model. The successful results from the Audi project demonstrate the potential of our data-based approach for product development.

Digital authenticity: how to spot AI-generated content

In today’s digital age, we often question whether we can trust images, videos, or texts. Tracing the source of information is becoming more and more difficult. Generative AI accelerates the creation of content at an incredible pace. Images and audio files that once required a skilled artist can now be generated by AI models in a matter of seconds. Models like OpenAI’s Sora can even produce high-quality videos!

This technology offers both opportunities and risks. On the one hand, it speeds up creative processes, but on the other hand, it can be misused for malicious purposes, such as phishing attacks or creating deceptively real deepfake videos. So how can we ensure that the content shared online is genuine?

Digital watermarks: invisible protection for content

Digital watermarks are one solution that helps verify the origin of images, videos, or audio files. These patterns are invisible to the human eye but can be detected by algorithms even after minor changes, like compressing or cropping an image, and are difficult to remove. They are primarily used to protect copyright.

However, applying watermarks to text is way more difficult because text has less redundancy than pixels in images. A related method is to insert small but visible errors into the original content. Google Maps, for instance, uses this method with fictional streets – if these streets appear in a copy, it signals copyright infringement.

Digital signatures: security through cryptography

Digital signatures are based on asymmetric cryptography. This means that the content is signed with a private key that only the creator possesses. Others can verify the authenticity of the content using a public key. Even the smallest alteration to the content invalidates the signature, making it nearly impossible to forge. Digital signatures already ensure transparency in online communication, for example through the HTTPS protocol for secure browsing.

In a world where all digital content would be protected by signatures, the origin and authenticity of any piece of media could be easily verified. For example, you could confirm who took a photo, when, and where. An initiative pushing this forward is the Coalition for Content Provenance and Authenticity (C2PA), which is developing technical standards to apply digital signatures to media and document its origin. Unlike watermarks, signatures are not permanently embedded into the content itself and can be removed without altering the material. In an ideal scenario, everyone would use digital signatures – then, missing signatures would raise doubts about the trustworthiness of the content.

GenAI detectors: AI vs. AI

GenAI detectors provide another way to recognize generated content. AI models are algorithms that leave behind certain patterns, such as specific wording or sentence structures. Other AI models can detect these. Tools like GPTZero can already identify with high accuracy whether a text originates from a generative AI model like ChatGPT or Gemini. While these detectors are not perfect yet, they provide an initial indication.

What does this mean for users?

Of all the options, digital signatures offer the strongest protection because they work across all types of content and are based on cryptographic methods. It will be interesting to see if projects like C2PA can establish trusted standards. Still, different measures may be needed depending on the purpose of ensuring the trustworthiness of digital content.
In addition to technological solutions, critical thinking remains one of the best tools for navigating the information age. The amount of available information is constantly growing; therefore, it is important to critically question, verify, and be aware of the capabilities of generative AI models.

For a more comprehensive article, check out the CONTACT Research Blog.

Digitalization for the High Seas

The sun is shining in Hamburg, and the mild autumn air is in motion – even though I am perfectly equipped for rainy weather. In early October, shipbuilders from around the world gather in a conference hotel near the harbor for the CADMATIC Digital Wave Forum. The user meeting invites participants to experience CADMATIC’s CAD application for shipbuilding firsthand and to learn about current trends, product innovations, and new developments. The highlight: CADMATIC Wave, an integrated CAD/PLM solution specifically designed for shipbuilding and jointly developed by CADMATIC and CONTACT.

Model visualization simplifies data retrieval and collaboration

After our first coffee, we slowly make our way into the conference hall. The morning is filled with numbers and facts around CADMATIC’s digitalization strategy. In the afternoon, our Managing Director Maximilian Zachries presents CADMATIC Wave to the 200 participants. As we demonstrate the first functionalities of the integrated Product Data Management (PDM), some attendees quickly pull out their phones to snap a photo of the feature. I am somewhat excited – now it’s official. Now we also need the data model. And that isn’t quite so simple.

Cadmatic's Atte Peltola introduces the audience to Cadmatic Wave

The resounding call for a data model for shipbuilding carries me through the three days in Hamburg. During my conversations with industry colleagues, it becomes evident that the information required and generated in the shipbuilding process must be able to be mapped within the model. Model-centric is the magic word: the ship’s geometry is visualized, including equipment, fittings, and logistics. Information can then be retrieved and added via the specific parts of the model. Model visualizations provide a shared and intuitive view of the ship for all involved trades, significantly simplifying information retrieval. This enhances the efficiency of engineering activities and collaboration, also with partners.

Basing a data model on ship geometry is challenging

Engaged in a discussion with a research associate from the Norwegian University of Science and Technology (NTNU), we stumble upon a question: Is the geometry model even suitable for generating a generic product structure for data storage in the PDM? After all, as a placeholder in a data model, there are quite a few locations in such a ship. And let me put it this way: data models are typically organized along the processes in product creation, not the geometry of a ship model. I am curious to see how we will solve this challenge in CADMATIC Wave.

The evening event takes place on the Cap San Diego, a museum ship in the Hamburg harbor. The rustic flair of a ship’s belly and the lavish buffet create a cozy atmosphere for lively conversations. We talk about life in Finland and Norway and the difference between information and data management. The evening ends stormy and rainy, and I finally put my rain gear to good use and return to the hotel dry and warm.

SEUS brings European shipbuilding to a new efficiency level

At the CADMATIC Digital Wave Forum, I also meet my consortium partners from the Smart European Shipbuilding (SEUS) project for the first time. Among them are representatives from NTNU and CADMATIC, as well as employees from two shipyards, the Norwegian Ulstein Group and the Spanish Astilleros Gondan SA. SEUS is an EU-funded research project with the goal of developing an integrated CAD and PLM solution for shipbuilding. This endeavor goes way beyond the functionalities we develop in CADMATIC Wave. For instance, we aim to incorporate knowledge management and utilize AI for searching within product data.

In this context, the broad positioning of our research department, CONTACT Research, works to our advantage. Our focus areas include not only Digital Lifecycle Management, where we conduct research on digitalization strategies for various industries, but also Artificial Intelligence. The AI product data search we aim to implement in SEUS allows us to bring our self-declared motto to life: “Bringing artificial intelligence into the engineering domains.”

As three days in Hamburg come to an end, three strong impressions remain:

It is necessary to design an abstract data model for shipbuilding. One that contains the modules of a ship and yet can be customized to fit the specific needs of any shipbuilder. This data model must be closely linked to the development process.
Personal exchange and meeting each other face to face have been an enriching experience for me in this new work area. This positive feeling motivates me for my future work in the SEUS project.
In Hamburg, rain gear is a must.