Category: CAD

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.

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

CADMATIC’s Atte Peltola presents CADMATIC Wave. (© CADMATIC)

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:

  1. 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.
  2. 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.
  3. In Hamburg, rain gear is a must.

How intuitive CAE apps accelerate product development

Today, companies face multiple challenges in launching increasingly complex products to the market faster. In particular, the lack of specialized knowledge available from simulation experts in the field of computer-aided engineering (CAE) often slows down product development. Easy-to-use CAE applications can remedy this situation and significantly improve the way products are developed and optimized.

Isolated expertise as a bottleneck in product development

However, daily practice reveals that answering supposedly simple questions, such as the effects of a material change on the deformation behavior of a component or the functional consequences of minor, production-related changes to the component geometry, via simulation, still demands significant organizational effort.

Complex issues require the exchange of numerous pieces of information between the involved process partners. Examples of this include providing current CAD statuses on the part of the design department or feeding back existing test results into the simulation. In addition, relevant decision deadlines and available simulation capacities must be considered. The execution and evaluation of the simulation usually demands specialized expertise, often isolated in expert groups and only available to a limited extent.

Providing access to expert knowledge throughout the organization

Therefore, the goal should be to break down barriers to using simulation technologies, making them accessible to a broad user group – regardless of their technical expertise. The way to achieve this can be termed as “technical democratization of simulation”. It involves integrating existing technical know-how into intuitive CAE applications and making them available to all users company-wide through a CAE business layer.

Three steps to the CAE business layer:

  1. Analysis
    The initial step involves a thorough inventory of the existing CAE processes within the company. This helps to identify the most important processes based on their relevance to the application and to decide which ones are suitable for the development of a CAE application according to the cost-benefit principle.
  2. Standardization
    The next step is the standardization of the identified CAE processes which needs the expertise of the calculation engineers. The requirements for the input factors of the CAE process, such as necessary parameters and data, as well as the desired output from the CAE process, are clearly defined. Since simulation processes are typically a complex interplay of different software tools, particular attention is paid to error handling in case potential issues arise during the ongoing process.
  3. Automation
    Subsequently, the CAE application is developed and implemented in the company. Deployment on a software platform available throughout the company, which also hosts the data required and generated for the process, ensures comprehensive traceability

Successively, a CAE business layer is created which unites the CAE applications.

CAE apps dashboard in CONTACT Elements (© CONTACT Software)

Concerns and opportunities

Broad access to simulation technologies does not mean everyone becomes an expert but users are guided through complex processes. An integrated error-handling system reacts to incorrect inputs or deviations in the expected data. Experience shows that expert skills and simulation expertise are not devalued. On the contrary, experienced engineers with a wealth of practical experience and methodological know-how remain indispensable. Through general usage, they can focus on more challenging tasks, accompany decision-making processes, or concentrate on the advancement of simulation methods.

Conclusion: User-friendly CAE applications combine efficiency and innovation

The company-wide provision of user-friendly CAE applications marks an opportunity to establish simulation methods even earlier and more consistently in product development. More users are involved in the process, utilization of resources improves, innovations can be advanced more efficiently and enhanced products will be brought to market in less time. At the same time, it allows simulation experts to focus on more demanding tasks.