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An interview with AIMEN Technology Centre researchers on advancing additive manufacturing through data integration and standardization.

Contemporary European manufacturing enterprises face unprecedented computational and data management challenges in an increasingly complex global marketplace. The Pioneer Project, a significant EU Horizon initiative, addresses these critical manufacturing optimization challenges by developing an open innovation platform featuring an interoperable digital pipeline specifically designed for design-by-simulation optimization frameworks. In this interview, we speak with key researchers from AIMEN Technology Centre, PIONEER project coordinator, to understand how the project is revolutionizing the transition from design engineering to production, particularly in additive manufacturing applications.

The Vision: Connecting Two Different Worlds

Félix Vidal , Head of Smart Systems and Smart Manufacturing at AIMEN, explains the fundamental challenge that Pioneer addresses: “The goal is to help the transition from design engineering phase to the production. There are two very different domains, and Pioneer is trying to link them together, knowing the differences and special features from product design on one hand and from production on the other hand. We are using digital twins and Industry 4.0 methodologies, along with MODA/CHADA principles to bring in data from all domains and connect the different aspects of product development.” This challenge is particularly acute in additive manufacturing, where the complexity of transitioning from design to production can be overwhelming.
Pilar Rey Rodríguez, Senior Researcher at AIMEN, adds crucial context: “The core idea behind this project is to support the manufacturing of reliable products faster and at a lower cost by leveraging data from across the entire value chain. We’re creating datasets that integrate material, process, and monitoring information.”

Manufacturing First Time Right: The Ultimate Challenge

When discussing the challenges associated with both high and low volume production schemes, Félix identifies a critical success factor: “Manufacturing first time right.” This concept is particularly challenging in low-volume, one-of-a-kind production scenarios common in additive manufacturing.
Pilar provides insight from the materials perspective: “In low volume, and specifically in wire-arc additive manufacturing, the transition from designing something to being able to manufacture something is always tricky. It’s a complex process, and even when you think you’re going to fabricate something similar to what you fabricated months ago, it’s completely different because sometimes very slight differences in chemical composition or microstructure give you very different results.”
Lara Suárez Casabiell, AI Research Engineer at AIMEN, adds another dimension: “One of the biggest challenges is maintaining flexibility in the process without increasing complexity. Each product might require different simulations and data, and we need to ensure that all information has traceability and has been validated, which sometimes increases the complexity of the process.”

The Role of Simulation: From Prediction to Optimization

Simulation plays a crucial role in the Pioneer project, serving both predictive and optimization functions. Pilar explains their experience with Wire-Arc Additive Manufacturing (WAAM) simulations: “We have correlated quite well some results that we obtain from simulation software with data that we were able to extract from manufacturing and quality control. Thanks to tools that can represent 3D data from robot trajectories, cameras, and other peripherals, we can correlate these 3D representations with models extracted from simulation software.”
The simulation accuracy varies significantly based on material knowledge. As Félix notes: “For classical materials, getting the material properties needed for performing simulation models is easy. For other materials, this is an issue. Generally, you have two options: performing specific testing to get specific values, which is usually time-intensive and costly, or trying to look for alternatives from scientific publications.”

Data Interoperability and Standards: Building the Foundation for Integration

Data interoperability represents one of Pioneer’s most significant technical challenges. Félix outlines their approach: “We convert the design into different layer structures, then try to convert into a neutral format that is machine independent. This links trajectories with process parameters. Later, we convert the data into the specific machine format.”
Beyond technical interoperability, the project addresses semantic challenges. “We’re working on having a common dictionary and moving to ontologies, because different domains refer to different properties or aspects in different ways. If you have different partners working together, you need to build common understanding of the different information that you have to share.” Pilar emphasizes the software dimension: “In simulation, this kind of project is very multidisciplinary. You need a lot of inputs. If one partner uses one type of software and another uses a different type, it’s quite tricky to be aligned with a common objective. The more standardized and interoperable the data is, the better it is for research.”
The importance of standardization emerged as a central theme throughout the discussion. Pilar explains: “Standards ensure that you’re going to have a guide for something, ensuring that you’re going to do things with more quality and reliability.” She provides a practical example: “WAAM is the industrial name for directed energy deposition arc, or LMD is the industrial name for laser-based directed energy deposition. The standard names are not these names that a lot of people use. When you’re searching for these keywords, the result is going to be very different depending on your keywords, and standards can help in that way.”

VMAP Standard: Enabling Simulation and Data Connectivity

The discussion highlighted the specific role of the VMAP standard in addressing these challenges. Félix explains the connection: “At the end, what VMAP is trying to do is promote how to connect with different simulation models and also how to link information from different partners, from different software and from different tools in a common way. This is crucial, if you don’t have a common way, you cannot connect. You have to build your own tools to convert data into your specific data format, which means a lot of work for each tool or data that you need from external sources.”

The VMAP standard represents a significant step forward for both modelling and simulation standardization, as Félix notes: “For quality control, there are a lot of standards. If you work in inspection, you know for each inspection technique the standard you need to work with – ultrasound, radiography, magnetic testing. For modelling and simulation, there are initiatives like VMAP, and it is ongoing.”
From an AI perspective, Inés emphasizes: “Having standardized data is the key to start making anything in artificial intelligence and analytics in general. You need to compare things that have the same units and the same kind of information. You often lose a lot of time because the data is not standardized, and that’s always the first step in data analytics tasks.”

Industry Impact: Beyond Manufacturing Boundaries

The benefits of Pioneer’s approach extend across multiple industries. Félix sees broad applicability: “It’s quite generic, not specific to a sector or manufacturing process. I think especially those that have to work in low volume production will benefit mostly, because these are the sectors that have to think about product design in the engineering phase and how to move efficiently to production.”
Inés highlights benefits for researchers: “Researchers can use the tools developed in Pioneer, like, search for material data in different sources – papers, technical reports etc. The structured information converted to an international system of units for comparison can be quite useful for material science researchers.”

Looking Forward: The Path to Implementation

As Pioneer approaches its final phase, the team reflects on the complexity of bridging multiple domains. Félix concludes: “The project is quite complex. We’re trying to connect multiple domains – design, engineering, production, and data-driven approaches. All of us speak very different languages, and this is reflected in the complexity of this project.”
The Pioneer Project represents more than just a technological advancement; it’s a fundamental shift towards integrated, data-driven manufacturing that preserves domain expertise while enabling unprecedented collaboration. For the manufacturing industry, particularly in additive manufacturing, Pioneer’s approach offers a roadmap for navigating the increasing complexity of modern production while maintaining the quality and reliability that markets demand.
As Pilar aptly summarizes: “Sometimes it’s more important to standardize a methodology more than the data itself. The methodology is very important, once you document quite well how you’re doing things, it is often enough. Reliable and replicable data is the key to research.”

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The Pioneer Project continues to develop innovative solutions for manufacturing optimization, with results expected to benefit industries ranging from construction to automotive applications. For more information about the Pioneer Project and its outcomes, visit the project website or contact the VMAP SC.

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Transvalor carried out a case study with Faurecia Automotive Seating, who assembles lightweight components by laser welding for the manufacture of automotive seats.
This study illustrates the potential gains associated with the new VMAP exchange format, including the simplification of operations chaining between “process” simulation and “crash” simulation. In the end, the standard VMAP exchange format is intended to be more efficient because it is more compact and very well documented, with the ultimate guarantee of strong interoperability for a wide range of CAE software.

More details here.