Enabling functional 3D printed parts and products by development of innovative polymer additive manufacturing materials.
The goal of the Additive Manufacturing (AM) program is to accelerate innovation for our industrial partners and support their success in the market. We do this by developing and modifying polymer materials and printing processes to enable functional parts and products.
We improve and exploit the unique manufacturing freedom of AM which allows a high level of control of the material composition of a printed part. This creates the opportunity to make parts and products that have an integrated functionality which is hard to manufacture with existing production techniques.
Expertises, Capabilities and
We are strongly driven by industrial application challenges and our core competences are in polymer material expertise, additive manufacturing process know-how and computational materials modeling and simulations. In our labs we operate commercial 3D printers as well as 3D printers customized to our own specifications and design requirements. Being located at the Chemelot Brightlands Campus gives us access to a large field of materials science competences and facilities.
We have close connections with AMSYSTEMS Center and Holst Centre which are research centers for additive manufacturing equipment and flexible electronics respectively. We partner with Eindhoven University of Technology and University of Maastricht for our PhD and post-doc students who develop fundamental knowledge on additive manufacturing materials.
We focus on two research programs
Improvement of mechanical reinforcement of 3D printed parts is one of the current challenges in additive manufacturing to transition from prototypes to functional parts that meet similar requirements as for example injection molded parts do. A smartly designed part with embedded continuous fibers can improve mechanical strength where needed.
Self-sensing is the ability of a material to sense its own condition. The material itself is used as a sensor. Advantage is that you don’t need an implanted or attached sensor system. The costs are lower, the durability is higher, the sensing volume is bigger and the mechanical property loss is lower.
Polymer-matrix composites, containing continuous carbon fiber, are known materials that have self-sensing capabilities based on measurable changes in electrical resistance of the continuous fibers.
A self-sensing composite was used for damage detection in a cylinder made by filament winding, a standard continuous fiber composite manufacturing technique. The practical importance of such products can potentially be found in structural health monitoring in airplanes or critical parts of constructions like bridges.
The concept was proven by Brightlands Materials Center by monitoring deformation in a simple bending beam and in a scale model of a pedestrian composite bridge.
100% Limburg Bike Project
Damage detection by self-sensing in 3D printed bike frame lugs is part of the “100% Limburg Bike” project. In this project we collaborate with (amongst others) Eurocarbon, Cera- Carbon, Brightlands Chemelot Campus and Belgian Cycling Factory – known from racing bike brands like Ridley and Eddy Merkcx – and which is supported by the European Fund for Regional Development and the Province of Limburg in the OPZuid framework .
Self-sensing fiber reinforced thermoplastics can help gather important information
Self-sensing can also play a role in the design and prototype phase of new products or in replacing spare parts that are not available anymore.
3D printed self-sensing prototypes can help to gather information about the real use circumstances. During a testing period the self-sensing 3D printed part registers the real dynamics and forces that a product needs to withstand. This gives designers and engineers a clearer understanding of what requirements the 3D printed parts will have to meet.
As a diagnosis tool 3D printed self-sensing orthoses or protheses might guide patients and provide valuable information to doctors, regarding stress distribution and movement patterns.
 Journal of Intelligent Material Systems and Structures, vol17 ,57 ,2006.
 More info on 100% Limburg Bike Project.