Research and Industry

The department combines the main research areas of materials technology and plastics process technology. The focus is on a holistic approach. Both basic research-oriented and application-oriented tasks are considered. The research objectives are to elucidate the relationships between microstructures and properties of polymer materials, composite materials with polymer matrix and material composites, with particular emphasis on the effects of manufacturing processes.


Research areas and departments

Application Center UNIpace

UNIpace was founded in 2013 together with B. Braun Melsungen AG. Its mission is to conduct industry-related research and be a service provider for industry. The application center is primarily concerned with the processing of elastomers, in particular silicone rubber. Liquid silicone rubber (=LSR) is processed by injection molding, by multi-component injection molding in combination with various thermoplastics, and by the LAM process. Solid silicone rubber (HCR) is compounded with the aid of a mixer and usually processed by extrusion or compression molding. In addition to the processing machines, the following testing equipment is available: rebound elasticity, hardness, tensile strength, IR spectroscopy, capillary rheometer, RPA, climate chamber and FTIR.

Currently, UNIpace employs six scientific staff, three technical staff and 5 student assistants. The application center cooperates with more than 30 companies.

Image: Blafeld
Image: Blafeld
Image: Blafeld

Materials and structures

Cross-scale material development begins with the introduction of nanoparticles or molecular functional units and continues with the targeted setting of superstructures (crystalline phases or molecular orientations) and the macroscopic arrangement of fibers, layers or particle clusters. The aim of the research content is to understand cross-material mechanisms on the individual size scales and to experimentally investigate and model interactions as well as transferability of the different size scales. In particular, the anisotropic mechanical behavior, interfacial properties and damage mechanisms play an important role.

Fundamental insights into scale effects will be gained by using high-resolution imaging characterization methods (e.g. ┬ÁCT), which will also be used to identify structure- and load-dependent material and fatigue behavior in an in-situ application. Occurring cross-scale interactions would be calculated using phenomenological and physical models.

The cross-material application range of the planned methods is also reflected in the department's projects, which include the use of glass-fiber-reinforced engineering thermoplastics in hybrid combination with light metals, foamed thermoplastic structures, intrinsically reinforced plastics, and biogenic and functionalized materials.

Image: ifw
Image: ifw
Image: ifw

In the "Bioplastics" department, biocomposites are produced and characterized from biobased plastics and natural raw materials such as starch, wood or cellulose. Processing parameters play a decisive role in the production of biobased composites. Components made from renewable raw materials are usually more sensitive than materials made from petroleum or glass. During characterization, mechanical, thermal and chemical properties of the biocomposites are investigated. Special attention is paid to the aging or long-term durability of bio-based composites. Within the framework of the "BeBio2" research network, a wide variety of bioplastics and biocomposites for industries in the medical, electrical and automotive sectors as well as the consumer sector are being tested for their durability. We are constantly working in close contact with numerous companies to develop the biocomposites of the future.

The department of "Bioplastics" is assigned to the research focus "Materials and Structures".

Image: ifw
Image: ifw
Image: ifw

Functions and digitization

In the Function-Integrating Manufacturing department, the functionalization of plastics is the thematic focus. Functionalization can be carried out via in situ additives, surface modifications or with the use of multilayer systems in order to make additional physical, chemical or biological properties usable. Application examples can be found in actuators, sensors, electroluminescence, photovoltaics or lab-on-chip technology. The additional functions are introduced via thermally or electrically conductive additives such as carbon fibers, CNT (carbon nanotubes) or graphite. Quantum dots can be used to generate electrical energy, and electrochromic layer systems can be used to darken transparent panes.

The aim of function-integrating manufacturing is to adapt and further develop common plastics processing methods to the new material systems. Processing effects can be both optimizing (e.g. filler arrangement and orientation) and limiting (e.g. thermal degradation, shear effects). This knowledge is elementary in order to make the innovative applications suitable for series production on a large scale. For this purpose, the application center UNIfipp (function integrating polymer processing) was founded in 2020. UNIfipp is intended to serve as a platform for cooperation with partners from industry and science in the key areas of additive manufacturing, production of layer systems, compounding and blending.

Simulation methods such as CFD (Computational Fluid Dynamics) and structural simulations have been established in plastics technology for many years. For example, hardly any injection molds are produced without prior simulation with Moldflow, Moldex or comparable software products. The calculation of structural properties is also well established in practical applications.

Nevertheless, there are always open questions for e.g. special simulation tasks, such as for foamed components or for the anisotropic behavior of fiber-filled plastics. These issues are addressed in the Simulation and Machine Learning department.

In addition, artificial intelligence methods have great potential for modeling plastics processing and materials. In particular, when models are required that are to integrate very different data, such as process data histories, high-resolution structural data, image data and also empirical knowledge, machine learning methods offer some highly interesting approaches. We are trying to transfer these to cusntom technology. Areas of application are start-up processes for machines, process monitoring or automated detection of process instabilities. Thereby, methods of supervised learning as well as unsupervised learning methods and e.g. transfer learning are used.

Image: ifw
Image: ifw
Image: ifw