Sustainably and Digitally Driven Hierarchical Laser Texturing for Complex Surfaces (SYNTECS)
Generation of Surface Functionalities Requires Multistage Machining Processes
Surface treatments are an important method for optimizing properties such as roughness, wettability, scratch resistance and microbial resistance in the manufacturing industry. Recently, with the increasing demand for more complex, customized, and high-performance components, there has been a growing need for multifunctional surfaces. Examples include surfaces that are both antimicrobial and scratch resistant in the aerospace and automotive sectors, surfaces that are low friction and wear resistant in the injection molding industry, and surfaces that are highly porous and antimicrobial in the medical sector. To achieve such multifunctional surfaces, both surface chemistry and surface topography are usually changed, requiring multiple surface treatment steps, such as chemical etching followed by chemical coating, sandblasting followed by chemical treatment, or soft lithography followed by electroplating. For example, six separate surface treatment steps are currently used to achieve the antimicrobial and high-porosity surface specifications required for a market-leading hip implant system. These chemical, mechanical and thermal surface treatment steps contribute significantly to the resource consumption and energy intensity of the overall manufacturing process, using consumable materials such as aluminum oxide and titanium powder.
Multifunctional Surfaces by Laser
Laser-based surface texturing (LST) is a sustainable, flexible and scalable approach to producing multifunctional surfaces. LST techniques such as Direct Laser Writing (DLW), Laser Induced Periodic Surface Structuring (LIPSS) and Direct Laser Interference Patterning (DLIP) can be used synergistically to create complex hierarchical surfaces.
Advantages:
- Sustainability: laser-based surface texturing is chemical- and waste-free and requires no consumables (other than electricity). Compared to coated components, lasertextured components are easier to recycle at the end of their life cycle because the bonded materials do not need to be separated.
- Flexibility: Laser-based surface texturing offers multiple options for digital parametering (pulse duration, laser power, pulse repetition rate, single pulse/burst mode method, etc.) to flexibly respond to customer-specific adaptations (shape, material, surface functionality, production line).
- Scalability: Laser-based surface texturing is suitable both for direct texturing of individual components and for indirect texture transfer (transfer of the texture from replica templates, e.g. by injection molding onto molded components). This allows functional surfaces to be produced with high throughput.
Project Objective:
To industrialize hierarchical LST, the high capital investment required to perform multiple LST steps (which currently require different machine platforms) must be reduced. SYNTECS will overcome this obstacle and address the need for "more efficient and flexible manufacturing processes that contribute to competitiveness and the transition to environmentally friendly and sustainable production processes." The overall goal of SYNTECS is to develop and demonstrate a versatile, low-cost laser texturing concept for creating hierarchical surfaces with features in the submicrometer to 200 μm range and with multifunctional performance characteristics.
The Competencies of Fraunhofer IWS
- Design for surface engineering: creation of a predictive AI model that defines the required LST process parameters to achieve the desired texture and (multi-)functionality.
- Development of an intelligent process monitoring and control system: development of an inline control of the working distance for a 5-axis laser texturing system using the airborne acoustic emission of the laser-material interaction. For this purpose, a stand-alone device with microphones and the necessary computing power for the pre-processing of audio streams will be built. This acoustic module will also be trained to control surface texture using machine learning.
- Construction of a compact process monitoring demonstrator: Construction of an integrative demonstrator that analyzes not only acoustic process emissions but also the optical diffraction properties of the structured surfaces. An API will also be developed for integration into the overall system.