The research described in this paper is dedicated to the use of continuous fibers as reinforcement for additive manufacturing, particularly using Shotcrete. Composites and in particular fiber reinforced polymers (FRP) are increasingly present in concrete reinforcement. Their corrosion resistance, high tensile strength, low weight, and high flexibility offer an interesting alternative to conventional steel reinforcement, especially with respect to their use in Concrete 3D Printing. This paper presents an initial development of a dynamic robot-based manufacturing process for FRP concrete reinforcement as an innovative way to increase shape freedom and efficiency in concrete construction. The focus here is on prefabricated fiber reinforcement, which is concreted in a subsequent additive process to produce load-bearing components. After the presentation of the fabrication concept for the integration of FRP reinforcement and the state of the art, a requirements analysis regarding the mechanical bonding behavior in concrete is carried out. This is followed by a description of the development of a dynamic fiber winding process and its integration into an automated production system for individualized fiber reinforcement. Next, initial tests for the automated application of concrete by means of Shotcrete 3D Printing are carried out. In addition, an outlook describes further technical development steps and provides an outline of advanced manufacturing concepts for additive concrete manufacturing with integrated fiber reinforcement.

The reduction of material defects in the automated fiber placement process is one of the significant factors for manufacturing large and complex components more efficiently in the future. However, the monitoring of complex manufacturing processes usually requires complex sensor and computer systems that are often quite sensitive to disturbances and errors. New techniques such as image segmentation with neural networks provide a new approach to this problem and have the potential to solve complex processes faster and more robustly. In this study, a system is presented that performs monitoring, inspection and measurement tasks simultaneously in automated fiber placement processes. The system is based on the SiamMask network which is used for the automatic image processing. The artificial neural network is trained to recognize individual carbon fiber tapes and segment them for additional analysis. For the creation of the testing- and training data, an analytical approach is presented. The post-processing of the object segmentation, which is the primary output of the SiamMask network and the identification of individual tapes, provides accurate measurements which are demonstrated by an example. We show that image segmentation with modern approaches like SiamMask offers great potential to handle highly complex engineering tasks in a faster and more intelligent manner in comparison to conventional methods.

Automated Fiber Placement (AFP) has been used for more than 30 years as a manufacturing process for large components such as aircraft fuselage or nose made of composite materials. As the literature research showed, 85% of efficient AFP head process time is used for the cutting process, accelerating, decelerating and necessary directional changes of the AFP head. The AFP head consists of various modules that synchronize the complete process. In recent years, scientists tried to extend the advantages of this technique and optimize the process. The aim of this article is to show the integration of the cutting unit and the feeding unit in a compact and simple system. Therefore the cutting edge of the tape and the productivity can be increased significantly. The feeding system pulls the tape from the material spool to under the compaction roller. Obviously, the tape should also cut to a predefined length. According to the state of the art, the cutting unit works when the feeding unit is in the standing position. The layup process has therefore a short break, which is no longer required with the new approach. The cutting process is realized without interruption, therefore the lost time during cutting can be saved. The integrated unit is synchronized with the AFP process to apply the active or passive feeding process.

Automation technologies such as Automated Fiber Placement (AFP) or Automated Tape Laying (ATL) are widely used in the aerospace industry today. However, these processes can still be further improved for higher productivity. Fiber-reinforced plastics allow the production of components with extremely high specific strength and stiffness. Regarding the automated manufacturing processes, the thermoplastic tape placement offers efficiency improvements compared to the nowadays more commonly used thermoset tape placement, especially through the substitution of the expensive and time-consuming autoclave process. The consolidation of thermoplastic Prepregs is achieved with an elastic or rigid roller according to the current state of the art. The Prepregs must be consolidated precisely on the substrate or on top of each other. The most important process parameters for high-quality laminate structure with low porosity are the control of heat source, consolidation force, consolidation roll speed, and tape tension. The efficiency of the AFP process can generally be improved by increasing the speed of the consolidation roller. By increasing the speed of the consolidation roller, porosity is increased and mechanical properties of the laminate are reduced significantly due to the short contact time between consolidation roller and Prepregs. This study investigates a process that can reduce these challenges by increasing the contact time and force duration of the consolidation roller on the Prepregs. The consolidation roller in this study is additionally to be driven by the harmonic oscillations. The new method allows the consolidation roller to oscillate forward and backward during the fiber placement process. This creates another force vector in addition to the compressive force of the consolidation roller and increases the bonding strength between the layers.