Additive manufacturing processes are still on the way from a prototyping process to an established serial production process. Often the limiting factors are the variety and costs of materials compared with standard materials. Polypropylene is a commonly used material in series production. The processing of polypropylene in additive-and extrusion-based manufacturing processes is still a challenge. A big issue in pellet-based large scale additive manufacturing of polypropylene is to achieve dimensional accuracy of the printed parts. Due to the semi-crystalline nature of polypropylene and its low adhesive force on standard printing surfaces (aluminum) the fabricated parts tend to warp and deviate from the geometric shape. In this paper the influence of a melt adhesive and different filler types (glass fibers, glass bubbles, and talcum) on the warpage behavior of 3D printed polypropylene parts is investigated. Another issue in additive manufacturing is the mechanical properties of the fabricated parts compared to injection molded parts. The mechanical properties of 3D printed parts often show a dependency on the print direction, especially in case of using fillers with an anisotropic shape (fibers). In this study the influence of different fillers on mechanical properties and process indicated orientation is analyzed.
Ausgehend vom Faserverbundfertigungsprozess „Pultrusion“ wurde eine Softwarearchitektur zur Digitalisierung von meist manuellen Fertigungsschritten entwickelt. Um persönliches Wissen für jeden Anwender zugänglich zu machen, die Rüst- und Inbetriebnahmezeiten zu beschleunigen beziehungs- weise strukturierte Daten und Informationen der Fertigung zu gewinnen, wurde eine ganzheitliche digitale Toolchain für die Planung, Einrichtung und den Betrieb von Bestandsanlagen aufgebaut. Based on the fiber composite manufacturing process „pultrusion“, a software architecture for the digitalization of mostly manual manufacturing steps was developed. A holistic digital toolchain for the planning, set-up and operation of existing lines was devised to make personal knowledge accessible to every user and accelerate set-up or commissioning times, as well as to gain structured data and information from the manufacturing system.
To ease teaching self-organizing systems design, we implemented the AntNet routing algorithm for real-world application using educational robots called ActivityBot. Using line sensors and ultrasonic distance sensors, the robotic ants traverse a tiled graph printed on paper, collectively converging to the shortest path. In our descriptions, we address the challenges to face when employing such self-organizing systems on educational hardware and provide a video on YouTube https://youtu.be/JFduHJ0o0UM.
Setting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be done to further improve, visualize or permanently store the data in a digitized company.
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