Selective laser sintering (SLS) with polymers is currently at the transition stage for the production of functional components and holds great potential to revolutionize conventional production processes. Nevertheless, its application capability is confronted by newly imposed requirements regarding reliability and reproducibility. To safeguard these requirements, a deeper process understanding of material aging mechanisms in polymeric materials is needed. In order to enable the traceability of the materials as well as the identification of defective components with subsequent tracing of the cause, the use of a material marking process represents an alternative. SLS in combination with material marking is proving to be an efficient option for reproducible, high-quality manufacturing based on an increased understanding of the process. In this study, the idea of a marker-based traceability methodology for the purpose of process optimization is presented. Fundamental to the subsequent experimental investigation of the marking agent suitability, this work first focuses on the systematic selection of a suitable marking agent for use in SLS. Based on an analysis of the sinter material to be marked and a set of marking technologies, as well as using the selection methodology, the modified polymer marking technology was evaluated as the most suitable marking technology.
The growing demand for individualized products is becoming more and more significant and leads to a reduction in batch sizes. In particular, the production of multi-material components for lightweight design presents new challenges to the manufacturing process. This is evident when it comes to the production of individual parts, as today’s processes are characterized by high tool costs and manual operations. The described challenge can be overcome by a robot-based manufacturing cell allowing the use of a novel, modular process chain in which metal parts are mechanically pre-treated, subsequently completed by additive plastic application, and afterwards finalized in a machining step to achieve the required surface qualities and geometries. In order to realize the novel process chain, robot-based solutions for free-form metal sheet processing, increased interlayer bonding strength of plastic, and multi-material machining with integrated chip extraction have to be found. Therefore, this paper presents the first approach of a robot-guided surface structuring end-effector and a concept for a direct extraction hood, which is able to be adapted specifically to the movement of the robot and the part surface, so free-form surfaces can be machined. Based on this, first experimental studies for increasing the interlayer bonding strength of plastic were carried out using an extruder set up to applicate thermoplastics onto metal at high deposition rates. To define the positioning accuracy for a robot-guided structuring process, different point to point movements have been investigated.
This paper presents an algorithm that contributes to an automatic decomposition of a mechanical part based on geometric features and methods of unsupervised machine learning. For the development of the algorithm, existing techniques of 3D shape segmentation, especially surface-based part segmentation procedures are reviewed and important areas of activities are revealed. The developed multi-step approach results in an abstract product model. This representation leads to a new way of designing and redesigning parts for the novel hybrid manufacturing concept Incremental Manufacturing (IM).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.