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PublicationAssessing the Environmental and Economic Impact of Wire-Arc Additive Manufacturing( 2024-05)
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PublicationAssessing the Environmental and Economic Impact of Wire Arc Additive Manufacturing( 2024-04-25)
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PublicationMachinability study in orthogonal cutting of additively manufactured Inconel 718 with specifically induced porosity( 2024-02-01)In comparison to conventional manufacturing technologies, additive manufacturing (AM) offers great design freedom, the integration of functions into components, new lightweight design concepts and high material efficiency. In aerospace and turbomachinery, this technology is increasingly coming into focus, especially the laser-based powder bed fusion of metals (PBF-LB/M) process. PBF-LB/M is already used for some aerospace components, which are often exposed to high thermal and mechanical loads. Dependent on the component geometry, support structures are required for AM, which then usually have to be removed by machining. One suitable support structure is the use of material with specifically induced porosity. This ensures good heat dissipation and thus homogeneous component properties, high retention forces and short process times in PBF-LB/M. However, the machinability of porous, additively manufactured material has hardly been researched so far. One preliminary investigation of milling porous, additively manufactured Inconel 718, though, showed significantly poorer machinability of the porous material compared to the dense material. To further examine this phenomenon, this paper presents the results of fundamental machinability studies with porous, additively manufactured Inconel 718 in orthogonal cutting. The investigations with tungsten carbide cutting tools on a special fundamental test rig include the analysis of the cutting force, the chip geometry, the chip temperature and the surface quality. The research results provide explanations for the poorer machinability of the porous material and derived approaches for improving the machinability in future studies.
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PublicationAdditively Manufactured Robot Gripper Blades for Automated Cell Production Processes( 2022-10-14)The automation of cell production processes demands strict requirements with regard to sterility, reliability, and flexibility. Robots work in such environments as transporting devices for a huge variety of disposables, e.g., cell plates, tubes, cassettes, and other objects. Therefore, the blades of their grippers must be designed to hold all of these different materials in a stable, gentle manner, and in defined positions, which means that the blades require complex geometries. Furthermore, they should have as few edges as possible, so as to be easy to clean. In this report, we demonstrate how these requirements can be met by producing stainless steel robot grippers by additive manufacturing.
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PublicationConcept for the reduction of non-value-adding operations in Laser Powder Bed Fusion (L-PBF)( 2022-05-26)In additive manufacturing with metallic base material, Laser Powder Bed Fusion (L-PBF) is an industrially established technology for tool-free production of complex and individualized components and products by applying material layer by layer. In addition to in-processing, the L-PBF process chain consists of several other process steps in pre- and post-processing. A holistic view of the additive manufacturing process chain shows that many process steps currently involve non-value-adding (NVA) operations and that there is generally great potential for reducing throughput times and costs. Due to the great importance of efficiency in industrial manufacturing process chains, this paper proposes a concept for analyzing the L-PBF process chain and deriving measures to eliminate NVA operations and increase the efficiency of value-adding (VA) operations. First, this paper presents a methodology for identifying VA and NVA operations that encompasses the entire chain. After the process mapping, the process steps are divided into sub-tasks to subsequently analyze them with regard to the level of automation. This analysis is the basis for selecting the appropriate automation strategy and optimizing the individual process steps by applying automation-aligned lean production methods to increase the proportion of VA operations. The potential for a significant increase in the productivity of the additive manufacturing process chain is demonstrated through systematic analysis and requirements-based optimization of the level of automation. The approach is structured in an adaptive manner so that, beyond the possibilities of lean production and automation, other optimization alternatives from further disciplines, such as digitization and networking, can be integrated.
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PublicationMethodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking( 2022)Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures. The first stage of the approach specifies the model's initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.
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PublicationMachinability analysis for milling of additively manufactured Inconel 718 with specifically induced porosity( 2022)Compared to conventional manufacturing technologies, additive manufacturing (AM) offers great design freedom, the integration of functions into components, new lightweight construction concepts and high material efficiency. This technology is increasingly coming into focus in aerospace and turbomachinery engineering, especially the Laser Powder Bed Fusion (LPBF) process. LPBF is already being used for some aerospace components that are often subject to high thermal and mechanical loads. Depending on the component geometry, support structures are required for additive manufacturing, which then have to be removed, usually by machining. Among others, the use of material with specifically induced porosity is suitable as a support structure. This ensures good heat dissipation and thus homogeneous component properties, high retention forces and short process times in the LPBF process. However, the machinability of porous, additively manufactured material has hardly been researched to date. This paper therefore presents the results of machinability investigations with porous, additively manufactured Inconel 718. The investigations included the analysis of active cutting force, cutting tool wear, surface finish and chip geometry in the milling process with tungsten carbide cutting tools. It was found that with the porous material, the dominant type of wear is early starting chipping of the cutting tool edges. The active force decreases with increasing porosity. Partial smearing of the pores was observed on the milled surfaces. The chips of the porous material show a disrupted surface. In future investigations, the aim is to improve the wear behaviour when milling porous Inconel 718.
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