Univ. Prof. Dr. Ing.

Bergs, Thomas

Filters
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Assessing the Environmental and Economic Impact of Wire-Arc Additive Manufacturing
    ( 2024-05)
    Derra, Thomas orcid-logo
    ;
    Belghith, Khouloud
    ;
    Gräfe, Stefan
    ;
    Day, Robin
    ;
    Bergs, Thomas
    Additive Manufacturing (AM) has continuously been integrated in the modern production landscape and complements traditional manufacturing processes by allowing the creation of complex three-dimensional objects through layer-by-layer material deposition. Especially with new design opportunities and short lead times it has significant impact on different industrial sectors such as healthcare, automotive and aerospace. Compared to other AM technologies, Wire Arc Additive Manufacturing (WAAM) has a particularly high material deposition rate and a high degree of flexibility when building large components. Therefore, WAAM has great potential for efficient and resilient production. To quantify this potential the environmental and economic impact must be assessed. The presented study focuses Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) and presents a general methodology for impact analysis as well as a transfer to WAAM. The methodology consists of four steps in accordance with ISO 14044:2006: goal and scope definition, inventory analysis (environmental/economic), environmental impact assessment/cost aggregation, interpretation. For the transfer to WAAM a cradle-to-gate analysis is conducted. The relevant process chain leads from alloy production to the WAAM product manufacturing. The methodology generates relative data, so the final assessment of WAAM must be set into context with alternative processes.
  • Publication
    Methodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking
    ( 2022)
    Dannen, Tammo
    ;
    Schindele, Benedikt
    ;
    Prümmer, Marcel
    ;
    Arntz, Kristian
    ;
    Bergs, Thomas
    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.