Additive Manufacturing

Safeguarding Against Risks in the Wire Arc Additive Manufacturing Process

Safeguarding Against Risks in the Wire Arc Additive Manufacturing Process

Lennart Grüger ORCID Icon, Tim Sebastian Fischer, Ralf Woll, Johannes Buhl ORCID Icon
In this article, the potential risks in wire arc additive manufacturing are analyzed using failure mode and effects analysis. To achieve this, 186 possible causes of risk were analyzed and the five most critical risks were discussed in detail. Four significant risk factors were identified in the construction process. The fifth risk relates to the shielding gas flow. This is only one influencing factor among the welding parameters, which have strong interactions with each other. Therefore, their relationships should be analyzed on the basis of numerous tests.
Industry 4.0 Science | Volume 40 | 2024 | Edition 1 | Pages 63-69 | DOI 10.30844/I4SE.24.1.63
Modeling Influences on the Wire Arc Additive Manufacturing Process

Modeling Influences on the Wire Arc Additive Manufacturing Process

Tim Sebastian Fischer, Lennart Grüger ORCID Icon, Ralf Woll
Wire Arc Additive Manufacturing (WAAM) is an additive manufacturing process which produces metallic components on the basis of arc welding. ISO/ASTM 52900 describes additive manufacturing as a process that creates components layer by layer from 3D model data. The basic equipment required includes a welding device, introducing the energy necessary for melting the metal wire, and a guiding machine, which traces the specified geometry of the component. Applications for WAAM include rapid prototyping and tooling, direct manufacturing and additive repair. The greatest advantages the process offers are low-cost system technology and a high deposition rate. The disadvantages of the process are the lack of process stability and exact repeatability. This article is intended to provide a clear overview of the WAAM manufacturing process, and to address its complex interactions.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 5 | | DOI 10.30844/I4SE.23.1.80
Holistic Clamping and Referencing

Holistic Clamping and Referencing

Improving 3D printing and further processing of metal parts
Moritz Wollbrink, Semir Maslo, Kristian Arntz, Thomas Bergs
The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surface roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the article presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 4 | Pages 35-39
Implementation of Additive Manufacturing

Implementation of Additive Manufacturing

An Analysis of Supply Chain Related Decision Factors of the Implementation Decision
Ralf Elbert, Anne Friedrich, Elisa Schuhmann
Additive manufacturing technologies, such as 3D printing, have reached a stage of performance for industrial application such as small series and spare parts. The adoption of additive manufacturing has so far mostly been investigated from the perspective of individual manufacturing firms. This paper focuses on the identification of overarching influence factors. In a category system, influence factors are analyzed from the perspectives of the supply and demand side, the supply chain actors and flows as well as sustainability, thus contributing to the adoption from a supply chain perspective.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 4 | Pages 30-34
LearningGripper – Machine Learning in the Factory of the Future

LearningGripper – Machine Learning in the Factory of the Future

Grasping and orientation through independent learning
Arne Rost, Elias Maria Knubben, Nina Gaissert
The LearningGripper from Festo looks like an abstract form of the human hand. The four fingers of the compliant gripper are driven by 12 pneumatic bellows actuators with low-level pressurisation. Thanks to the process of machine learning, it is able to teach itself to carry out complex actions such as, for example, gripping and positioning an object. By means of the LearningGripper we demonstrate how the development of such complex systems will be accelerated in the production of the future. Furthermore, the specific usage of machine learning algorithms will increase the efficiency of whole production plants.
Industrie Management | Volume 31 | 2015 | Edition 1 | Pages 13-16