Manufacturing Systems

The development of textile products is associated with high material, time, personnel and cost expenditure. The paper describes the digital twinning for materials and processes and their application in a digital product development process, which can accelerate the ramp-up phase and thus reduce development costs by up to 60%.

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.

Global supply and value chains have become increasingly complex and interconnected, exposing companies to a range of risks caused by natural disasters, political instability, or global pandemics. The paper outlines some strategic options for companies to improve the resilience of their value chains, namely expansion of local or global supply chains, regional concentration or global diversification of production capacities, and insourcing or outsourcing activities. Data of 314 German manufacturing firms is used to investigate the influence of different digital technologies and adaptable production systems.

In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.