# Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
## 1 Introduction  
The material-forming processes for manufacturing pipe-shaped parts require the generation of process models as an essential task in production engineering [1]. In recent years, significant technological development has been made in this type of modelling, resulting in a wide variety that continues to expand [2]. The need to continuously advance in process modeling and its limits is a recent trend in manufacturing engineering. The design and parameterization of robust processes can be effectively evaluated with appropriate models [3], which helps meet the demand for tighter product tolerances [4]. The inclusion of these models and the virtualization of the workspace enable the digitization of manufacturing in the era of new smart factories [5]. The metal-forming processes selected for modelling stand out due to their efficiency and relevance in various industrial applications. These processes offer a favourable terrain for modelling due to their significant impact on production and their versatility in creating metal components with various shapes and properties. Among the parts that can be manufactured through mechanical sheet forming, many engineering applications, such as the skeleton of oil-and-gas platforms, tunnel construction, and commercial and industrial buildings [6], employ large- and medium-sized tubes andtubular sections. Round tubes generally have curves of single or double orders. The manufacturing of tubes can be performed sequentially or divided into phases, first by preforming or roller bending and then by forming to the final geometry. Efficient bending is a prerequisite for precise formation of bending surfaces [7]. In normal practice, the skill, knowledge, and experience of the operator largely determine the final quality of roller bending. Adjusting the process through trial and error and subsequent work with templates is a common practice in the industry. The integrity of flexible pipes used in offshore oil-and-gas transportation is critical, and failures related to tensile armor fractures have raised concerns [8]. To address this issue, one study focused on characterizing a tensile wire made of cold-worked high-carbon steel. The microstructure analysis revealed a fine-and-deformed pearlite structure with fragmented and spheroidized cementie carbides. Mechanical properties were determined through tensile testing, and a fatigue curve was constructed to assess the wire's resistance to cyclic loading [9]. These findings provide valuable insights into the microstructure and mechanical behavior of the high-carbon steel used in tensile wires for flexible pipes, contributing to improved understanding and potential enhancements in the construction of these vital components. In another study, the influence of bending roll profiles on the stress-strain state of billets for longitudinally welded rolled pipes was investigated using 3D finite-element modeling. The researchers explored alternative methods for lowering the upper roller of a traditional profile during the bending process [10]. By analyzing the resulting geometry, deformations, and stress distribution, they identified areas of the billets that experienced the main load and underwent plastic deformation and local thinning. The researchers proposed changes to the bending tool's profile aimed to create additional contact pads during bending, potentially improving the quality of the produced billets. This research highlights the significance of optimizing the bending process for ensuring the structural integrity and performance of longitudinally welded rolled pipes used in various applications. However, in recent years, there has also been significant research on the monitoring of sheet bending with rollers [11; 12; 13]. The monitoring of the sheet-bending process can also be improved with the application of artificial-intelligence techniques. For example, machine learning algorithms can be used to predict the geometric deviation of curved parts and detect problems in the manufacturing process.