# Methodology for Verification of Geometrically Complex Components Through Reverse Engineering
## 1 Introduction  
Currently, the verification of geometric components in industrial applications primarily relies on traditional measurement methods, such as coordinate measuring machines (CMMs) and manual measurements. While these techniques are effective for assessing basic geometric shapes, they encounter limitations when applied to complex geometries with minor deviations. Traditional methods can be time-consuming and prone to human error, ultimately affecting measurement accuracy and the overall quality of the components [1, 2, 3, 4]. With advancements in reverse engineering technologies, more efficient and precise verification of complex components, including welded structures, has become possible. Reverse engineering involves analyzing an existing product or component to understand its design, function, and parameters. It is widely used in manufacturing, quality control, digitalization, and metrology. The process consists of digitizing an object through 3D scanning, processing the acquired data, reconstructing a CAD model, and validating itsaccuracy. Three-dimensional scanners enable the capture of highly detailed geometric data, facilitating the creation of digital models that can be compared against predefined CAD specifications. This approach provides precise measurements and an in-depth deviation analysis between scanned data and original models, allowing for the identification of potential manufacturing defects, welding-induced deformations, and optimization of production processes. Consequently, reverse engineering enhances the ability to swiftly detect inconsistencies, contributing to improved quality and reliability of welded components. Similar challenges in component verification and the application of reverse engineering have been explored in previous research. The study conducted by [PERSON] et al. examines a reverse engineering approach to facilitate the remanufacturing of worn mechanical components with uncontrolled failure conditions and complex geometries. The research outlines a methodology that includes 3D surface data acquisition, nominal model reconstruction, precise registration, defect extraction, tool path generation, and machining processes. A Prominent Cross-Section algorithm with curvature constraints is employed to identify damaged areas and exclude defective point clouds during reconstruction. Additionally, an improved Iterative Closest Point (ICP) algorithm integrates curvature and distance constraints to achieve optimal alignment between worn and nominal models. The study concludes with the successful remanufacturing of mechanical components [5]. [PERSON] and his team also addressed this topic by examining the application of reverse-engineering-based 3D scanning technologies for quality control. Their study involved scanning a 3D-printed component with a complex geometry using a laser scanner. Two surface modeling techniques were tested: the primitive-based method, suitable for manufacturing, and NURBS modeling, which provided a more accurate surface representation. The research confirmed the suitability of 3D scanning for complex shapes while emphasizing the need to improve the efficiency and accuracy of reconstruction methods [6]. Furthermore, the study by [PERSON] et al. focuses on the analysis of residual deformations occurring during the resistance spot welding (RSW) process on a basic structural panel (BSP), which was designed using reverse engineering and finite element analysis (FEA) simulations. Here, reverse engineering was employed to compare the original and final geometries of the panel to determine the overall residual deformation, highlighting the interaction of deformations across multiple welding processes [7]. [PERSON] et al. investigated the use of visualization and machine learning for analyzing parameters influencing manufacturing quality. Their reverse-engineering-based approach identifies key parameters and their impact on parts using dimensionality reduction.