# Methodology for Verification of Geometrically Complex Components Through Reverse Engineering
## 5 Conclusions  
Reverse engineering has proven to be a highly effective tool in optimizing manufacturing processes, especially in reducing geometric deviations and improving the quality of final products. The use of 3D scanning to collect detailed geometric data allowed for the identification of inconsistencies in design and manufacturing during the early stages of production. This approach enabled the timely implementation of necessary adjustments, leading to significant improvements in the precision of manufacturing processes. Unlike traditional measurement methods, which can be time-consuming and prone to human error, reverse engineering offers a faster, more accurate, and automated alternative. Three-dimensional scanning allows for the acquisition of complex geometric data, which can be immediately analyzed using specialized software, eliminating the need for manual measurements and reducing the risk of errors. As a result, not only is accuracy improved, but the overall manufacturing process becomes more efficient, minimizing the need for network and reducing the costs of rework and claims. The research focused on developing a methodology for verifying geometric components through 3D scanning and deviation analysis, contributing to improved geometric accuracy and product quality. The proposed methodology, implemented in real-world manufacturing processes, allowed for the early detection of inconsistencies and errors, achieving higher compliance with quality requirements and minimizing the need for rework, thus reducing repair costs. The aim was to enhance the reliability and quality of products during the early stages of production, which had a positive impact on the overall efficiency of the manufacturing process. Tests showed that the implementation of this methodology led to a measurable reduction in geometric and welding errors, as well as an increase in the quality of the final products, particularly in the case of welding fixtures and other critical components. The proposed methodology combined theoretical approaches with practical experiments, emphasizing the iterative process of fixture efficiency. Measurement results indicate that we were able to approach zero deviation. With a tolerance of \(\pm 1\) mm, the lowest deviation was almost zero, specifically 0.002 mm, which demonstrates the effectiveness of the applied methodology. With the highest tolerance of \(\pm 4.5\) mm, the maximum deviation was 1.19 mm, and with a tolerance of \(\pm 1\) mm, we also recorded a deviation of \(-0.66\) mm. This progress is the result of a precise process of verifying geometric components through 3D scanning and deviation analysis. However, it is important to note that although we aim to ensure consistent conditions during repeated measurements, these processes may be influenced to some extent by various factors, such as small variations in material properties, environmental conditions, or technical settings during individual measurements. These factors may slightly affect the results, although the methodology overall showed significant improvement in accuracy and quality. Despite these potential influences, we still achieve values that are below the requirements set for production, significantly reducing the need for rework and repair costs. The results suggest that further improvements in the design of welding fixtures are needed to achieve even better performance. Future efforts should focus on more precise adaptation of fixtures to different material properties and changes that occur during the manufacturing process. However, these materials may be more expensive and require additional modifications in production. Such a step could be advantageous in applications where the highest precision is required, but economic considerations and manufacturing capabilities will need to be considered. A crucial step in process optimization is improving measurement technologies and integrating them with IoT elements directly into welding fixtures.