A general framework for auto-body assembly gap and flush measurement and virtual alignment via noise-robust breakpoint detection

This article presents a groundbreaking framework for measuring gap and flush (G&F) in automotive body assembly, addressing critical challenges in modern intelligent manufacturing. It begins by outlining the fundamental importance of G&F measurement for evaluating assembly quality between adjacent parts, not only influencing a vehicle’s aesthetic appeal and perceived quality but also being closely related to functional performance such as air tightness, operational smoothness, and NVH behavior. The paper then delves into the current limitations of traditional inspection methods, which largely depend on visual checks and feeler gauge measurements, suffering from low efficiency, high labor costs, and poor adaptability. In response, it introduces a general measurement framework based on noise-robust breakpoint detection, enabling high-precision measurement and supporting virtual alignment assessment. The article covers four key focal points: First, it explores the challenges and requirements for G&F measurement in the context of digital manufacturing and intelligent inspection systems, highlighting the need for robust detection of key measurement points and standardized evaluation models. Second, it provides a detailed review of major geometric analysis methods and their applications, focusing on breakpoint detection techniques for measured contours. Third, it presents a general framework for G&F measurement, including three standardized protocols and comparative analysis of their characteristics under different engineering requirements. Fourth, it validates the effectiveness and robustness of the proposed methods through both simulated point set experiments and real-world scanning and virtual assembly experiments, including consistency validation under rigid transformations and minor section perturbations. The paper concludes by outlining future research directions, including perceived quality-driven assembly optimization and online measurement integration for intelligent manufacturing, emphasizing the potential of the proposed framework for practical applications in digital auto-body assembly quality control.