In this paper

the detection methods and evaluation systems for material surface cleanliness after laser cleaning are systematically reviewed. These encompass various online detection techniques such as laser-induced breakdown spectroscopy (LIBS)

fluorescence spectroscopy

reflectance spectroscopy

acoustic signal analysis

and image recognition

as well as offline methods like wettability testing

surface morphology observation

and elemental analysis. The research finds that these diverse methods can effectively identify the characteristic stages of insufficient cleaning

complete cleaning

and excessive cleaning during the cleaning process

with distinct signal features reflecting the evolution of the cleaning state. The collaborative analysis of multi-source detection information aids in enhancing the accuracy of state recognition and process monitoring. At present

there are differences among these methods in terms of response mechanisms

evaluation dimensions

and application scopes. Techniques such as LIBS

spectroscopy

and acoustic signal analysis are more suited for laboratory conditions

whereas image acquisition and analysis

as well as roughness and wettability assessments

are more practical for on-site industrial monitoring. Each detection method yields distinct results with its own characteristics. Future research can further explore areas such as multi-modal integration

cross-scale modeling

and the establishment of standardized systems

thereby advancing laser cleaning quality detection towards higher precision

intelligence

and engineering applicability. This progression will help meet the technical demands for surface cleanliness control in aerospace manufacturing and other advanced industrial fields.