Development of a hybrid photoacoustic and optical monitoring system for the study of laser ablation processes upon the removal of encrustation from stonework

In the context of this work,a prototype hybrid photoacoustic(PA)and optical system for the on-line monitoring of laser cleaning procedures is presented.The developed apparatus has enabled the detection of MHz frequency range acoustic waves generated during the laser ablation process.The intrinsically generated PA signals combined with high resolution optical images provide the opportunity to follow the cleaning process accurately and in real time.Technical mock-ups have been used to demonstrate the potential of this novel technique with emphasis given to applications that refer to the restoration of Cultural Heritage(CH)surfaces.Towards this purpose,the real time monitoring of the laser assisted removal of unwanted encrustation from stonework has been achieved using IR and UV wavelengths.This novel approach has allowed for the precise determination of the critical number of laser pulses required for the elimination of the encrustation layer,while highlighting the dominant ablation mechanisms according to the irradiation wavelength.The promising results obtained using the prototype hybrid PA and optical system can open up new perspectives in the monitoring of laser cleaning interventions,promoting an improved restoration outcome.

Application of thermal stress model to paint removal by Q-switched Nd:YAG laser

In this paper, we demonstrate that thermal stress is the main mechanism in the process of paint removal by Q-switched Nd：YAG laser （λ = 1064 nm, τ = 10 ns）. A theoretical model ofpaint removal by short-pulse laser is established from the perspective of thermal stress. Thermal stress is generated by thermal expansion, and the temperatures of different samples are calculated according to the one-dimensional （1D） heat conduction equation. The theoretical cleaning threshold can be obtained by comparing thermal stress with the adhesion of paint, and the theoretical damage threshold is obtained by calculating the temperature. Moreover, the theoretical calculations are verified by experimental results. It is shown that the thermal stress model of the laser cleaning is very useful to choose the appropriate laser fluence in the practical applications of paint removal by Q-switched Nd： YAG laser because our model can validly balance the efficiency of laser cleaning and the safety of the substrate.

Removing paint from a metal substrate using a flattened top laser

In this paper,we investigate laser cleaning using a flattened top laser to remove paint coating from a metal substrate.Under the irradiation of a flattened top laser,the coating paint of the metal substrate can be removed efficiently by laser induced ablation,stress,and displacement force.The temperature distribution,stress,and displacement are calculated in the coating layer and substrate using finite element analysis.The effects of a Gaussian laser and a flattened top laser and the results of different diameters of laser spot are compared.The investigation shows that the flattened top laser can reduce the substrate damage and enhance the cleaning efficiency.This method meets the need of large area industrial cleaning applications by optimizing the flattened top laser parameters.

Test method of laser paint removal based on multi-modal feature fusion

Laser cleaning is a highly nonlinear physical process for solving poor single-modal(e.g., acoustic or vision)detection performance and low inter-information utilization. In this study, a multi-modal feature fusion network model was constructed based on a laser paint removal experiment. The alignment of heterogeneous data under different modals was solved by combining the piecewise aggregate approximation and gramian angular field. Moreover, the attention mechanism was introduced to optimize the dual-path network and dense connection network, enabling the sampling characteristics to be extracted and integrated. Consequently, the multi-modal discriminant detection of laser paint removal was realized. According to the experimental results, the verification accuracy of the constructed model on the experimental dataset was 99.17%, which is 5.77% higher than the optimal single-modal detection results of the laser paint removal. The feature extraction network was optimized by the attention mechanism, and the model accuracy was increased by 3.3%. Results verify the improved classification performance of the constructed multi-modal feature fusion model in detecting laser paint removal, the effective integration of acoustic data and visual image data, and the accurate detection of laser paint removal.