In Laser-Induced Breakdown Spectroscopy (LIBS) detection, the different surface morphologies of the sample can change the focusing distance between the focusing lens and the sample surface, resulting in instability of LIBS spectra and low signal-to-noise ratio, which affects the accuracy and reliability of detection. The LIBS auto-focusing system is designed to search the optimal focusing distance and thus obtain stable and reliable spectra. By adding a visible light source, the combination of focal spot image of the visible light on the sample surface and LIBS spectra are applied to search the focus position. The focusing process is divided into coarse-step and fine-step focusing. The four-neighborhood weighted gradient operator is adopted as the evaluation index of the clarity of the visible spot image. The hill-climbing method is used to focus by coarse-step. The Self-Organizing feature Mapping (SOM) artificial neural network is established by training and learning the fused data of visible spot images and LIBS spectra, which is to achieve fine step focusing. The experimental results show that, compared with the other two focusing methods, the proposed method has the best spectral performance. Its LIBS spectral intensity is the highest and the Relative Standard Deviation (RSD) of characteristic spectral line is the lowest, average value reduced from 14.71% to 6.31%. The Signal-to-Noise Ratios (SNRs) of characteristic spectral lines of the main elements, Fe I 385.9955 nm and Cr I 513.8852 nm, are increased from 19.18 and 14.28 to 24.71 and 23.62, respectively. The spectral intensity, stability, and sensitivity are improved effectively.
Laser-induced breakdown spectroscopy (LIBS) is an effective technology to analyze the content of the target elements. The surface morphology of the target will affect the coupling between the laser and the target, which will change the plasma spectrum and lead to inaccurate results. The surface relief and surface roughness are taken as the research parameters of the target surface morphology, the influence of which on LIBS spectrum are researched. The LIBS spectra are acquired on a set of ferroalloy targets, whose included angles θ with horizontal direction changed from -10° to 10°, or surface roughness are different. On the basis of theoretical derivation, we explore the variation trends of line intensity, line integral area, line intensity ratio of different main elements, and line intensity ratio of the same main element with surface morphology parameters. The experimental results have an increasing trend with the increase of θ and a decreasing trend with the decrease of surface roughness. The line intensity ratios are closely related to the change of surface morphology. The line integral area of Cr Ⅰ 429.3438nm has a large variation amplitude and higher correlation coefficient R, which is suitable for characterizing the change of LIBS spectrum with the target surface morphology. The results can provide a valuable reference for reducing the influence of target surface morphology on LIBS detection.
The surface laser speckle image is obtained by the reflected and scattered light beams from a rough surface illuminated by laser. Based on the fractal theory, Double Blanket Model (DBM) is proposed to analyze laser speckle images. The dimension of the space surface is regarded as the characteristic parameter in DBM method. Laser speckle images are preprocessed to remove interference and noise from the environment at first. The size and direction of optimum window are researched. The DBM characteristic parameter is calculated under the optimum window. The relationships are researched between DBM characteristic parameter and surface roughness Ra. The results show that the surface roughness contained in the surface speckle images has a good monotonic relationship with DBM characteristic parameter. To obtain roughness value through a laser speckle image, the fitting function relationship between Ra and DBM characteristic parameter is established, and the fitting function stability is analyzed by experiments. The experiment results show that surface roughness measurement based on DBM method of laser speckle is feasible and applicable to on-line high-precision roughness detection, which has some advantages such as non-contact, high accuracy, fast, remote measurement and simple equipment.
Based on the computer texture analysis method, a new noncontact surface roughness measurement technique is proposed. The method is inspired by the nonredundant directional selectivity and highly discriminative nature of the wavelet representation and the capability of the Markov random field (MRF) model to capture statistical regularities. Surface roughness information contained in the texture features may be extracted based on an MRF stochastic model of textures in the wavelet feature domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients. To investigate the relationship between the texture features and surface roughness Ra, a simple research setup, which consists of a charge-coupled diode camera without a lens and a diode laser, was established, and the laser speckle texture patterns are acquired from some standard grinding surfaces. The research results have illustrated that surface roughness Ra has a good monotonic relationship with the texture features of the laser speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value Ra can be deduced in the case of the same material surfaces ground at the same manufacture conditions.
A non-contact surface roughness measurement technique is put forward based on texture analysis of the digital laser
speckle pattern coming from a measured grinding surface. The speckle pattern was captured by a digital camera while
the grinding metal surface was illuminated by a laser. Then the surface roughness information immerged in the high
frequency sub-band of the speckle pattern was extracted with the texture analysis of wavelet transform and Markov
Random Fields model. Our research has illustrated that surface roughness Ra has a good monotonic relationship with the
texture features of the speckle pattern. If this measuring system is calibrated with the surface standard samples roughness
beforehand, the surface roughness actual value Ra may be obtained in the case of the same material surfaces ground at
the same manufacture conditions.
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