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- Calibration-free analysis (1)
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A neural network-based analysis method for the identification of a viscoplasticity model from spherical indentation data, developed in the first part of this work [J. Mater. Res. 21, (2006)], was applied for different metallic materials. Besides the comparison of typical parameters like Young's modulus and yield stress with values from tensile experiments, the uncertainties in the identified material parameters representing modulus, hardening behavior, and viscosity were investigated in relation to different sources. Variations in the indentation position, tip radius, force application rate, and surface preparation were considered. The extensive experimental validation showed that the applied neural networks are very robust and show small variation coefficients, especially regarding the important parameters of Young's modulus and yield stress. On the other hand, important requirements were quantified, which included a very good spherical indenter geometry and good surface preparation to obtain reliable results.
The improved Monte-Carlo (MC) method for standard-less analysis in laser induced breakdown spectroscopy (LIBS) is presented. Concentrations in MC LIBS are found by fitting model-generated synthetic spectra to experimental spectra. The current version of MC LIBS is based on the graphic processing unit (GPU) computation and reduces the analysis time down to several seconds per spectrum/sample. The previous version of MC LIBS which was based on the central processing unit (CPU) computation requested unacceptably long analysis times of 10's minutes per spectrum/sample. The reduction of the computational time is achieved through the massively parallel computing on the GPU which embeds thousands of co-processors. It is shown that the number of iterations on the GPU exceeds that on the CPU by a factor > 1000 for the 5-dimentional parameter space and yet requires > 10-fold shorter computational time. The improved GPU-MC LIBS outperforms the CPU-MS LIBS in terms of accuracy, precision, and analysis time. The performance is tested on LIBS-spectra obtained from pelletized powders of metal oxides consisting of CaO, Fe2O3, MgO, and TiO2 that simulated by-products of steel industry, steel slags. It is demonstrated that GPU-based MC LIBS is capable of rapid multi-element analysis with relative error between 1 and 10's percent that is sufficient for industrial applications (e.g. steel slag analysis). The results of the improved GPU-based MC LIBS are positively compared to that of the CPU-based MC LIBS as well as to the results of the standard calibration-free (CF) LIBS based on the Boltzmann plot method.
Tomography of a laser-induced plasma in air is performed by inverse Radon transform of angle-resolved plasma images. Plasmas were induced by single laser pulses (SP), double pulses (DP) in collinear geometry, and by a combination of single laser pulses with pulsed arc discharges (SP-AD). Images of plasmas on metallurgical steel slags were taken at delay times suitable for calibration-free laser-induced breakdown spectroscopy (CFLIBS). Delays ranged from few microseconds for SP and DP up to tens of microseconds for SP-AD excitation. The white-light and the spectrally resolved emissivity ε(x,y,z) was reconstructed for the three plasma excitation schemes. The electron number density Ne(x,y,z) and plasma temperature Te(x,y,z) were determined from Mg and Mn emission lines in reconstructed spectra employing the Saha-Boltzmann plot method. The SP plasma revealed strongly inhomogeneous emissivity and plasma temperature. Re-excitation of plasma by a second laser pulse (DP) and by an arc discharge (SP-AD) homogenized the plasma and reduced the spatial variation of ε and Te. The homogenization of a plasma is a promising approach to increase the accuracy of calibration-free LIBS analysis of complex materials.