About methodical errors in studies of mass transfer processes induced by intense pulsed ion beams in solids
The present paper briefly discusses the questions of accuracy of depth profiling techniques in studies of mass transfer processes induced by intense pulsed ion beams (IPIB) in solids. The best measurement tools provide good precision only. An acceptable reproducibility, correctness, and, finally, accuracy of measurement are a result of complete elimination all of the systematic errors. The first ones are caused by a dependence of main SIMS and AES analytical characteristics, ionization coefficients and sputtering factors, on a degree of IPIB exposure. It is shown that the preferable application of nuclear analysis methods, RBS and PIGE, avoids these errors. A mechanical polishing conventionally applied for surface preparation of specimens under investigation engenders to unpredictable changing of mechanical, electric and thermodynamic properties of the surface layer, for at least 1 /spl mu/m depth in the case of Cu surface polishing and for the depth less than the previous one by the factor of 3-5 for /spl alpha/-Fe, steel, Zr, etc. Surface preparation with electrochemical polishing allows a second kind of error to be avoided. A third kind of systematic error is connected with vagueness of the main IPIB parameters (ion species, energy distribution, flux) at the investigated section of the modified sample. Use of more detailed specimens analyzed by cross section is suggested in order to avoid these errors.
Intense Ion-Beam Treatment of Materials
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Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.