Surface roughness modeling of CBN hard steel turning

Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters

Surface roughness detector Patent

Roughness detector for recording surface pattern of irregularitie

Measurement of surface roughness slope

Instrument, consisting of isolator, differentiator, absolute value circuit, and integrator, uses output signal from surface texture analyzer profile-amplifier to calculate surface roughness slope. Calculations provide accurate, instantaneous value of the slope. Instrument is inexpensive and applicable to any commerical surface texture analyzer

Experimental investigation on micromilling of oxygen-free, high-conductivity copper using tungsten carbide, chemistry vapour deposition and single-crystal diamond micro tools

Insufficient experimental data from various micro tools limit industrial application of the micromilling process. This paper presents an experimental comparative investigation into micromilling of oxygen-free, high-conductivity copper using tungsten carbide (WC), chemistry vapour deposition (CVD) diamond, and single-crystal diamond micromilling tools at a uniform 0.4mm diameter. The experiments were carried out on an ultra-precision micromilling machine that features high dynamic accurate performance, so that the dynamic effect of the machine tool itself on the cutting process can be reduced to a minimum. Micromachined surface roughness and burr height were characterized using white light interferometry, a scanning electron microscope (SEM), and a precision surface profiler. The influence of variation of cutting parameters, including cutting speeds, feedrate, and axial depth of cut, on surface roughness and burr formation were analysed. The experimental results show that there exists an optimum feedrate at which best surface roughness can be achieved. Optical quality surface roughness can be achieved with CVD and natural diamond tools by carefully selecting machining conditions, and surface roughness, Ra, of the order of 10nm can also be obtained when using micromilling using WC tools on the precision micromilling machine.EU FP6 MASMICRO projec

Surface Roughness Measurement Using Dichromatic Speckle Pattern: An Experimental Study

Surface roughness is studied experimentally by making use of the statistical properties of dichromatic speckle patterns. The rms intensity difference between two speckle patterns produced by two argon laser lines are analyzed in the far field as functions of the object surface roughness and the difference in the two wavenumbers of the illuminating light. By applying previously derived formulas, the rms surface roughness is obtained from rms intensity differences. Glass and metal rough surfaces are used. Other than the scattering arrangement, the experimental setup has a simple spectrometric system and an electronic analyzing circuit

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Stochastic analysis of surface roughness

For the characterization of surface height profiles we present a new stochastic approach which is based on the theory of Markov processes. With this analysis we achieve a characterization of the complexity of the surface roughness by means of a Fokker-Planck or Langevin equation, providing the complete stochastic information of multiscale joint probabilities. The method was applied to different road surface profiles which were measured with high resolution. Evidence of Markov properties is shown. Estimations for the parameters of the Fokker-Planck equation are based on pure, parameter free data analysis

Surface evolution during crystalline silicon film growth by low-temperature hot-wire chemical vapor deposition on silicon substrates

We investigate the low-temperature growth of crystalline thin silicon films: epitaxial, twinned, and polycrystalline, by hot-wire chemical vapor deposition (HWCVD). Using Raman spectroscopy, spectroscopic ellipsometry, and atomic force microscopy, we find the relationship between surface roughness evolution and (i) the substrate temperature (230–350 °C) and (ii) the hydrogen dilution ratio (H2/SiH4=0–480). The absolute silicon film thickness for fully crystalline films is found to be the most important parameter in determining surface roughness, hydrogen being the second most important. Higher hydrogen dilution increases the surface roughness as expected. However, surface roughness increases with increasing substrate-temperature, in contrast to previous studies of crystalline Si growth. We suggest that the temperature-dependent roughness evolution is due to the role of hydrogen during the HWCVD process, which in this high hydrogen dilution regime allows for epitaxial growth on the rms roughest films through a kinetic growth regime of shadow-dominated etch and desorption and redeposition of growth species

Role of surface roughness in superlubricity

We study the sliding of elastic solids in adhesive contact with flat and rough interfaces. We consider the dependence of the sliding friction on the elastic modulus of the solids. For elastically hard solids with planar surfaces with incommensurate surface structures we observe extremely low friction (superlubricity), which very abruptly increases as the elastic modulus decreases. We show that even a relatively small surface roughness may completely kill the superlubricity state.Comment: 11 pages, 17 figures, format revte