Aluminum nitride (AlN) films were synthesized onto Si(100) substrates by pulsed laser deposition (PLD) in vacuum or nitrogen, at 0.1, 1, 5, or 10 Pa, and substrate temperatures ranging from RT to 800 °C. The laser parameters were set at: incident laser fluence of 3–10 J/cm2 and laser pulse repetition frequency of 3, 10, or 40 Hz, respectively. The films’ hardness was investigated by depth-sensing nanoindentation. The optical properties were studied by FTIR spectroscopy and UV-near IR ellipsometry. Hardness values within the range of 22–30 GPa and Young’s modulus values of 230–280 GPa have been inferred. These values were determined by the AlN film structure that consisted of nanocrystallite grains, strongly dependent on the deposition parameters. The values of optical constants, superior to amorphous AlN, support the presence of crystallites in the amorphous film matrix. They were visualized by TEM and evidenced by FTIR spectroscopy. The characteristic Reststrahlen band of the h-AlN lattice with component lines arising from IR active phonon vibrational modes in AlN nanocrystallites was well detectable within the spectral range of 950–500 cm−1. Control X-ray diffraction and atomic force microscopy data were introduced and discussed. All measurements delivered congruent results and have clearly shown a correlation between the films’ structure and the mechanical and optical properties dependent on the experimental conditions.
The estimation 01 parameters and obtaining an accurate and comprehensive mathematical model of the polymerization process is 01 strategie importance to the Control engineering purposes in the polymerization industry.It is characteristic lor these processes a grate non-linearity and many diliireulties applying traditional estimation techniques. This paper describes a n approach based upon neural-luzzy representation of the model. A concrete model is constructed with the Sugeno fuzzy inference technique and a fuzzy-neural network is used to model the dynamic behavior of the polymer process. Such neural-luzzy models of polymer quality could be used successfully for optimization and control of polymerization processes. Short example lor such implementation is included with additional results for modeling o l M n and Mw.Index Terms-Fuzzy neural networks, Fuzzy modeling, Process control, Polymerization. I. INTRODIJCTIONAdvanced modeling and control of polymerization processes is of major strategic importance to the polymer manufacturing industries. The main goal of polymer production is to reduce the costs whilst maintaining or increasing the yield and also ensuring that the quality and consistency of the final product is maintained through safe operation of the process. On the other hand there are many factors limiting the development of comprehensive policies for controlling the properties of the polymer processesa lack of detailed understanding and modeling of the dynamics of the process and the highly sensitive and nonlinear behavior of the process. It is necessary to apply appropriate process control technology, modeling and optimization technique to achieve better results according to the goals. The development of modeling and control of polymerization reactors has been reviewed by a number of authors [I]. Much of research in the area of polymerization processes has focused upon the estimation of infrequently measured polymer quality variables through non-linear state estimation techniques [2], the development of optimal trajectories for the manipulated variables [3]. The neural networks offer an excellent approximation possibility and they are recently often used to approximate functions that defme the plant input/output characteristics. They have been applied widely in process modeling and control [4]-[5]. A survey for different approaches to fuzzy modeling can be found in [6]. 0-7803-8278-
A nanocomposite CrAlSiN–AlSiN coating with periodically modulated composition was developed and investigated regarding the effect of the composition and structure on the mechanical properties. The modulation was performed by variation of the pressure, cathode current and bias voltage during deposition. The structure and composition of the coating were investigated by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) analyses. The coating had a nanocomposite structure consisting of (CrAl)N and (AlSi)N nanograins embedded in a Si3N4 matrix. The EDS analysis of the cross-section revealed that the period composition had changed from Cr051Al0.41Si0.08N to Al0.82Cr0.04Si0.14N. It was shown that the elastic modulus could be adjusted by composition modulation. The coating hardness of 54 GPa was obtained by nanoindentation. The modulated CrAlSiN–AlSiN coating exhibited improved elastic strain to failure (H/E* = 0.11, H—nanohardness, E*—the effective elastic modulus), excellent resistance to plastic deformation (H3/E*2 = 0.72), and elastic recovery of 70%, which suggested improved toughness.
A study of the structural and mechanical properties of nanocrystalline TiAlSiN gradient coatings deposited by cathodic arc deposition techniques at 500 °C and post-annealed at 525 °C is presented. Analysis of the coatings, chemical composition and microstructure revealed that the coatings have a structure based on (Ti, Al)N nanocrystals with an average size of 10 nm embedded in an amorphous Si3N4 phase. The study of the mechanical properties showed that post-annealing causes improvement and increase of the coatings hardness. A maximum hardness of 48 GPa and elastic modulus of 560 GPa were measured. Also, excellent adhesion to the WC-Co substrate was observed in the post-annealed coatings.
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