Optimization of tool steel properties and corresponding heat treatment is mainly based on trial and error approach, which requires tremendous experimental work and resources. Therefore, there is a ...huge need for tools allowing prediction of mechanical properties of tool steels as a function of composition and heat treatment process variables. The aim of the present work was to explore the potential and possibilities of artificial neural network-based modeling to select and optimize vacuum heat treatment conditions depending on the hot work tool steel composition and required properties. In the current case training of the feedforward neural network with error backpropagation training scheme and four layers of neurons (8-20-20-2) scheme was based on the experimentally obtained tempering diagrams for ten different hot work tool steel compositions and at least two austenitizing temperatures. Results show that this type of modeling can be successfully used for detailed and multifunctional analysis of different influential parameters as well as to optimize heat treatment process of hot work tool steels depending on the composition. In terms of composition, V was found as the most beneficial alloying element increasing hardness and fracture toughness of hot work tool steel; Si, Mn, and Cr increase hardness but lead to reduced fracture toughness, while Mo has the opposite effect. Optimum concentration providing high K
Ic
/HRC ratios would include 0.75 pct Si, 0.4 pct Mn, 5.1 pct Cr, 1.5 pct Mo, and 0.5 pct V, with the optimum heat treatment performed at lower austenitizing and intermediate tempering temperatures.
Performance characteristics of the products made of metallic materials such as wear resistance, fatigue strength, stability of gaps and strain between the connections, corrosion resistance, etc., ...depend to a large extent by the quality of their surfaces roughness. An interactive control of the manufacturing parameters which influence the surface roughness is particularly crucial in the construction of many mechanical components. The present paper devises a new method for statistical pattern recognition on samples produced by the process of robot laser hardening using network theory and describes its application to the determination of surface roughness. The method is based on the analysis of SEM images. Indeed the data characterizing the state of surface irregularities detected as extremely small segments contain indicators of surface roughness. Different methods of machine learning techniques designed to predict the surface roughness of robot laser hardened material are discussed.
The paper is an attempt to describe how neural networks may be used as an approximation–modelling tool. A brief survey of the evolution of the approximation theory and neural networks is presented. ...Practical applications are based on modelling of vacuum science problems, especially the modelling of a cold cathode pressure gauge. The problem of approximation of wide range functions, that are one of the characteristics of vacuum science problems, is introduced. Parameters such as pressure or cathode current span over several decades and neural networks are not suitable for any approximation of such functions; therefore, two strategies need to be introduced, and these are described. The approximation made by the neural network is obtained by the training process. The models obtained by several independent repetitions of training processes performed on the same training set lead to slightly different results. Therefore the definition of training stability is introduced and described. Finally, some practical hints regarding the neural network synthesis (design) are given.
We have investigated new and retrieved cementless hip endoprostheses that prematurely failed due to (i) aseptic loosening, (ii) infection and (iii) latent infection. The aim was to better understand ...the physico-chemical phenomena on the surfaces and sub-surfaces of the Ti6Al7Nb alloy implant. The results of our studies should enable us to distinguish the causes of premature failure, optimize the surface modification, achieve optimal osseointegration and extend the useful lifetime of the implants. The surface properties of the Ti6Al7Nb alloys of the hip-stem endoprostheses (30 retrieved and 2 new) were determined by contact-angle measurements and the average surface roughness. The surface chemistry and microstructure were analysed by scanning electron microscopy (SEM) for morphology, energy-dispersive X-ray spectroscopy (EDS) for the chemistry, and electron back-scatter diffraction (EBSD) for the phase analysis; Auger electron spectroscopy (AES) and X-ray photoelectron spectroscopy (XPS) for the surface chemistry; and electrochemical measurements for the corrosion. The improved wettability of the grit-blasted surface of the Ti6Al7Nb stems after autoclaving was measured, as was the super wettability after oxygen-plasma sterilization. The secondary-electron images showed that the morphology and microstructure of the new and retrieved stems (prematurely failed due to aseptic loosening, infection and latent infection) differ slightly, while the EDS analysis revealed corundum contamination of the grit-blasted surface. We found corundum-contaminated Ti6Al7Nb stem surfaces and sub-surfaces for all the investigated new and retrieved implants. These residues are a potential problem, i.e., third-body wear particles, and probably induce the osteolysis and aseptic loosening.
This article describes modelling of the operating characteristics of a cold-cathode ionisation gauge (CCG). The gauge characteristics were measured on a gauge comparison UHV calibration system with a ...test chamber, an extractor gauge, a spinning rotor gauge, and a gas manifold with a precise leak valve. Discharge intensity was measured vs. anode voltage at different pressures selected in the range from 1×10
−9 to 1×10
−5
mbar, and vs. pressure at different operating voltages ranging from 1.2 to 9
kV. In all cases the magnetic flux density was the same and amounted to about 0.13
T. The CCG exhibits an extremely low thermal outgassing rate and a low measurement limit. Therefore, it is suitable for pressure measurements in the ultrahigh vacuum range; however, it has a significant disadvantage. The discharge current vs. the pressure characteristic is non-linear and, in some cases, even discontinuous.
The measured CCG characteristics were used as an input for the artificial neural network, which was used to generate a non-linear CCG input–output function used for linearisation purposes. It is generally known and strictly proven that neural networks are capable of learning and building any kind of real and non-polynomial input–output function. Furthermore, it was also mathematically proven that the single hidden neural layer system can learn any function. Other authors have reported that the learned function characteristics are not always continuous.
In our experimental work, no mapping discontinuities in the formed model were detected. Despite the fact that learning of the input–output characteristics can be obtained by the neural networks with only one hidden layer, we have used the multilayer neural networks that exhibit a faster convergent and smoother learning process. The neural networks were trained to perform the transfer function between the input gauge parameters and the pressure. The neural networks are a suitable solution for CCG characteristics modelling and thus offer the possibility to overcome the disadvantages of the CCG.
PDF
