Modelling the scatter of E–N curves using a serial hybrid neural network

If structural reliability is estimated by following a strain-based approach, a material’s strength should be represented by the scatter of the ε–N (E–N) curves that link the strain amplitude with the corresponding statistical distribution of the number of cycles-to-failure. The basic shape of the ε–N curve is usually modelled by the Coffin–Manson relationship. If a loading mean level also needs to be considered, the original Coffin–Manson relationship is modified to account for the non-zero mean level of the loading, which can be achieved by using a Smith–Watson–Topper modification of the original Coffin–Manson relationship. In this paper, a methodology for estimating the dependence of the statistical distribution of the number of cycles-to-failure on the Smith–Watson–Topper modification is presented. The statistical distribution of the number of cycles-to-failure was modelled with a two-parametric Weibull probability density function. The core of the presented methodology is represented by a multilayer perceptron neural network combined with the Weibull probability density function using a size parameter that follows the Smith–Watson–Topper analytical model. The article presents the theoretical background of the methodology and its application in the case of experimental fatigue data. The results show that it is possible to model ε–N curves and their scatter for different influential parameters, such as the specimen’s diameter and the testing temperature.     

Batch Grinding in Laboratory Ball Mills: Selection Function

The selection functions and the breakage distribution functions are based on the experimentally‐determined particle size distribution on the basis of comminution of one size fraction particles. Therefore, to obtain a clear picture of the product properties during comminution of the “real” polydisperse sample, a number of experiments are needed. This work introduces the tested methodology for the selection function determination based on the starting and maximal values of the selection function. The principle was tested on the planetary ball mill and the horizontal laboratory ball mill, and according to the results obtained, it can be concluded that the proposed methodology can be useful for the evaluation of the selection function during batch comminution in different mills.     

Generalized renewal process for repairable systems based on finite Weibull mixture

Repairable systems can be brought to one of possible states following a repair. These states are: ‘as good as new’, ‘as bad as old’ and ‘better than old but worse than new’. The probabilistic models traditionally used to estimate the expected number of failures account for the first two states, but they do not properly apply to the last one, which is more realistic in practice. In this paper, a probabilistic model that is applicable to all of the three after-repair states, called generalized renewal process (GRP), is applied. Simplistically, GRP addresses the repair assumption by introducing the concept of virtual age into the stochastic point processes to enable them to represent the full spectrum of repair assumptions. The shape of measured or design life distributions of systems can vary considerably, and therefore frequently cannot be approximated by simple distribution functions. The scope of the paper is to prove that a finite Weibull mixture, with positive component weights only, can be used as underlying distribution of the time to first failure (TTFF) of the GRP model, on condition that the unknown parameters can be estimated. To support the main idea, three examples are presented. In order to estimate the unknown parameters of the GRP model with m-fold Weibull mixture, the EM algorithm is applied. The GRP model with m mixture components distributions is compared to the standard GRP model based on two-parameter Weibull distribution by calculating the expected number of failures. It can be concluded that the suggested GRP model with Weibull mixture with an arbitrary but finite number of components is suitable for predicting failures based on the past performance of the system.     

Mixing of solids in different mixing devices

Mixing of powders is a common operation in any industry. Most powders are known to be cohesive, many agglomerate spontaneously when exposed to humid atmosphere or elevated storage temperature. Agitation of the powder (especially powders with different bulk densities) may result in migration of smaller particles downwards and of larger ones upwards. Another problem is segregation whose main cause is the difference in particle size, density shape and resilience. There are standard mixing devices, such as drum tumblers or Turbula mixers. Alternate device type used is the static mixer of Kenics type. Static mixers save energy, disable segregation and effect particle migration. In this paper, static mixers, as devices for powder mixing, are tested as well as Turbula and V-shaped drum mixer, since those devices are commonly used for powder blending in industry. Mixtures that were blended by means of those three devices were made out of the model material, quartz sand, in different component ratios (20:80 and 30:70). The results were statistically calculated and graphically presented. Cohesion indexes were measured with Powder Flow Analyser to see the effect of material flow on the mixture quality. The results obtained by those three devices, the particle size effect and cohesion indexes, bring us to the conclusion that static mixers could be used for mixing of powders, but their shape, number of mixing elements and the mixer length should be adapted for each mixture separately, experimentally and mathematically, through modelling of the system.     

Host-country Absorption of Technology: Evidence from Automotive Supply Networks in Eastern Europe

This paper provides an analysis of technology transfer in automotive supply networks in six EU candidate countries with important vehicle (component) industries. We survey more than 400 firms, representing roughly half of the automotive supply industry. In addition, we have in-depth information from 39 case studies. We address the generation, the origin, and the quality of technology transfer. In terms of generation, we look at the determinants of who receives technology along the value chain, and who passes it on. In terms of origin, we compare local and foreign-owned firms and those with mixed ownership. We also compare differences across the countries. In terms of quality, we discuss the change-inducing effects of technology as perceived by recipient firms. Our results confirm the salience of networks and the key role of MNCs for the generation and diffusion of technology. We also find that diffusion of technology happens within the countries so that host countries absorb more technology than is immediately apparent and commonly believed.     

Evolutionary algorithm with fuzzy numbers for planning active distribution network

Modern approach to distribution systems planning requires developing tools able to interpret and analyze the stochastic nature of large power system problems. At the same time, it has to be able to optimize multiple, conflicting goals that appear in today’s open market environment. The presented evolutionary algorithm is based on a Multiple Vehicle Routing Problem adjusted for solving active electric distribution networks. An original approach to fuzzy number modeling is used for active power sources, consumption substations and distributed generation. This model, besides shorter computational time and less memory usage, is applicable to any form of fuzzy number shape required by the planner in modeling the stochastic nature of elements. An idea of defuzzification using pessimism is presented and compared to the solutions obtained without pessimism included. Application of the original universal fuzzy number modeling, using FER-fuzzy modeling, is demonstrated on multi-objective evolutionary algorithm and applied to real Croatian distribution network.     

Prediction of particle size distribution of dronedarone hydrochloride in spiral jet mill using design of experiments

In pharmaceutical industry, micronization is used to achieve solubility enhancement through the increase in specific surface area, and finally improving the dissolution rate of the drug. Industrial application of micronization is mostly based on experience and trial and error method. This research deals with the micronization of dronedarone hydrochloride and the path for evaluation of characteristics of micronized drug. Micronization was performed in laboratory spiral jet mill and the samples were analyzed to determine particle size distribution, span of distribution, sphericity, shape, and specific surface area. Results were analyzed using analysis of variance and in terms of specific energy consumption. Paths for evaluation of particle size and span of distribution were proposed and good correlation between experimental and model results was achieved.     

Design of Engineered Elastomeric Substrate for Stretchable Active Devices and Sensors

In the field of flexible electronics, emerging applications require biocompatible and unobtrusive devices, which can withstand different modes of mechanical deformation and achieve low complexity in the fabrication process. Here, the fabrication of a mesa‐shaped elastomeric substrate, supporting thin‐film transistors (TFTs) and logic circuits (inverters), is reported. High‐relief structures are designed to minimize the strain experienced by the electronics, which are fabricated directly on the pillars' surface. In this design configuration, devices based on amorphous indium‐gallium‐zinc‐oxide can withstand different modes of deformation. Bending, stretching, and twisting experiments up to 6 mm radius, 20% uniaxial strain, and 180° global twisting, respectively, are performed to show stable electrical performance of the TFTs. Similarly, a fully integrated digital inverter is tested while stretched up to 20% elongation. As a proof of the versatility of mesa‐shaped geometry, a biocompatible and stretchable sensor for temperature mapping is also realized. Using pectin, which is a temperature‐sensitive material present in plant cells, the response of the sensor shows current modulation from 13 to 28 °C and functionality up to 15% strain. These results demonstrate the performance of highly flexible electronics for a broad variety of applications, including smart skin and health monitoring.