Dependence of the surface morphology of ultrathin bismuth films on mica substrates on the film thickness

The results of studying the surface of 15- to 100-nm-thick bismuth films by atomic-force microscopy are reported. The near-linear character of the dependences of the average surface roughness and the average height of growth patterns on the film thickness is established. It is found that the average crystallite size increases, as the film thickness is increased. A slight dependence of the crystallite size on the film thickness is observed at thicknesses in the range of 27–70 nm.     

A case study on the object-oriented framework for modeling product families with the dominant variation of the topology in the one-of-a-kind production

Traditional configuration-based product family modeling techniques do not yield favorable solutions for modeling the product families with the dominant product topology variations. This paper presents the case study for the modeling of variations of the product configuration, parameters, and topologies in mass production of custom windows and doors in a one-of-a-kind production (OKP) company. The case study is based on the object-oriented product family modeling framework we developed for OKP which is characterized by the dominant product variation by topology. The problem of variation in topology in our framework is solved by the manual model transformation in the computer-aided design module which creates a new metamodel of products with the desired topology and which is the basis for a further, automatic model transformation. Usage of our framework has a positive effect on the level of the product customizations and shortening of the leading time which helps OKP companies in obtaining a better position at the market. Our software solution for the designing and manufacturing of custom windows and doors based on our framework is implemented in more than 300 mostly small- and medium-sized enterprises. It proves our concept which can be used for the realization of the information systems for computer-aided one-of-a-kind production in the companies with a different production plan.     

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.     

DESIGN OF SUPERVISORY CONTROL FUNCTIONS BASED ON FEEDFORWARD NEURAL NETWORKS

This paper presents the results of the research concerning possibilities of applying artificial neural networks for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the feedforward neural networks so applied was considered. The emphasis was put on neural network's function as state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of neural networks were analyzed.     

Current stability of multilayer heterogeneous carbon-containing field-emission cathode–grid devices

Stability of current characteristics of a field-emission cell is analyzed. Experimental and simulated results are presented.     

Prediction of traditionally utilised wheat dough technological quality parameters from Mixolab values: development and evaluation of regression models

The research was conducted with the aim to investigate the possibility of Farinograph, Extensograph and Amylograph values prediction with linear and/or multiple Mixolab regression models. In total, 163 flour samples were divided based on Machalanobis distances into three sets: prediction, validation and external sample set. Determination coefficients ranged from 0.145 to 0.640 for linear regression models and from 0.279 to 0.739 for multiple regression models. Internal and external validation of developed regression models was conducted. Testing of developed models resulted in a high share of samples for which predicted values were out of the ranges of method official reproducibility and a high share of samples for which predicted values were out of the quality level range obtained by analytical measurement. It was concluded that it is impossible to develop applicable regression models for prediction of Farinograph, Extensograph and Amylograph parameters on the basis of Mixolab values from standard protocol.