The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

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.

Since 1970, many studies of various laser machining processes and their applications have been published. This paper describes some of our experience in laser surface remelting, consolidating, and hardening of steels. We focus on the problem of robot laser hardening of metals with overlapping of the hardened zone. The process of laser hardening with remelting of the surface layer allows us to very accurately determine the depth of modified layers. In this procedure, we know the exact energy input into the material. Heating above the melting temperature and then rapidly cooling causes microstructural changes in materials, which affect the increase in hardness. We identify the relationship between hardness and width of overlapping. We describe the results of previous work, research, and experience in robot laser hardening of metals. We also show the results of laser processing techniques with the problem of overlapping. Our tests were carried out on materials of DIN standard 1.2379 and 1.7225, and measurements were performed in the hardened zone of overlapping at 2 mm, 3 mm, 4 mm, 6 mm, and 10 mm. We show relationship between hardness and width of overlap for material of DIN standard 1.2379 and 1.7225. The modeling of the relationship was obtained by the 3 layers artificial neural network.

This paper discusses an approach developed for exploiting the local elementary movements of evolution to study complex networks in terms of shared common embedding and, consequently, shared fractal properties. This approach can be useful for the analysis of lung cancer DNA sequences and their properties by using the concepts of graph theory and fractal geometry. The proposed method advances a renewed consideration of network complexity both on local and global scales. Several researchers have illustrated the advantages of fractal mathematics, as well as its applicability to lung cancer research. Nevertheless, many researchers and clinicians continue to be unaware of its potential. Therefore, this paper aims to examine the underlying assumptions of fractals and analyze the fractal dimension and related measurements for possible application to complex networks and, especially, to the lung cancer network. The strict relationship between the lung cancer network properties and the fractal dimension is proved. Results show that the fractal dimension decreases in the lung cancer network while the topological properties of the network increase in the lung cancer network. Finally, statistical and topological significance between the complexity of the network and lung cancer network is shown.

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Visibility is a very important topic in computer graphics and especially in calculations of global illumination. Visibility determination, the process of deciding which surface can be seen from a certain point, has also problematic applications in biomedical engineering. The problem of visibility computation with mathematical tools can be presented as a visibility network. Instead of utilizing a 2D visibility network or graphs whose construction is well known, in this paper, a new method for the construction of 3D visibility graphs will be proposed. Drawing graphs as nodes connected by links in a 3D space is visually compelling but computationally difficult. Thus, the construction of 3D visibility graphs is highly complex and requires professional computers or supercomputers. A new method for optimizing the algorithm visibility network in a 3D space and a new method for quantifying the complexity of a network in DNA pattern recognition in biomedical engineering have been developed. Statistical methods have been used to calculate the topological properties of a visibility graph in pattern recognition. A new n-hyper hybrid method is also used for combining an intelligent neural network system for DNA pattern recognition with the topological properties of visibility networks of a 3D space and for evaluating its prospective use in the prediction of cancer.

The accurate prediction of the mechanical properties of foundry alloys is a rather complex task given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of different intelligent methods of classification, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and of a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that 'light' open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15 %.

Aim of this research was to identify and analyse relative age effect (RAE) on sample composed of young Croatian taekwondo competitors. In order with aim of research, for medal winners (n1=72) and other competitors (n2=187) who competed at Croatian taekwondo cadet championship 2015, date of birth, weight category and sport success were extracted. By conducting of Chi-square test on all competitors (n=259) it is proven there is non-significant difference (χ2=12.28; p=0.34) between expected and observed frequencies according to month of birth. Furthermore, significant difference between observed and expected frequencies according to year of birth of medal winners (χ2=45.31; p<0.01) is confirmed. Results of this research are pointing on presence of RAE which could lead to mistakes in selection of young athletes. Authors are suggesting to minimize allowed age range for competition, or to separate competitors in more age categories, which would enable more equal competitions and reduce effect of age on sport success.