The problem of generating is analyzed in science and practice in various ways by identifying it once as an element of the machine, and the second time as a part of production, that is, a final product. The nature of the materials of the machine elements, the loads in the contact zone, the relative velocities, the topography of the contact surfaces, and the temperature in the contact zone influence on the tribological characteristics of the elements, and hence on the characteristics of the tribo-mechanical systems. The surfaces of the tribo-mechanical elements of the machines through which mutual contact is realized are essentially thin layers of materials whose composition and properties differ significantly from the properties of the basic mass element material. There are a significant number of tribo-mechanical systems in the energy sector. Gear cutting is the most important operation in the production of gears. The quality of the gear cutting is one of the conditions for achieving the required quality of the work-piece. The gear is an element of a large number of tribo-mechanical systems. The geometrical parameters of the hob milling, the accuracy of the profiling and the accuracy of manufacture significantly affect the productivity and machining costs. In this paper, the topography and roughness parameters of lateral back surfaces of the model hob milling tools are analyzed before and after cylindrical gear cutting.

Considering the importance of segmental duration from a perceptive point of view, the possibility of automatic prediction of natural duration of phones is essential for achieving the naturalness of synthesized speech. In this paper phone duration prediction model for the Serbian language using tree-based machine learning approach is presented. A large speech corpus and a feature set of 21 parameters describing phones and their contexts were used for segmental duration prediction. Phone duration modelling is based on attributes such as the current segment identity, preceding and following segment types, manner of articulation (for consonants) and voicing of neighbouring phones, lexical stress, part-of-speech, word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. These features have been extracted from the large speech database for the Serbian language. The results obtained for the full phoneme set using regression tree, RMSE (root-mean-squared-error) 14.8914 ms, MAE (mean absolute error) 11.1947 ms and correlation coefficient 0.8796 are comparable with those reported in the literature for Czech, Greek, Lithuanian, Korean, Indian languages Hindi and Telugu, Turkish.Index Terms-Decision trees, machine learning algorithms, speech, speech synthesis.

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One of the essential prerequisites for achieving the naturalness of synthesized speech is the possibility of the automatic prediction of phone duration, due to the high importance of segmental duration in speech perception. In this paper we present a new phone duration prediction model for the Serbian language using meta learning approach. Based on the data obtained from the analysis of a large speech database, we used a feature set of 21 parameters describing phones and their contexts. These include attributes related to the segmental identity, manner of articulation (for consonants), attributes related to phonological context, such as segment types and voicing values of neighboring phones, presence or absence of lexical stress, morphological attributes, such as part-of-speech, and prosodic attributes, such as phonological word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. Phone duration model obtained using meta learning algorithm outperformed the best individual model by approximately 2,0% and 1,7% in terms of the relative reduction of the root-meansquared error and the mean absolute error, respectively.