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One of the most time-consuming aspects of creating 3D virtual models is the generation of geometric models of objects, in particular if the virtual model is derived (digitized) from a physical version of the object. A variety of commercially available technologies can be used to digitize objects at the molecular scale but also multi-storey buildings or even planets and stars. The process of 3D digitizing basically consists of a sensing phase followed by a rebuild phase. The sensing phase collects or captures raw data and generates initial geometry data, usually as a 2D boundary object, or a 3D point cloud. Sensing technologies are based on tracking, imaging, and range finding or their combination. The rebuild phase is internal processing of data into conventional 3D CAD and animation geometry data, such as NURBS and polygon sets. Finally, in most cases, the digitized objects must be refined by using the CAD software to gain CAD models of optimal quality which are needed in the downstream processes. Leading CAD software packages include special modules for such tasks. Many commercial vendors offer sensors, software and/or complete integrated systems. Reverse engineering focuses not only on the reconstruction of the shape and fit, but also on the reconstruction of physical properties of materials and manufacturing processes. Reverse engineering methods are

In recent years, in addition to laboratory tests to determine emissions from road motor vehicles, tests in real driving conditions RDE (Real Driving Emissions) with the use of portable measuring equipment PEMS (Portable Emissions Measurement System) have been conducted. The paper presents the results of the emission research conducted on a vehicle Dacia Duster 1.0 TCe 100 ECO-G fuelled by petrol Eurosuper 95 and liquefied petroleum gas (LPG). The aim of the research was to determine the emissions of harmful substances and carbon dioxide, i.e. fuel consumption, in accordance with the prescribed RDE procedure and their comparison for the two types of motor vehicle fuel. Results clearly showed that the method used can differentiate between fuel types. The results correspond to the RDE emissions public data, which secures that the methodology used is in accordance with the procedure for measuring emissions in real driving conditions.

Original scientific paper In order to improve conceptual phase of vehicle development, this research is focused on development of new multi-objective optimization model for determining the optimal parameters of the suspension system. In this research emphasis is on the development of suspension system from the viewpoint of full vehicle dynamics behaviour. The new optimization model consists of the integration of fast simulation tools with a suitable degree of accuracy for analysis of suspension system kinematics and analysis of vehicle dynamics into multi-objective optimization environment. The necessary steps that proceed to development of optimization model are identification of influence parameters, definition of criteria for the evaluation of vehicle dynamic characteristics in different test procedures and selection of multi-objective optimization algorithms, primarily contemporary evolutionary algorithms. In comparison of the algorithms, the best results in terms of convergence, number of solutions, short computing time and Pareto front approximation were achieved with the FMOGA-II algorithm. Keywords: evolutionary algorithms; multi-objective optimization; suspension system parameters; vehicle dynamics Višekriterijski optimizacijski model u razvoju ovjesa vozilaIzvorni znanstveni članak U cilju unapređenja konceptualne faze razvoja vozila, ovo istraživanje je usmjereno na razvoj novog višekriterijskog optimizacijskog modela za određivanje optimalnih parametara ovjesa vozila. U ovom istraživanju naglasak je na razvoju ovjesa vozila promatrano kroz dinamičko ponašanje kompletnog vozila. Novi optimizacijski model temelji se na integraciji brzih simulacijskih alata s zadovoljavajućom razinom točnosti za analizu kinematike ovjesa i dinamiku vozila unutar okruženja za višekriterijsko optimiranje. Nužni koraci koji prethode razvoju optimizacijskog modela su identifikacija utjecajnih parametara, definiranje kriterija za ocjenu dinamičkih karakteristika vozila u različitim ispitnim procedurama i odabir višekriterijskih optimizacijskih algoritama, prvenstveno suvremenih evolucijskih algoritama. Usporedba optimizacijskih algoritama pokazala je da se najbolji rezultati u pogledu konvergencije, broja mogućih rješenja, trajanja računanja i približavanja Pareto fronti postižu s FMOGA-II algoritmom. Ključne riječi: dinamika vozila; evolucijski algoritmi; parametri ovjesa vozila; višekriterijsko optimiranje

Greenhouse emissions and air pollutants pose a global threat to the environment and human health. Emission inventories are a valuable tool in understanding emission sources and their overall impact on the environment. Most cities and countries do not include non-road mobile machinery (NRMM) when compiling emission inventories. Furthermore, little effort has been made to understand better the impact of this source of emissions on the environment. For these reasons, this research examines the data from the existing NRMM emission inventories and other studies concerning NRMM emissions. After careful literature review, three main problems in creating a national NRMM emission inventory are identified and reviewed: lack of a comprehensive list of NRMM and their activity data, lack of emission factor data, and lack of research. The data from the existing inventories show that compared to some emissions, NRMM has a three times larger proportion of emissions compared to the proportion of energy consumption. Furthermore, there are significant differences in total emissions when comparing the same pollutants among different countries. A general lack of data is the common denominator for all these problems and can only be solved by creating national NRMM databases operated by a designated institution. This institution must be able to annually update relevant NRMM data through questionnaires and experimental research on the existing NRMM.