The shift toward lightweight powertrain architectures necessitates a detailed characterization of polymer gears to verify their efficiency and durability. This study investigated the effectiveness of non-contact structured-light 3D scanning for evaluating the surface topography and dimensional tolerance quality of polymer gears produced via distinct manufacturing technologies. A structured-light 3D scanner was used to capture dense point clouds (exceeding 6 million points) of gears produced by three methods: conventional hobbing (POM-C), Material Extrusion (MEX) with carbon fiber reinforcement, and Selective Laser Sintering (SLS). The manufactured parts were compared against the nominal Computer Aided Design (CAD) models to evaluate their geometrical deviations in accordance with DIN 3961 and surface roughness parameters per ISO 25178. The experimental results revealed a consistent ranking of manufacturing quality. The conventionally hobbed POM-C gear exhibited superior precision, achieving DIN quality grades of Q9–Q10 and the smoothest surface finish (Sa = 5.0 µm). Among additive manufacturing techniques, SLS-printed PA 12 showed intermediate quality (Q11, Sa = 12 µm), whereas MEX-printed PPS-CF exhibited significant deviations (exceeding Q12) and the highest surface irregularity (Sa = 25 µm) due to stair-stepping effects. These findings indicate that while additive manufacturing offers geometric flexibility, conventional hobbing retains a decisive advantage in dimensional precision. The optical scanning methodology demonstrated here constitutes an efficient metrological framework for gear quality control, with potential applications extending to the quality assurance of additively manufactured adaptive fixtures and assembly tooling, including automotive assembly operations.

With engineering architecture being shifted to meet the requirements of sustainable development, the need for optimized design solutions places precise engineering methods at the core of the contemporary industrial transition toward data-driven strategies. A timely conversion to lightweight components in drivetrain systems has led to the prominent use of high-strength polymer gears, establishing them as a critical point of interest in the field of power transmission. However, as the conversion to polymer gears relies on expensive and time-consuming laboratory testing, there is a standstill in evaluating the structural properties specific to polymer gear design. In addition, one of the major concerns in the development of polymer-based gear drives is linked with their operational performance and dynamic response under fault conditions influenced by surface wear. To address these difficulties, a framework for surface wear prediction is developed, enabling precise design optimization for specific drivetrain requirements. Computations of wear progression over multiple duty cycles are built upon the mathematical background of Archard’s wear theory, while internal changes in gear contact pressure distribution are constructed on Winkler’s surface model. The framework provides an innovative support for polymer gear systems, as it imports the three-dimensional (3D) scanning data of gear geometry, therefore enabling the analysis of actual flank surfaces with designated surface modifications and manufacturing errors. The framework’s effectiveness, confirmed by experimental validation, demonstrates a superior estimation of contact parameters and overall performance compared to traditional design methods, highlighting scalable solutions that contribute to ongoing industrial engineering objectives.

The neuroanatomy course consistently presents significant challenges, primarily due to the short lifespan of formalin-preserved brain specimens and their restricted availability. Numerous studies have evaluated the strengths and limitations of alternative resources for neuroanatomy education, with a particular focus on technology-based learning methods. This study aimed to assess the effectiveness of integrating 3D printed models into the neuroanatomy curriculum and to evaluate student satisfaction with their use. The experimental group consisted of the first-year students enrolled in the integrated study program at the University of Sarajevo-Veterinary Faculty during the summer semester of the academic year 2022/2023. The course was organized using 3DP models for 15 h, while during the remaining three hours formalin-preserved specimens were utilized. Data obtained from this cohort were compared with the results of the previous two cohorts (2021–2022 and 2020–2021), who studied veterinary anatomy exclusively using formalin-preserved specimens. The neuroanatomy test scores improved in the experimental group compared to the two control groups. The students exhibit positive attitudes and report high satisfaction with using 3DP models. They support innovative teaching methods and find that the colored segments of the models enhance recognizability of different anatomical structures, highlighting the didactical value of 3DP models. Overall, study demonstrated that 3DP models were highly beneficial for neuroanatomy learning, pedagogically useful and well-received by students.

With engineering plastics increasingly replacing traditional materials in various drive and control gear systems across numerous industrial sectors, material selection for any gearwheel critically impacts its mechanical and thermal properties. This paper investigates the engagement of steel and Polyvinylidene Fluoride (PVDF) gear pairs tested under several load conditions to determine polymer gears’ characteristic service life and failure modes. Furthermore, recognizing that the application of polymer gears is limited by insufficient data on their temperature-dependent mechanical properties, this study establishes a correlation between the tribological contact, meshing temperatures, and wear coefficients of PVDF gears. The results demonstrate that the flank surface wear of the PVDF gears is directly proportional to the temperature and load level of the tested gears. Several distinct load-induced failure modes have been detected and categorized into three groups: abrasive wear resulting from the hardness disparity between the engaging surfaces, thermal failure caused by heat accumulation at higher load levels, and tooth fracture occurring due to stiffness changes induced by the compromised tooth cross-section after numerous operating cycles at a specific wear rate.

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of the structure and number of vehicles in Bosnia and Herzegovina in the period from 2010 to 2024, an estimate of the total number of passenger cars, as well as the number of electric vehicles for the period up to 2050, is made. It is estimated that in 2050 the number of electric passenger cars will be around 300,000. For one representative electric passenger car, averaged annual electrical energy consumption is calculated. Based on the calculation and for the estimated number of electric vehicles in use, the total annual consumption of electrical energy for the segment of passenger cars is defined, for different scenarios of increasing the number of electric vehicles. Following the estimated increase in the number of passenger electric cars, an exponential increase in electrical energy consumption is estimated, reaching the annual amount of 635 GWh in 2050, which is 10 times higher than the total electrical energy consumption of the transport sector in 2024. In this way, for the period up to 2050, the additional amount of electrical energy that the electrical power grid should provide, due to the electrification of the vehicle fleet, is estimated.

Computer aided design (CAD) 3D modelling is one of the engineering tasks which is largely routine tasks with a large amount of repetition of the same operations to get from the initial idea for a new product to a 3D model ready for manufacturing. As with all other forms of routine tasks, artificial intelligence (AI) will certainly play a significant role in the future and it will largely automate such jobs. On the other hand, additive manufacturing (AM) can use AI generated CAD 3D models to produce finial product without the need (or with minimal need) for human labour. The combination of these two technologies will certainly shape the future of product design, development and manufacturing. Overview of the current possibilities of using artificial intelligence (AI) and additive manufacturing (AM) in the field of product development, design and manufacturing is presented in this paper. From the point of view of CAD modelling, special attention is given to the so-called "text to 3D model" systems. The challenges, possibilities and further directions of development of these technologies are shown through two real case studies (design, development and manufacturing of two stool chairs). Stool chairs design was generated with the help of "text to 3D model" AI System in a form of 3D models. The generated 3D models were then manufactured with the help of AM.In the last chapter of the paper a comparative analysis of the time spent by human labour for the development, design and manufacturing of this two stool chairs using conventional methods and using AI and AM is carried out

An assessment of different materials for additive manufacturing (AM) of polymer gears is presented in this research. Experimental testing is carried out for three different materials. Two materials are selected as the most common materials used for gears made by additive manufacturing. These materials are nylon and polycarbonate (PC). The third material is IGUS i180, which is a tribological material specially developed for additive manufacturing of parts with demands for high resistance properties such as resistance to friction, wear, and high temperatures which are essential for the long service life of gears. Gears are experimentally tested to determine service life in the form of operating cycles until failure. In addition, the gear temperature is monitored during the experimental testing. Using the value of maximum temperature at the moment of total gear failure at a specific load level enables the categorization of failure type. Different types of gear failures are categorized and presented. Taking into consideration failure type and the service life in the form of operating cycles, the applicability of analyzed materials for specific applications concerning load, speed, and thermal conditions is presented and discussed at the end of the paper. The main goal of this research was to test IGUS i180 material and compare its mechanical and thermal properties with other commonly used materials for gears manufactured by AM, such as nylon (PA6/66) and polycarbonate (PC). IGUS i180 material showed inferior properties concerning gear design in the case of high loads. This research showed that PA6/66 material is still the best solution for polymer gears production using AM, but the applicability of this material, due to temperature constraints, is still quite limited.

With the ever-increasing number of polymer materials and the current number of commercially available materials, the polymer gear design process, regarding the wear lifetime predictions, is a difficult task given that there are very limited data on wear coefficients that can be deployed to evaluate the wear behavior of polymer gears. This study focuses on the classic steel/polymer engagements that result in a wear-induced failure of polymer gears and proposes a simple methodology based on the employment of optical methods that can be used to assess the necessary wear coefficient. Polymer gear testing, performed on an open-loop test rig, along with VDI 2736 guidelines for polymer gear design, serves as a starting point for the detailed analysis of the wear process putting into service a digital microscope that leads to the evaluation of the wear coefficient. The same wear coefficient, as presented within the scope of this study, can be implemented in a rather simple wear prediction model, based on Archard’s wear formulation. The developed model is established on the iterative numerical procedure that accounts for the changes in tooth flank geometry due to wear and investigates the surface wear impact on the contact pressure distribution to completely describe the behavior of polymer gears in different stages of their lifetime. Although a simple one, the developed wear prediction model is sufficient for most engineering applications, as the model prediction and experimental data agree well with each other, and can be utilized to reduce the need to perform time-consuming testing.

Product design and manufacturing leverage 3D scanning for various applications. This study aims to investigate the effectiveness of 3D scanning in furniture production by surveying the literature and showcasing four real-world case studies. The literature review reveals that 3D data acquired from real-world objects have applications in research, rapid prototyping, restoration, and preservation of antique furniture, optimizing CNC machining processes, and measuring furniture components for quality control. The case study descriptions demonstrated the circumstances, rationale, and methodology for 3D scanning. All the case studies analyzed stem from the collaboration between the Laboratory for Product Development and Design at the Faculty of Mechanical Engineering at the University of Sarajevo and various furniture production enterprises from Bosnia and Herzegovina. The conclusions highlight that 3D scanning in the furniture sector is advantageous for developing computer-aided design models from early-stage design prototypes, validating the dimensional accuracy of manufactured components by comparing with CAD models, safeguarding and reconstructing vintage furniture, and remanufacturing formerly produced goods that lack complete technical records (reverse engineering).

The current paper investigates the effects of geometric design parameters on the fatigue failure of the drive axle housing using the Finite Element Method (FEM). The study examines the effects of various factors on the fatigue life of the drive axle housing, such as axle housing wall thickness, housing cross-sectional rounding radius, and rounding radius of the central part of the housing. Based on the known material properties and dynamic loads, a CAD/FEM model of the drive axle housing was developed, and a structural analysis was carried out. Based on the results of the structural analysis, critical places on the housing were determined, and fatigue analysis and lifetime prediction were performed. Through a series of simulations, the study reveals that increasing housing wall thickness can significantly improve fatigue performance. Similarly, increasing the rounding radius at the housing cross-section, as well as the rounding radius at the central part of the housing can also lead to improved fatigue performance. However, the effect of increasing the value of these two radii is not as significant as the effect of the wall thickness. These findings give useful information regarding the design and manufacture of drive axle housings for vehicles, intending to reduce the likelihood of fatigue failure.