The study aims to explore the usefulness of existing VR 3D modelling tools for use in mechanical engineering. Previous studies have investigated the use of VR 3D modelling tools in conceptual phases of the product development process. Our objective was to find out if VR tools are useful in creating advanced freeform CAD models that are part of the embodiment design phase in the context of mechanical design science. Two studies were conducted. In the preliminary study, the group of participants modelled a 3D part in a standard desktop CAD application, which provided information about the key characteristics that must be satisfied to obtain a solid model from a surface model. In the research study conducted with a focus group of participants, who were firstly trained in the use of VR, the same part was modelled using a VR headset. The results were analysed and the fulfilment of key characteristics in the use of VR was evaluated. It was found that using VR tools provides a fast way to create complex part geometries, however, it has certain drawbacks. Finally, the ease of use and specific features of the VR technology were discussed.

Injection moulding is currently the most widely employed production method for polymer gears. Current standardised gear metrology methods, which are based on metal gear inspection procedures, do not provide the key information regarding the geometric stability of injection moulded gears and are insufficient for a thorough gear inspection. The study developed novel areal quality parameters, along with a so-called moulding runout quality parameter, with a focus on the injection moulding method. The developed parameters were validated on twenty-nine gear samples, produced in the same moulding tool using various processing parameters. The gears were measured using a high-precision structured-light 3D scanner. The influence of injection moulding process parameters on the introduced novel quality parameters was investigated. The developed moulding runout quality parameter proved to be effective in evaluating the shrinkage that can occur in the injection moulding process. The novel moulding runout parameter returned an average value of −21.8 μm in comparison to 29.4 μm exhibited by the standard parameter on all the gears, where the negative value points directly to mould shrinkages. The rate of cooling was determined to be the most influential factor for the shrinkage of the gear. The developed areal parameters demonstrated to be advantageous in characterising the deviations on the teeth more comprehensively.

Measurements of technical objects can be done with contact and non-contact approaches. Contact methods are accurate but slow. On the other hand, non-contact methods deliver rapid point acquisition and are increasingly being used as their precision mounts. However, multiple scanning parameters such as the incident angle, object colour and scanning distance influence the measurement error and uncertainty when capturing the geometry of the object. With the aim of creating a generalised model that considers the influence of the aforementioned scanning parameters with satisfactory accuracy, a model for predicting the random measurement error based on machine learning (ML) is proposed in this study. Data acquired from measurements with varying scanning distances, incident angles and surface colours were used to train ML models. The tested ML methods included linear regression, support vector machine, neural network, k-nearest neighbour, AdaBoost and random forest. The best-performing trained model was the random forest, with a standard deviation of relative differences of 1.46% for the case of red surfaces, and 5.2% for the case of an arbitrarily coloured surface, which is comparable to results achieved with model-based methods. The trained models and the data are available online.