20.500.12556/RUL-182520 Machine learning method for predicting the influence of scanning parameters on random measurement error 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 incident angle, object colour, and scanning distance influence the measurement error and uncertainty when capturing the geometry of the object. With the aim to create a generalized model, which considers the influence of the aforementioned scanning parameters with satisfactory accuracy, a model for predicting the random measurement error based on machine learning is proposed in this study. Data acquired from measurements with varying scanning distances, incident angles, and surface colours were used to train machine learning models. The tested machine learning 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. scanning parameters machine learning random measurement error parametri skeniranja strojno učenje naključna merilna napaka true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-14 14:08:13 2026-05-14 14:08:18 2026-05-15 04:46:16 0000-00-00 00:00:00 2021 0 0 9 str. no. 6 Vol. 32 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85 0957-0233 10.1088/1361-6501/abd57a 49131523 Urbas,_Vlah,_Vukasinovic_-_Machine_learning.pdf Urbas,_Vlah,_Vukasinovic_-_Machine_learning.pdf 1 133728748B1B1E581A2028CA81FFF359 227cee1594467322be96ce61aa40d55c12bc9ecdbcd97032bb95443529da38a7 8c96ad64-4f8d-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233864 https://iopscience.iop.org/article/10.1088/1361-6501/abd57a 1 4abbef92-4f8d-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233862 Fakulteta za strojništvo 0 0 0