@Article{ApplSci2020JFPLVL,
  author   = {Krzysztof Okarma and Jaros\{l}aw Fastowicz and Piotr Lech and Vladimir Lukin},
  journal  = {Applied Sciences},
  title    = {Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation},
  year     = {2020},
  note     = {Article no. 6248},
  number   = {18},
  volume   = {10},
  abstract = {Quality assessment of the 3D printed surfaces is one of the crucial issues related to fast prototyping and manufacturing of individual parts and objects using the fused deposition modeling, especially in small series production. As some corrections of minor defects may be conducted during the printing process or just after the manufacturing, an automatic quality assessment of object's surfaces is highly demanded, preferably well correlated with subjective quality perception, considering aesthetic aspects. On the other hand, the presence of some greater and more dense distortions may indicate a reduced mechanical strength. In such cases, the manufacturing process should be interrupted to save time, energy, and the filament. This paper focuses on the possibility of using some general-purpose full-reference image quality assessment methods for the quality assessment of the 3D printed surfaces. As the direct application of an individual (elementary) metric does not provide high correlation with the subjective perception of surface quality, some modifications of similarity-based methods have been proposed utilizing the calculation of the average mutual similarity, making it possible to use full-reference metrics without the perfect quality reference images, as well as the combination of individual metrics, leading to a significant increase of correlation with subjective scores calculated for a specially prepared dataset.},
  doi      = {10.3390/app10186248},
  keywords = {additive manufacturing; 3D prints; surface quality assessment; machine vision; image analysis; combined metrics; structural similarity},
}