# A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces
## 1 Introduction
Nevertheless, although there exist some 3D printing-monitoring solutions based on the use of cameras, there is still a need for improvement of existing surface quality-assessment methods for 3D prints to achieve better correlation with subjective opinions. In a recent paper [4], an overview of the current state-of-the-art of the quality control for additive manufacturing has been presented, with a particular emphasis on video feedback and 3D scanning. Its authors indicate the need for quality control for robust and reliable 3D printing due to the lack of a comprehensive solution and the need for further work. A promising direction of research is the application of Convolutional Neural Networks (CNNs) for such purposes, e.g., for the calibration of Fused Deposition Modeling 3D printers, which has been examined in a recent paper [5]. The applied network has demonstrated its usefulness for the detection of various types of defects, including cracks [6], stringing [7], as well as under-extrusion and over-extrusion [8], which had been detected separately in previously presented solutions. Another advantage of this approach is its ability to detect and quantify the degree of warp deformations [9; 10]. Another application of neural networks, related to printing error detection and correction using a multi-camera machine vision system, is presented in the paper [11], where a large number of images of the nozzle tip and material deposition captured at various production stages were used to train the network. A complicated measurement system designed for dimension accuracy verification was proposed by [PERSON] et al. [12] that is based on a depth sensor and a precise servomechanism. Nevertheless, precise control of the dimensions of the manufactured object requires the use of dedicated software, a programmable logic controller (PLC), and a servomotor. [PERSON] et al. [13] have proposed an online surface quality-monitoring system based on the analysis of the first and the last layer of the 3D printed samples using a laser profiler. A quite similar approach, however, utilizing micro-lidar, is applied, e.g., in Bambu Lab X1C and X1E devices, where the printing correctness and quality are checked for the first layer. The micro-lidar is also used for the calibration of some other important parameters, such as pressure advance and filament flow. An original idea of using computer vision for the quality evaluation of 3D printed concrete structures has been proposed in the paper [14]. Similar to earlier papers, e.g., ref. [15] where the plastic filaments have been examined, its authors assumed that higher entropy variation characterizes lower-quality concrete surfaces. Nevertheless, in the approach proposed for concrete structures, Local Binary Patterns (LPBs) were additionally applied for texture analysis. Although the proposed methodology seems promising, its practical implementation and further verification are considered one of the challenges for further research. The authors of the paper [16] used simple and popular methods, such as color difference calculated for CIELAB color space, Canny edge detector, Hu moments, and median filtering, for quality control of 3D printed parts, achieving an overall accuracy of 86.5%. As stated by the authors, the developed system has some limitations, e.g., related to its sensitivity to lighting conditions and reflections, the necessity of tuning the filter size, as well as some other parameters such as binarization thresholds. A novel idea of the application of the linear regression, support vector regression (SVR), and long short term memory (LSTM) network for the analysis of the filament flow data has been presented by [PERSON] et al. [17]. This approach is based on using both scans of the printed flat elements as well as time series from the in-process sensor measuring the filament flow. The method was verified using a dataset of 73 flat samples, similar to the database used in this paper.