A reliable automatic visual quality assessment of 3D-printed surfaces is one of the key issues related to computer and machine vision in the Industry 4.0 era. The colour-independent method based on image entropy proposed in the paper makes it possible to detect and identify some typical problems visible on the surfaces of objects obtained by additive manufacturing. Depending on the quality factor, some of such 3D printing failures may be corrected during the printing process or the operation can be aborted to save time and filament. Since the surface quality of 3D-printed objects may be related to some mechanical or physical properties of obtained objects, its fast and reliable evaluation may also be helpful during the quality monitoring procedures. The method presented in the paper utilizes the assumption of the increase of image entropy for irregularly distorted 3D-printed surfaces. Nevertheless, because of the local nature of distortions, the direct application of the global entropy does not lead to satisfactory results of automatic surface quality assessment. Therefore, the extended method, based on the combination of the local image entropy and its variance with additional colour adjustment, is proposed in the paper, leading to the proper classification of 78 samples used during the experimental verification of the proposed approach.

中文翻译：

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基于图像熵的增材制造彩色表面质量评估得到改善

对3D打印表面进行可靠的自动视觉质量评估是工业4.0时代与计算机和机器视觉相关的关键问题之一。本文提出的基于图像熵的颜色独立方法使检测和识别在增材制造中获得的物体表面可见的一些典型问题成为可能。根据质量因素，某些此类3D打印故障可能会在打印过程中得到纠正，或者可以中止该操作以节省时间和耗材。由于3D打印对象的表面质量可能与获得的对象的某些机械或物理特性有关，因此在质量监控过程中对其进行快速可靠的评估也可能会有所帮助。本文提出的方法利用了不规则变形的3D打印表面的图像熵增加的假设。然而，由于变形的局部性质，全局熵的直接应用不能产生令人满意的自动表面质量评估的结果。因此，本文提出了一种基于局部图像熵及其方差与附加色彩调整相结合的扩展方法，从而在对方法进行实验验证时对78个样本进行了正确分类。