Computer Vision Methods for Non-destructive Quality Assessment in Additive Manufacturing

Okarma, Krzysztof; Fastowicz, Jarosław

doi:10.1007/978-3-030-19738-4_2

Cited by 23 publications

(8 citation statements)

References 25 publications

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“…Multiview vision detection was used to detect defects in the 3D printing process by changing the field of view in the printing progress [112]. Future development directions for CV-based 3D printing applications are to developing metrics to obtain a better classification accuracy and to develop benchmark databases to include quality scores [126].…”

Section: Pipelining and Optimizingmentioning

confidence: 99%

Computer Vision Techniques in Manufacturing

Zhou¹,

Zhang²,

Konz³

2022

Preprint

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Computer vision techniques have played an important role in promoting the informatization, digitization and intelligence of industrial manufacturing systems. Considering the rapid development of computer vision techniques, we present a comprehensive review of the state-of-the-art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation and 3D modeling. A system framework of computer vision in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, computer vision algorithms, a decision-making module, and an actuator. Applications of computer vision to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data pre-processing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for non-annotated data processing, developing effective data pre-processing mechanisms, customizing computer vision models, and opportunities aroused by 5G.

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Section: Pipelining and Optimizingmentioning

confidence: 99%

Computer Vision Techniques in Manufacturing

Zhou¹,

Zhang²,

Konz³

2022

Preprint

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“…Non-destructive analyses of finished AM products entail dimensional accuracy and surface finish assessment, internal structure evaluation, and defect detection. Vision [ 13 ], microscopy [ 14 , 15 ], and laser profilometry [ 16 ] are among the methods most frequently used to assess the surface quality of printed products. The examination of the internal structure of 3D prints, including the detection of subsurface defects, is conducted mainly with the use of computed tomography [ 17 , 18 , 19 , 20 ] and ultrasounds [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks

et al. 2021

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In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter.

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“…Image processing has found numerous applications in multimedia and smart education [1][2][3], remote sensing and non-destructive control [4][5][6], etc. It is widely used in various medical applications [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

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Computer Vision Methods for Non-destructive Quality Assessment in Additive Manufacturing

Cited by 23 publications

References 25 publications

Computer Vision Techniques in Manufacturing

Computer Vision Techniques in Manufacturing

Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks

A Fast Method of Visually Lossless Compression of Dental Images

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