Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

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.

[1]  Krzysztof Okarma,et al.  Quality Assessment of 3D Prints Based on Feature Similarity Metrics , 2016, IP&C.

[2]  Wojciech Matusik,et al.  MultiFab , 2015, ACM Trans. Graph..

[3]  Martin Koch,et al.  Optical Properties of 3D Printable Plastics in the THz Regime and their Application for 3D Printed THz Optics , 2014 .

[4]  Jack Beuth,et al.  Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm , 2018 .

[5]  Shing I. Chang,et al.  Automated Process Monitoring in 3D Printing Using Supervised Machine Learning , 2018 .

[6]  Przemyslaw Mazurek,et al.  Estimation of Geometrical Deformations of 3D Prints Using Local Cross-Correlation and Monte Carlo Sampling , 2017, IP&C.

[7]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[8]  Krzysztof Okarma,et al.  No-reference quality assessment of 3D prints based on the GLCM analysis , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[9]  Krzysztof Okarma,et al.  Color Independent Quality Assessment of 3D Printed Surfaces Based on Image Entropy , 2017, CORES.

[10]  Vedang Chauhan,et al.  A Comparative Study of Machine Vision Based Methods for Fault Detection in an Automated Assembly Machine , 2015 .

[11]  J. Astola,et al.  Image database TID 2013 : Peculiarities , results and perspectives , 2016 .

[12]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[13]  Igor I. Bezukladnikov,et al.  Development of visual inspection systems for 3D printing , 2017, 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus).

[14]  Nikolay N. Ponomarenko,et al.  Image database TID2013: Peculiarities, results and perspectives , 2015, Signal Process. Image Commun..

[15]  Jeremy Straub,et al.  Initial Work on the Characterization of Additive Manufacturing (3D Printing) Using Software Image Analysis , 2015 .

[16]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[17]  Nektarios Georgios Tsoutsos,et al.  Manufacturing and Security Challenges in 3D Printing , 2016 .

[18]  Recommendation Itu-r Bt.601-7 Studio Encoding Parameters of Digital Television for Standard 4:3 and Wide-screen 16:9 Aspect Ratios Bt Series Broadcasting Service (television) , .

[19]  Krzysztof Okarma,et al.  Entropy Based Surface Quality Assessment of 3D Prints , 2017, CSOC.

[20]  Brian Surgenor,et al.  Vision Based Fault Detection of Automated Assembly Equipment , 2011 .

[21]  Mariusz Oszust,et al.  Decision Fusion for Image Quality Assessment using an Optimization Approach , 2016, IEEE Signal Processing Letters.

[22]  Vedang Chauhan,et al.  Fault detection and classification in automated assembly machines using machine vision , 2017 .

[23]  Jeremy Straub,et al.  Alignment issues, correlation techniques and their assessment for a visible light imaging-based 3D printer quality control system , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[24]  Adam Lewis,et al.  In situ process monitoring in selective laser sintering using optical coherence tomography , 2018, Optical engineering.

[25]  Yuan Cheng,et al.  Vision-Based Online Process Control in Manufacturing Applications , 2008, IEEE Transactions on Automation Science and Engineering.

[26]  Xiaodong Li,et al.  In situ real time defect detection of 3D printed parts , 2017 .

[27]  Chris Bailey,et al.  Data driven approach to quality assessment of 3D printed electronic products , 2015, 2015 38th International Spring Seminar on Electronics Technology (ISSE).

[28]  Mohsen A. Jafari,et al.  Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators , 2003, Machine Vision and Applications.

[29]  Krzysztof Okarma,et al.  Application of Structural Similarity Based Metrics for Quality Assessment of 3D Prints , 2016, ICCVG.

[30]  Weisi Lin,et al.  Image Quality Assessment Using Multi-Method Fusion , 2013, IEEE Transactions on Image Processing.

[31]  Jeremy Straub,et al.  Automated testing and quality assurance of 3D printing/3D printed hardware: Assessment for quality assurance and cybersecurity purposes , 2016, 2016 IEEE AUTOTESTCON.

[32]  Mohsen A. Jafari,et al.  Online defect detection in layered manufacturing using process signature , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[33]  Krzysztof Okarma,et al.  Texture Based Quality Assessment of 3D Prints for Different Lighting Conditions , 2016, ICCVG.