A Comparative Study of Machine Vision Based Methods for Fault Detection in an Automated Assembly Machine

Chauhan, Vedang; Surgenor, Brian

doi:10.1016/j.promfg.2015.09.051

Cited by 55 publications

(26 citation statements)

References 2 publications

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“…The main purpose of the method developed by Szkilnyk et al [8] was the detection of failures in automated assembly machines using the machine vision approach based on processing od images captured by webcams in the LabVIEW environment. Further extensions of this idea were discussed by Chauhan and Surgenor [9], [10].…”

Section: Machine Vision In Additive Manufacturingmentioning

confidence: 98%

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

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Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

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Section: Machine Vision In Additive Manufacturingmentioning

confidence: 98%

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Okarma¹,

Fastowicz²

2019

ELEKTRON ELEKTROTECH

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“…jams, however only those previously defined. A comparison of some other machine vision methods used for fault detection has been provided by Chauhan and Surgenor [8,9], whereas Straub [10] has described the system dedicated to initial image analysis used for comparison of the inprocess object with the final one. However, considering the pixel-by-pixel comparison used by the Author, an accurate calibration of the camera and the printing device is necessary to obtain good results.…”

Section: Overview Of Machine Vision In Monitoring Of 3d Printingmentioning

confidence: 99%

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

Okarma

Fastowicz

2020

Pattern Anal Applic

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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.

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“…Vedang [5] fully describe at least ten documented works dated between 1988 and 2014, he himself presented in 2015 a Comparative study of machine vision-based methods for fault detection in an automated assembly machine [6], that compares three different methods, Gaussian Mixed Models and Blob Analysis, optical flow method and running average method.…”

Section: Related Workmentioning

confidence: 99%

Automated Fault Detection and Diagnostics for Aluminum Threads Using Statistical Computer Vision

Alcántara¹,

Toledo²,

Carrillo³

2019

RCS

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The present work describes the use of statistical computer vision to detect presence-absence of aluminum threads in automotive parts, the vision system is based on a Keyence IV 500 camera and its statistical software. A real case of detection of an industrial aluminum thread was used to demonstrate the effectiveness of the non-invasive machine developed. The results show that using three sequenced tools: brightness adjustment, position reference and area calculation, the repeatability improves by 38%. The study verified the usefulness of the statistical computer vision for fault detection and diagnostics in aluminum threads, a standard statistical analysis of the results presented in the study demonstrates that parts with threads have a punctual performance and, the parts without threads or even without the hole have a statistical normal behavior. The developed method has the ability to automate the correct segregation of good parts with enhanced accuracy avoiding damage to the part, normal in conventional manual methods. The used camera and its brightness compensation demonstrated that environmental light has no effect to the results.

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A Comparative Study of Machine Vision Based Methods for Fault Detection in an Automated Assembly Machine

Cited by 55 publications

References 2 publications

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

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

Automated Fault Detection and Diagnostics for Aluminum Threads Using Statistical Computer Vision

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