Fault detection and classification in automated assembly machines using machine vision

Chauhan, Vedang; Surgenor, Brian

doi:10.1007/s00170-016-9581-5

Cited by 48 publications

(20 citation statements)

References 40 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: 97%

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: 97%

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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“…A model predictive controller is designed to stabilize the flow process dynamics even with a stiction fault in [17]. An image of subsystems is processed to detect the faults in [18]. In [19], faults in the pneumatic valve are detected and diagnosed using neuro-fuzzy methods.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

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Fault detection and classification in automated assembly machines using machine vision

Cited by 48 publications

References 40 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

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

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