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Surface Inspection

Surface Inspection Our customer is a German-based worldwide designing and innovation organization and was established in the last part of the 1800s. Our customer set up their first assembling plant in Quite a while during the mid 1950s. With a turnover of over $3 billion in India and more than 31,000 representatives, they are spread across 10 areas and 7 application improvement focuses surface inspection systems

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Surface Inspection

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  1.   Surface Inspection      Client    Our client is a German-based ​multinational​ ​engineering​ and ​technology​ company and was             founded in the late 1800s. Our client set up their first ​manufacturing​ plant in India during the                               early 1950s. With a turnover of over $3 billion in India and over 31,000 employees, they are                                   spread across 10 locations and 7 application development centres.     

  2.     Problem Faced  Identification of defects on the samples.  ● Manual visualization of the defects was done previously and the client wanted it  ● automated.  Distinguishing between good and bad variants.  ● Technology introduced by Qualitas    Deep Neural Network image processing helps in optimal decision making & precise results. The                             previously used conventional rule-based image processing was a time-consuming task and                       wasn’t reliable when accuracy was asked for. This is how the previously used technology                             worked; the image processing was done by comparing captured images to a master image.                             During the comparison, any difference in captured image pattern above the pre-set threshold                           value resulted in rejection & images with pattern difference falling less than pre-set threshold                             value was accepted.​​But the issue here was with the accuracy and limitations of the application.    Solution     ● The disc was fed to the vision system conveyor (manual feeding).  Proximity type or through-beam laser sensor triggered the camera and captured top &  ● bottom surface images (2D).  Both the images were processed & real-time results were displayed over Qualitas  ● custom-built GUI and the defects were displayed with annotation.  Results (OK/NOT OK) from the top and bottom inspections actuated the flapper &  ● components were separated accordingly. Rejected components with defects on both top  & bottom surfaces, or only on one face were collected in the same bin.     

  3.     Images  Captured Image  Annotated Image        Results  The proposed solution works for all variants.  © Qualitas Technologies Pvt Ltd  53 Kempegowda Double Road, BEML Layout 5th Stage  Rajarajeshwarinagar, Bangalore 560098, INDIA  http://www.qualitastech.com    +91 (80) 4709-1438       

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