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Bearing Cage Defect Identification

Bearing Vibration Analysis Since their initiation in 1986, with more than 1700 representatives, our customer is the biggest maker of exactness bearing pens in the sorted out division in India regarding limit and activities and among the main Bearing Defects producers of accuracy bearing confines on the planet with a piece of the pie of roughly 5% to 6% in the composed fragment of the worldwide metal, steel, polyamide bearing enclosures as far as income Bearing Problems.

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Bearing Cage Defect Identification

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  1.   Bearing Cage Defect  Identification      Client    Since their inception in 1986, with over 1700 employees, our client is the largest manufacturer  of precision bearing cages in the organized sector in India in terms of capacity and operations  and amongst the leading manufacturers of precision bearing cages in the world with a market  share of approximately 5% to 6% in the organized segment of the global brass, steel, polyamide  bearing cages in terms of revenue.     

  2.         Problem Faced    The inspection of surface defects that could be present on bearing cages and also to  ● classify them as processed and unprocessed based on the presence and absence of  chamfer.    This resulted in the deteriorated quality of the manufactured products because of human  ● involvement. To get rid of the time consumption and achieve better accuracy,  automation is something our client was looking for.  The proposed system was to be installed in a quality assessment room integrated with a  ● rotary bench setup. The diameter of bearing cages varied over a wide range. Achieving a  depth of 80mm with a single camera, lens and fix working distance was another  challenge.  Technology introduced by Qualitas Technologies    AI is a powerful technology that has the capacity to solve a number of industrial problems. DNN,  which is a subset of AI, can be successfully used to train a machine according to user  requirements. Provided a very strong algorithm and vast training set, AI can emerge as the  best-suited technology for inspecting the surface anomalies for quality checking purposes. The  conventional system, which mostly uses a pattern-matching type of algorithm, when deployed in  actual line conditions, fails to deliver 100% accuracy. In an AI-based system, defects are labelled  and trained. Based on training, a threshold value is set for that particular defect. When a new  image is captured and processed, if the extracted feature crosses the preset threshold, it is  identified as a defect.   

  3.       Solution  An AI / DNN based solution typically follows the below steps.    Image acquisition > Image training (includes image segregation) > Model deployment and  testing.    Since the customer had a rotary bench setup, we decided to integrate our system with the same  setup. An area scan camera was placed on the top looking down at the object at an angle. The  acquired image set was labelled and trained for extracting the features. After the system got  trained for all the available inspection datasets, the model was ready for deployment. After  checking the accuracy, fine-tuning was done if needed, to improve accuracy.  Images  Captured Image  Annotated Image         

  4.         Results  2 cameras have been sent, one for inspecting the surface defects and others to inspect the  presence and absence of chamfer.    © 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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