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Girish has over 7 years of experience in the field of information technology and information security. He handled the production support and development teams for a major general insurance company based out of UK. He has also assisted several companies in Banking and Financial services sector to enhance their information security procceses.
The customer produces CTC wire as per their customer need. There are 3 variables that changes with each order and production ...
1. Pitch Length of CTC
2. Total CTC Length
3. Total No. of Transpositions
Challenge in Current Process:
1. Manual Error in CTC counts during continuous production.
2. High Production time due to manual stoppage while quality inspection to give breather to inspection person.
3. Pitch length measurement were not done completely and was done on sample basis which resulted in customer rejection.
4. Wastage of copper in case of rejection.
We discussed and analysed customer required and developed a vision system for quality inspection using deep learning algorithm. The developed solution enabled client to have 100% inspection instead of sample base inspection which resulted on only reaching high quality product to their customer. The algorithm did full inspection with 98% accuracy which resulting in substantial saving by eliminating copper wastage that was happening earlier due to defect detection at later stages.