Problem - Inaccuracy in defect detect... The danger of getting a cut... speed of the production is high and the minimum defect size...
CLIENT BACKGROUND
Our client is an ISO 9001-2008 company certified by TUV Nord, engaged in offering innovative automation solutions in the engineering field for the last 8 years. They have experienced amazing business growth exceeding more than 70% year on year, through its innovative & cost-effective salutations in various products and focus on complete customer satisfaction with competitive cost & excellent service.
PROBLEM
- Inaccuracy in defect identification on the edge surface of the glasses (after glass cutting)
- The danger of getting a cut in manual inspection
- Speed of the production is high and the minimum defect size is 0.5mm causing many glasses to be uninspected
PROBLEM IMPLICATIONS
- Irregularities in the edge surface of the mirror do not let it sit on the frame properly
- The quality standards do not meet and affect the customer experience badly
CLIENT REQUIREMENTS
- To automate the inspection of the mirrors and automatically reject the defective ones with the help of machine vision technology
- To achieve higher accuracy for defect identification in order to reduce or eliminate the false acceptance rate
- To identify the defects like chip-offs, scratches, etc
CURRENT PROCESS
The process is being done manually which is a very time-consuming process requiring a lot of labor and there is a significant scope of error as well.
The mirror glass is guided to the inspection station on a conveyor, where there will be a camera placed above the mirror glass with proper illumination to avoid reflection in the captured images.
- AI-based segmentation technique is used to identify the surface of the glass for defects like chips, scratches, etc.
CONCLUSION
With the help of an AI-based machine vision system, the following potential benefits could be observed –
- Can achieve an accuracy of close to 99 percent to identify defects with missing screws.
