Nordbo Robotics’ AI solution for quality control proves itself powerful as Amphenol Phoenix’ daily capacity per worker increases by 1,134.25 %.
did the customer face?
Amphenol Phoenix needed a software solution to improve their quality control processes. This included a wish to improve the stability in terms of the workforce, inspection time, and the quality level.
The previous manual process gave the company following challenges:
- Unstable quality control due to the nature of manual labor.
- Labor shortage as qualified operator recruitment in this field is difficult.
- A dissatisfactory cycle time of manual quality control (20 sec/pc).
- A dissatisfactory capacity of manually inspected parts per shift (2000 pcs/10 hrs).
How did Nordbo Robotics
address these challenges?
To solve these challenges, Nordbo Robotics made use of the powerful software solution for quality inspection, QC. After the initial setup, the AI was taught how to detect, classify, and segment various types of errors and defects. Hereafter, the Phoenix model was integrated into the production line. The AI monitors the operating performance and collects and evaluates data, giving continuous real-time feedback on the quality control process.
Based on deep learning algorithms, Nordbo Robotics’ QC Platform ensured an improvement in Amphenol Phoenix’ quality control process and facilitated the following results:
- Reduced labor shortage as the AI works equivalent to 12 manual laborers which, consequently, reduces labor costs.
- QC generated a decrease in cycle time – from 20 seconds to 3,5 seconds.
- QC improved the defect detection rate and accuracy while increasing the daily capacity from 4800 pcs/laborer to 24,685 pieces – that is a 5x increase in throughput.
- Increased market competitiveness due to the improved level of intelligent automation.