How does Ai contribute to quality assurance? 

Zhumanbay Aknur

October 29, 2023

Nowadays AI technologies are playing a crucial role in bettering Quality Assurance as well as industry, innovation and infrastructure. Artificial Intelligence is dramatically altering QA across industries as it adds to sustainability, accuracy and efficiency. 

Many manufacturing industries have a problem with equipment failures that often bring losses and reduce the quality of the product. Fortunately, modern AI technologies and machine learning algorithms can solve this problem. Predictive maintenance analyses collected data and indicates certain patterns, which enables them to foresee possible breakdowns. This reduces possible errors in manufacturing processes and improves efficiency and product quality. Various industries including steel production are implementing such computer vision in the manufacturing process. For example, AI-powered cameras were used to examine the steel coils of Tata steel; this decreased their false positives by 90% and inspection by 60%. 

Maintenance contributes to infrastructure development by providing longevity and reducing downtime. AI can analyse historical maintenance records and real-time data in order to optimise maintenance schedules. This technique makes infrastructure more reliable and reduces operational costs. Railway track maintenance also can be improved by artificial intelligence as it can advance inspection processes. AI can use computer vision techniques to inspect tracks and enable better identification of maintenance requirements. Predictive analytics can help to prevent railway accidents by warning maintenance teams when defects occur. Research conducted by Slawomir Nowaczyk has proved that the efficiency of railway track maintenance can be enhanced by AI technologies.

In conclusion, artificial intelligence improves quality assurance and at the same time contributes to SDG9. By using predictive maintenance it is able to provide a resilient infrastructure and sustainable industrialisation as it prevents any possible accidents or equipment failures.

Reference list

  • Dilmeghani, C. (2023). Quality assurance: guide to automating quality assurance with artificial intelligence
  • Draniceanu, A. Artificial intelligence in software testing
  • Singh, A. (2023). The future of quality assurance in manufacturing: artificial intelligence and machine learning solutions
  • Nowaczyk,S. (2023). Online machine learning-based predictive maintenance for the railway industry