AI-Powered Piston X-ray Image Analysis
This case study presents a project involving the application of an artificial neural network to analyze X-ray images of pistons. The goal of the project was to create a system capable of automatically recognizing correct X-ray images using advanced machine learning tools. By utilizing the Azure platform and its ready-made AI solutions, it was possible to achieve fast and efficient neural network training. The project aligns with Industry 4.0 trends, supporting automation and optimization of production processes.
Challenges:
- Lack of a standardized X-ray image verification process.
- The developed program must be able to recognize correct pistons at a level at least comparable to or higher than the currently used methods.
- Data preparation.
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Benefits:
1. Replacing manual analysis with automation
Transition from manual image analysis by employees to a fully automated system based on an artificial neural network.
2. Adapting the system to different types of pistons
Current solutions are limited to analyzing only one type of piston. The new system is universal and capable of analyzing various types of pistons without limitations.
3. Maintaining quality and speed of analysis
The automatic system provides faster and more accurate X-ray image analysis than manual work. Its performance surpasses human capabilities, which decline after several hours of work.
4. Minimizing human and operational errors
Transitioning to automated analysis eliminated errors related to manual processing, such as fatigue, decreased perception, and operational mistakes.
5. Integration with existing production processes
The new solution is properly integrated with existing processes and technologies used in the enterprise, ensuring operational fluidity and no downtime.
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