Challenge:
Our client is a major manufacturer of engine components based out of Chennai, India. They supply parts to several leading automotive companies. With increasing demand and stricter quality norms, clients faced challenges in maintaining consistent quality across all the engine components they produced. Minor defects, if left undetected, led to severe consequences in the performance of the vehicles. Manual inspection was no longer feasible due to the sheer volume and intricacies of the parts.
Implementation of Machine Vision:
We proposed our 4-step approach to solve this problem:
1. Our specialist team visited the site and had a walk through the manufacturing plant and a detailed understanding of the problem and challenges and the reason for each.
2. We designed a custom solution that involved high-resolution cameras to capture detailed images of the parts, which were then annotated as “Defected” and “Non-Defected” for machine learning.
3. We trained our machine vision algorithm based on this dataset for identifying anomalous boundaries. We used a cloud storage system instead of edge computing keeping in mind speed and cost suitable for the requirement.
4. We deployed our solution using cloud infrastructure for real-time monitoring and also built an alert and notification system to alert the authorities in case of any deviance. The solution was able to automatically detect minute cracks, uneven surfaces, or misalignments in the components. Defective parts were flagged, ensuring they didn’t proceed further in the production line.
Feedback loop: The system collected data on the type and frequency of defects. This helped clients identify areas of improvement in their manufacturing process for the next phase of changes.
Key Features:
- Automated Detection: The solution was designed to automatically detect minute cracks, uneven surfaces, or misalignments in the engine components.
- Real-time Feedback: The system was able to give immediate feedback in real-time. Defective parts were flagged and were ensured that they didn’t proceed further in the production line.
- Data Collection: The system collected data on the type and frequency of defects. This helped the client identify areas of improvement in their manufacturing process.
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Benefits:
- Increased Efficiency: The system inspected parts at a much faster rate than human inspectors, allowing the client to increase their production rate.
- Enhanced Accuracy: The system drastically reduced the margin of error in inspections. It detected defects that were often missed in manual inspections.
- Cost Reduction: Though the initial setup was capital-intensive, the system paid off in the long run. Savings were made by reducing the number of defective parts and returns, and by optimizing the production process using the collected data.
- Improved Reputation: Consistently delivering high-quality components bolstered the client’s reputation, leading to increased orders and expanding clientele.
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Conclusion:
The adoption of Machine Vision for industrial parts inspection serves as a compelling testament to the benefits of technology integration in India’s manufacturing sector. Not only did it ensure product quality and enhance production efficiency, but it also provided valuable insights to further refine the manufacturing process.
Tech@Levintutu is the technical team at @Levintutu who writes on the latest technology and its applications in industrial automation. To read more about us, visit our blogs at https://levintutu.com/blog/.
We are really excited about writing more on the application of technology, Artificial Intelligence, industrial automation, AMRs, AGV, ASRS, Industrial IOT, etc.
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