Highlight #35
Highlight #35: Progress Report and Technical Updates
Iteration Highlight (Progress Report)
Summary
In this iteration highlight, we provide a summary of the technical work carried out from 26th February to 11th March 2025. Our primary focus has been on integration with the WP07 platform, which is a crucial step towards the successful implementation of our project.
Highlights
- Backend SW Module: We have continued working on the container SW module for online data processing, which is essential for the integration of AI models. This module is a critical component of our system, and we are making steady progress in its development.
- Algorithms for Defect Detection: We have been working on the development of algorithms for defect detection using the data generated up to this point. This is a complex task that requires careful consideration of various factors, and we are making good progress in this area.
- WPI - ZDZW Platform: We have been working on the integration of the TraceabilityGateway PPP with the marketplace, which is a key feature of our system. This involves creating a product at the marketplace, which will enable us to showcase our system's capabilities to potential customers.
Issues and Challenges
During this iteration, we have encountered some issues and challenges that need to be addressed. Some of the key concerns include:
- IR Cameras: We have detected that IR cameras may not be as useful as expected in monitoring the SAW process, which is a critical aspect of our system. This is because the protective atmosphere created using flux in SAW occludes the melt pool and the slag, making it difficult to obtain accurate thermal information. As a mitigation measure, we are considering offering the IR camera in the BTC for potential customers using GMAW or GTAW welding processes. We will also continue to acquire IR images at Pilot 3 testing different camera positions and install pyrometers pointing to the base plate and the slag to get thermal info from those areas.
Functionalities Progress %
The following table provides an update on the progress of various functionalities:
| Functionality | Previous Iteration Progress % | Current Iteration Progress % |
|---|---|---|
| 1 Welding Process monitoring system | 100% | 100% |
| 2 Flexibility: monitoring system covers a great variety of sensors and communication protocols | 100% | 100% |
| 3 Adaptability: monitoring system adaptable to any other metallic welding process | 100% | 100% |
| 4 Scalability: Ease of adding new sensors | 100% | 100% |
| 5 Contribution to the digitization of the industry | 99% | 99% |
| 6 Early detection of defects: reduction in the waste of time and material in rework | 75% | 76% |
| 7 Enables implementation of AI-based quality assurance algorithms | 87% | 86% |
| 8 Set an alarm to stop the process when out of quality limits | 70% | 71% |
| 9 Enables process control | 70% | 71% |
| 10 Provides thermal info from welding bead and base plates | 65% | 65% |
Conclusion
In conclusion, this iteration has been marked by significant progress in various areas, including the development of the backend SW module, algorithms for defect detection, and integration with the WPI - ZDZW platform. However, we have also encountered some challenges, particularly with regards to the use of IR cameras in monitoring the SAW process. We are working on mitigation measures to address these concerns and ensure the successful implementation of our project.
Highlight #35: Progress Report and Technical Updates - Q&A
Frequently Asked Questions
In this Q&A section, we address some of the most common questions related to our project and provide additional information on the progress made during this iteration.
Q: What is the current status of the backend SW module?
A: We have continued working on the container SW module for online data processing, which is essential for the integration of AI models. This module is a critical component of our system, and we are making steady progress in its development.
Q: What are the challenges associated with using IR cameras in monitoring the SAW process?
A: We have detected that IR cameras may not be as useful as expected in monitoring the SAW process, which is a critical aspect of our system. This is because the protective atmosphere created using flux in SAW occludes the melt pool and the slag, making it difficult to obtain accurate thermal information.
Q: What mitigation measures are being taken to address the challenges associated with IR cameras?
A: As a mitigation measure, we are considering offering the IR camera in the BTC for potential customers using GMAW or GTAW welding processes. We will also continue to acquire IR images at Pilot 3 testing different camera positions and install pyrometers pointing to the base plate and the slag to get thermal info from those areas.
Q: What is the current progress on the development of algorithms for defect detection?
A: We have been working on the development of algorithms for defect detection using the data generated up to this point. This is a complex task that requires careful consideration of various factors, and we are making good progress in this area.
Q: What is the current status of the integration with the WPI - ZDZW platform?
A: We have been working on the integration of the TraceabilityGateway PPP with the marketplace, which is a key feature of our system. This involves creating a product at the marketplace, which will enable us to showcase our system's capabilities to potential customers.
Q: What is the current progress on the various functionalities?
A: The following table provides an update on the progress of various functionalities:
| Functionality | Previous Iteration Progress % | Current Iteration Progress % |
|---|---|---|
| 1 Welding Process monitoring system | 100% | 100% |
| 2 Flexibility: monitoring system covers a great variety of sensors and communication protocols | 100% | 100% |
| 3 Adaptability: monitoring system adaptable to any other metallic welding process | 100% | 100% |
| 4 Scalability: Ease of adding new sensors | 100% | 100% |
| 5 Contribution to the digitization of the industry | 99% | 99% |
| 6 Early detection of defects: reduction in the waste of time and material in rework | 75% | 76% |
| 7 Enables implementation of AI-based quality assurance algorithms | 87% | 86% |
| 8 Set an alarm to stop the process when out of quality limits | 70% | 71% |
| 9 Enables process control | 70% | 71% |
| 10 Provides thermal info from welding bead and base plates | 65% | 65% |
Conclusion
In conclusion, this Q&A section provides additional information on the progress made during this iteration and addresses some of the most common questions related to our project. We hope this information is helpful in understanding the current status of our project and the challenges we are facing.