Founded in 1991 in Lviv, today ELEKS is a trusted Top 100 Global Outsourcing company that provides full-cycle software engineering outsourcing services, from ideation to finished products.
For over 30 years, the tech giant has been working as a software innovation partner with Fortune 500 companies, big enterprises, and technology challengers.
What is the challenge?
To enhance manufacturing productivity, BADER has a requirement to upgrade the quality control of leather production at a high level. So the ELEKS technical team started to automate the process of anomaly detection in the manufacturing process by implementing ML algorithms. Moreover, at the very start of the project, there were no data to work with, so ELEKS team conducted also the process of Data Collection and Data Labelling for further models training.
What is the solution?
First of all, the engineering team researched the existing production process and implemented the technical solutions within it. The second step was to provide a flexible data model for verification, validation, and quality control of produced products, including search and reviewing results.
As a result – a model ensemble that can identify fastening details on the inner side of a seat and distinguish whether a defect is present. ELEKS team used Mask-RCNN + CNN stack for this purpose. To
- In the first stage, the Mask-RCNN model is trained to localize fastening details.
- In the second stage, the detected binary mask is passed to CNN trained on identifying a defect (binary: Defect or Non-Defect)
What are the final results?
After development and testing, ELEKS represented the integrated solution for BADER for each production line with defects controlling. Thanks to high-level expertise and cooperation with the client, the tech team provided:
- Automatic defect detection during the manufacturing process
- Decreased human QA intervention
- Increased the manufacturing capacity by improving the QA process
«Working with Bader was a unique opportunity to apply Machine Learning to a real production process. This project is a really good example to show people how intelligent compound can save both time and costs for manufacturers by applying Machine Learning solutions», Yurii Malna, Data Scientist, ELEKS