AI Improves Quality in Manufacturing
18 Aug 2020
The client is a start-up, helping industry customers identify and manage manufacturing defects. They had developed a data set and models using traditional computer vision techniques to identify and classify defects. They brought the XENON team in to see how they could improve their capabilities.
Data labelling was being done in-house and was a very expensive and time consuming process. The labelling process is also very subjective, and often labels for the same data would vary depending on who was classifying the defect image. The XENON team was able to implement a process for external, professional data labellers to take over this task, which resulted in lower costs and increased consistency.
Defect data is often very rare, so we also implemented new deep learning models that can be trained while working within the constraints of limited example data. The deep learning models were also trained to deal with inconsistencies and natural variations in defects, developing a robust response to variations with increased accuracy and greater richness in reporting data.
This has developed into a long-term engagement, where we form a permanent part of the client’s team. And the better identification of defects has resulted in fewer defects overall. This has reduced wastage, increased material efficiency, and lifted overall quality in the manufacturing process which also has benefited customers’ experience of the product.
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