Defect detection in mass production using images
What can be achieved?
Using AI for automated detection of production defects allows for reduced production cost, speed and accuracy.
Traditional product quality testing is slow and inefficient which can lead to production bottlenecks. With human inspection, the accuracy largely depends on each particular inspector and traditional automated systems are both expensive and difficult to implement.
Opportunity for deep learning
A deep learning approach will increase efficiency by allowing quality control to be integrated into a fully automated production line and enable more accurate analysis of the quality of each individual part. The fact that these deep learning models are automated and quick allows them to be implemented in more stages of the production to avoid defective parts from going through additional processes in the production process.
How is the AI model implemented?
Cameras mounted on the production line feed images to the model. Modern production lines can then act upon the prediction made by the model to either remove the part from production or allow it to continue to the next step.
The model is trained on images of manufactured parts, classified either as defective or non-defective.
Test it yourself
Use the app below to test the functionality of a model we built using the Peltarion platform to detect defects in metal castings. We have removed 10 images from the main dataset in the data library. This means that these images have not been seen by the model at all so far. Click here to download the demo images.
Click the icon below to upload an image of the part. Once you have uploaded an image, you can click it again to try another one.
Where to learn more
If you're interested in learning more, have a look at the in-depth article about defect detection. You can also read about Peltarion has been working with the Industrial 3D printing company Amexci to create a proof of concept around quality control in Industrial 3D printing.
Detecting defects is an example of binary Image classification. To learn how to do this for this use case, follow the Defect detection tutorial.
With good input data, deep learning can do a whole lot more than simply identifying defective parts. The technique used in the Car damage assessment tutorial could be used to classify different types of defects and the Skin cancer detection tutorial can be used to identify the locations of a particular condition.
Using Images to detect defects in manufactured parts
In-depth blog post about automated defect detection and the business value of using deep learning in manufacturing.
Detecting defects in mass produced parts
Follow this tutorial to learn how to use the Peltarion platform to build an image classification model to detect faulty parts in a production line.
Customer story /
Bringing AI to Industrial 3D Printing
Peltarion has been working with the Industrial 3D printing company Amexci to create a proof of concept around quality control and now you can be a part of that too.