Use Case 1: Transfer learning based robust quality inspection (for plastic injection sector) (FARPLAS)

FAR (Turkey) is a company which produces automobile plastic parts using injection moulding technique with more than 1000 product variants.

The scope of the demonstrator is to create an intelligent system, which will be able to communicate with the quality inspection unit and with the operator’s inputs through Human in the Loop (HITL) Dashboards, to automatically adapt the TEAMING.AI Engine to self-adjust the quality inspection model when a deviation from nominal is detected in the case of sufficient trust assurance evidence and otherwise to request human assistance. The goal is to increase the Overall Labor Effectivenss (OLE).

The goal is to use computer vision and advanced deep learning techniques with AI applications for detecting faults on product better/faster than human eye. Through the result of correct classification between non-fault and fault products and the data from production lines, Farplas will have a perfect data set for using AI at setting parameters for injection machines, more reliable and fastest quality control. FAR is a company which produces automobile plastic parts using injection moulding technique with more than 1000 product variants and will deploy the use-case on the production lines for collecting the real data and benchmarking