Quality Control
[wtm_use_case_cats]
Parts manufacturers can capture images of each component as it comes off the assembly line, and automatically run those images through a machine learning model to identify any flaws. Highly-accurate anomaly detection algorithms can detect issues down to a fraction of a millimeter. Predictive analytics can be used to evaluate whether a flawed part can be reworked or needs to be scrapped. Eliminating or re-working faulty parts at this point is far less costly than discovering and having to fix them later. It saves on more expensive issues down the line in manufacturing and reduces the risk of costly recalls. It also helps ensure customer safety, satisfaction and retention.
Benefits for the company
Machine learning algorithms that can quickly detect and eliminate faulty parts before they get into the vehicle manufacturing workflow.
Feasability
High
Type of expertise/ AI domain
Image recognition and anomaly detection
Internal data required
Tagged Images with issues, Data for Anomaly trends in production line