Assembly step time and cycle time determination using vision cameras
Assembly operation's anomaly detection
Mid-term
Designing novel deep learning architectures
Identifying non-value-added activities without explicit training
Improving the current inference architecture
Long-term
Integrating user feedback for model updates
Knowledge transfer of NVA activities
Development of a full-fledged assembly guidance system for AI in factories
Why do we do it?
In industries, up to 40% of the cost and 70% of the production time falls under assembly operations, either in
intermediate assembly operations or in final finished product assemblies. Hence, the end-product
quality and the lead time of a product are hugely impacted
by the quality of the assembly operations
Current Challenges:
Continuous monitoring of human centric assembly operations can be expensive
Current approach of using hand worn sensors can cause safety and privacy concerns
When using the hand-worn sensors, the operators have the authority to ensure that the sensors are always
operational
Any hand-worn or body-worn sensors can be an hassle to the assembly operators
Hence, we propose a non-contact approach of monitoring the assembly operations using sensors like vision etc.
We have developed an assembly monitoring system that can ac the following:
Accurately determine the step and cycle time in real-time using vision cameras
Identify anomalies like sequence break and missed steps when happens
Guide the assembly operators in real-time by identifying the parts and components of the subsequent step
Can detect non-value-added activities reliably without explicit model training i.e., information on the assembly steps is all that is required for the system to function effectively
An application was developed to showcase the system making inference in real-time. See below,