Dynamic training for assembly lines
Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.
1. A method, comprising:
initiating a workflow in a production line, wherein the production line includes a set of operator stations in which a product is manufactured;
generating one or more reference recordings, wherein the one or more reference recordings provide manufacturing instructions at each station of the set of operator stations for manufacture of the product;
capturing, at an operator station of the set of operator stations, motion data corresponding to operator interaction with a component at the operator station;
evaluating the motion data using a comparison model corresponding to an idealized motion profile of the operator station to identify a deviation from the manufacturing instructions; and
generating, based on the deviation from the manufacturing instructions, one or more new instructions to reduce the deviation.
2. The method of claim 1 , wherein the one or more new instructions are provided to at least one operator at the operator station to cause the at least one operator to implement the one or more new instructions to reduce the deviation.
3. The method of claim 1 , wherein evaluation of the motion data using the comparison model is performed using a deep learning model, wherein the deep learning model classifies motion deviations from the comparison model and determines how the workflow is affected as a result of the motion deviations.
4. The method of claim 3 , wherein the deep learning model is trained using feedback from customers on specific products generated via the workflow.
5. The method of claim 1 , wherein the motion data is captured using one or more image capture devices and one or more display devices deployed at the operator station.
6. The method of claim 1 , wherein the one or more new instructions are generated using a deep learning model.
7. The method of claim 1 , wherein the one or more new instructions are deployed to a downstream station from the operator station to cause an operator at the downstream station to implement the one or more new instructions to reduce the deviation.
8. A system, comprising:
a plurality of recording devices disposed at a plurality of operator stations in a production line in which a product is manufactured; and
an assembly instruction system coupled to the plurality of recording devices, wherein the assembly instruction system performs operations comprising:
generating, using the plurality of recording devices, one or more reference recordings, wherein the one or more reference recordings provide manufacturing instructions at each station of the plurality of operator stations for manufacture of the product;
capturing, at an operator station of the plurality of operator stations and using one or more recording devices of the plurality of recording devices associated with the operator station, motion data corresponding to operator interaction with a component at the operator station;
evaluating the motion data using a comparison model corresponding to an idealized motion profile of the operator station to identify a deviation from the manufacturing instructions; and
generating, based on the deviation from the manufacturing instructions, one or more new instructions to reduce the deviation.
9. The system of claim 8 , wherein the assembly instruction system further provides the manufacturing instructions to an operator at the operator station to cause the operator to implement the one or more new instructions to reduce the deviation.
10. The system of claim 8 , wherein the assembly instruction system further provides the manufacturing instructions to an operator at a downstream station of the production line to cause the operator at the downstream station to implement the one or more new instructions to reduce the deviation.
11. The system of claim 8 , wherein the assembly instruction system implements a deep learning model, wherein the deep learning model classifies motion deviations from the comparison model and determines how a workflow is affected as a result of the motion deviations.
12. The system of claim 11 , wherein the deep learning model generates a production quality report based on how the workflow is affected as a result of the motion deviations, the production quality report including the one or more new instructions.
13. The system of claim 11 , wherein the deep learning model analyzes attributes of the product at the operator station to determine an impact of the motion deviations to a quality of the product.
14. The system of claim 11 , wherein the deep learning model is trained using feedback on specific products generated via the production line.
15. A non-transitory computer-readable storage medium comprising executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:
initiate a workflow in a production line, wherein the production line includes a set of operator stations in which a product is manufactured;
generate one or more reference recordings, wherein the one or more reference recordings provide manufacturing instructions at each station of the set of operator stations for manufacture of the product;
capture, at an operator station of the set of operator stations and using one or more recording devices, motion data corresponding to operator interaction with a component at the operator station;
evaluate the motion data using a comparison model corresponding to an idealized motion profile of the operator station to identify a deviation from the manufacturing instructions; and
generate, based on the deviation from the manufacturing instructions, one or more new instructions to reduce the deviation.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to transmit the one or more new instructions to an operator at the operator station to cause the operator to implement the one or more new instructions to reduce the deviation.
17. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to transmit the one or more new instructions to an operator at a downstream operator station from the operator station to cause the operator to implement the one or more new instructions to reduce the deviation.
18. The non-transitory computer-readable storage medium of claim 15 , wherein the one or more new instructions are generated using a deep learning model.
19. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions that cause the computer system to evaluate the motion data further cause the computer system to use a deep learning model to classify motion deviations from the comparison model and determine how the workflow is affected as a result of the motion deviations.
20. The non-transitory computer-readable storage medium of claim 19 , wherein the deep learning model is trained using feedback from customers on specific products generated via the workflow.