System and method for improving assembly line processes
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:
capturing, at an 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 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 a manufacturing process corresponding to the manufacturing instructions 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 manufacturing process.
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 processor; and
a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:
capturing motion data corresponding to operator interaction with a component at an 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 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 operations further comprise:
providing 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 operations further comprise:
providing the manufacturing instructions to an operator at a downstream station of a 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 , evaluation of 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 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 a 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 a production line.
15. A non-transitory computer-readable storage medium comprising executable instructions that, when executed by one or more processors of a computer system, cause the computer system to perform operations comprising:
capturing, at an 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 manufacturing instructions; and
generating, 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 a manufacturing process corresponding to the manufacturing instructions 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 manufacturing process.