Endpoint detection in manufacturing process by near infrared spectroscopy and machine learning techniques

1. A device, comprising:

one or more processors to:

receive training spectral data associated with a manufacturing process that transitions from an unsteady state to a steady state;

generate, based on the training spectral data, a plurality of iterations of a support vector machine (SVM) classification model until a time, associated with a set of steady state data used to generate an iteration of the plurality of iterations of the SVM classification model, is a threshold amount of time away from a time at which the manufacturing process is known to be in the unsteady state;

determine, based on the plurality of iterations of the SVM classification model, a plurality of predicted transition times associated with the manufacturing process,

a predicted transition time, of the plurality of predicted transition times, identifying a time, during the manufacturing process, that the iteration predicts that the manufacturing process transitioned from the unsteady state to the steady state; and

generate, based on the plurality of predicted transition times, a final SVM classification model associated with determining whether the manufacturing process has reached the steady state.

2. The device of claim 1 , where the one or more processors are further to:

receive additional spectral data associated with the manufacturing process; and

determine, based on the final SVM classification model and the additional spectral data, that the manufacturing process has not reached the steady state.

3. The device of claim 1 , where the one or more processors are further to:

receive additional spectral data associated with the manufacturing process; and

determine, based on the final SVM classification model and the additional spectral data, that the manufacturing process has reached the steady state.

4. The device of claim 3 , where the one or more processors are further to:

provide an indication that the manufacturing process has reached the steady state.

5. The device of claim 3 , where the one or more processors are further to:

determine, based on determining that the manufacturing process has reached the steady state, a quantitative metric associated with the steady state; and

provide information associated with the quantitative metric.

6. The device of claim 1 , where the one or more processors, when generating the plurality of iterations of the SVM classification model, are to:

create, based on the training spectral data, a set of unsteady state data and the set of steady state data; and

generate the iteration based on the set of unsteady state data and the set of steady state data.

7. The device of claim 1 , where the one or more processors are further to:

perform a dimension reduction, based on the training spectral data, before generating the plurality of iterations of the SVM classification model.

8. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to:

receive training spectral data associated with a first performance of a manufacturing process that transitions from an unsteady state to a steady state;

determine, based on the training spectral data, a plurality of iterations of a support vector machine (SVM) classification model until a time, associated with a set of steady data, is a threshold amount of time away from a time at which the first performance of the manufacturing process is known to be in the unsteady state;

generate, based on the plurality of iterations of the SVM classification model, a final support vector machine (SVM) classification model associated with determining whether another performance of the manufacturing process has transitioned from the unsteady state to the steady state;

receive additional spectral data associated with a second performance of the manufacturing process; and

determine, based on the final SVM classification model and the additional spectral data, whether the second performance of the manufacturing process has transitioned from the unsteady state to the steady state.

9. The non-transitory computer-readable medium of claim 8 , where the one or more instructions that cause the one or more processors to determine whether the second performance of the manufacturing process has transitioned from the unsteady state to the steady state, cause the one or more processors to:

determine that the second performance of the manufacturing process has transitioned from the unsteady state to the steady state; and

provide an indication that the second performance of the manufacturing process has transitioned from the unsteady state to the steady state.

10. The non-transitory computer-readable medium of claim 9 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine, based on determining that the second performance of the manufacturing process has transitioned from the unsteady state to the steady state, a quantitative metric associated with the second performance of the manufacturing process; and

provide information associated with the quantitative metric.

11. The non-transitory computer-readable medium of claim 10 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

identify a regression model associated with the quantitative metric; and

where the one or more instructions, that cause the one or more processors to determine the quantitative metric, cause the one or more processors to:

determine the quantitative metric based on the regression model.

12. The non-transitory computer-readable medium of claim 8 , where the one or more instructions further cause the one or more processors to:

create, based on the training spectral data, a first set of unsteady state data and a first set of steady state data;

generate a first iteration of the SVM classification model based on the first set of unsteady state data and the first set of steady state data;

create, based on the training spectral data, a second set of unsteady state data and a second set of steady state data; and

generate a second iteration of the SVM classification model based on the second set of unsteady state data and the second set of steady state data.

13. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine, based on the first iteration of the SVM classification model, a predicted transition time associated with the first iteration of the SVM classification model; and

where the one or more instructions that cause the one or more processors to create the second set of unsteady state data, cause the one or more processors to:

create the second set of unsteady state data based on the predicted transition time associated with the first iteration of the SVM classification model.

14. A method, comprising:

receiving, by a device, first spectral data associated with a first performance of a manufacturing process that transitions from an unsteady state to a steady state;

generating, by the device and based on the first spectral data, a plurality of iterations of a support vector machine (SVM) classification model;

determining, by the device and based on the plurality of iterations of the SVM classification model, a plurality of predicted transition times, associated with the first performance of the manufacturing process, until a time, associated with a set of steady data used to generate an iteration of the plurality of iterations of the SVM classification model, satisfies a threshold time associated with a time at which the manufacturing process is known to be in the unsteady state;

generating, by the device and based on the plurality of predicted transition times, a final SVM classification model associated with determining whether another performance of the manufacturing process has reached the steady state;

receiving, by the device, second spectral data associated with a second performance of the manufacturing process; and

determining, by the device, whether the second performance of the manufacturing process has reached the steady state based on the final SVM classification model and the second spectral data.

15. The method of claim 14 , where determining whether the second performance of the manufacturing process has reached the steady state comprises:

determining that the second performance of the manufacturing process has not reached the steady state.

16. The method of claim 14 , where determining whether the second performance of the manufacturing process has reached the steady state comprises:

determining that the second performance of the manufacturing process has reached the steady state.

17. The method of claim 16 , further comprising:

providing an indication that the manufacturing process has reached the steady state,

where providing the indication causes an action, associated with the manufacturing process, to be automatically performed.

18. The method of claim 16 , further comprising:

determining, based on a regression model associated with the manufacturing process, a quantitative metric associated with the steady state; and

providing information associated with the quantitative metric.

19. The method of claim 14 , further comprising:

performing a dimension reduction, associated with the first spectral data, before generating the plurality of iterations of the SVM classification model.

20. The device of claim 1 , wherein the one or more processors are further to:

determine, based on the plurality of predicted transition times, a dominant transition time,

wherein the final SVM classification model is generated based on determining dominant transition time.