Defect detection for multi-function devices using machine learning
A method is disclosed. For example, the method executed by a processor of a multi-function device (MFD) includes executing a defect learning routine to identify defects, cataloging the defects based on a job function, a type of paper, and a machine state, receiving a job request, determining a known defect that has been catalogued based on the job function, the type of paper, and the machine state, and presenting a visualization of the known defect on a display of a user interface before executing the job request.
1. A method of operating a device, comprising:
receiving, by a processor associated with the device, a job request;
determining, by the processor, a known defect that has been catalogued as part of a defect learning routine to identify defects, wherein the know defect is based on at least one of: a job function, a type of paper, and a machine state; and
presenting, by the processor, a visualization of the known defect on a display of a user interface before executing the job request.
2. The method of claim 1 , wherein the defect learning routine comprises a diagnostic routine to identify a source of the defects by performing different job functions on different types of a paper and different rotations of the paper.
3. The method of claim 2 , wherein the diagnostic routine comprises:
generating, by the processor, a print out of an original image to be printed on a first type of paper;
generating, by the processor, a first scanned image of the print out;
generating, by the processor, a copy of an inverse of the print out on a second type of paper;
generating, by the processor, a second scanned image of the copy; and
analyzing, by the processor, defects on the print out, the first scanned image, the copy, and the second scanned image to identify defects caused by the job function and the type of paper.
4. The method of claim 3 , wherein the analyzing comprises:
comparing, by the processor, the original image to the first scanned image to identify defects generated by a print function, a scan function and the first type of paper;
comparing, by the processor, the first scanned image to the second scanned image to identify defects generated by the first type of paper, the second type of paper, the print function, and the scan function;
identifying, by the processor, a first set of defects from the defects that are flipped as a first type of defect associated with the scan function;
identifying, by the processor, a second set of defects from the defects that are mirrored as a second type of defect associated with the print function;
identifying, by the processor, a third set of defects from the defects that stayed as a third type of defect associated with the first type of paper; and
identifying, by the processor, a fourth set of defects from the defects that are new as a fourth type of defect associated with the second type of paper.
5. The method of claim 1 , wherein the visualization illustrates the known defect and presents an option for a user to continue with the job request.
6. The method of claim 1 , wherein the visualization comprises an option for a user to fix the known defect with a maintenance operation executed by the device.
7. The method of claim 6 , wherein the maintenance operation identifies components that are associated with the job request that can be cleaned, performs a cleaning operation, and resets a machine state associated with the components after the cleaning operation is performed.
8. The method of claim 6 , wherein the maintenance operation identifies a component associated with the job request that is to be replaced.
9. The method of claim 6 , wherein the maintenance operation identifies the type of paper selected for the job request that is associated with the known defect and notifies a user via the visualization to replace the type of paper for the job request.
10. The method of claim 1 , wherein the visualization identifies which maintenance operations will fix which known defects.
11. A non-transitory computer-readable medium storing a plurality of instructions, which when executed by a processor of a device, causes the processor to perform operations comprising:
receiving a job request;
determining a known defect that has been catalogued as part of a defect learning routine to identify defects, wherein the know defect is based on at least one of: a job function, a type of paper, and a machine state; and
presenting a visualization of the known defect on a display of a user interface before executing the job request.
12. The non-transitory computer-readable medium of claim 11 , wherein the defect learning routine comprises a diagnostic routine to identify a source of the defects by performing different job functions on different types of a paper and different rotations of the paper.
13. The non-transitory computer-readable medium of claim 12 , wherein the diagnostic routine comprises:
generating a print out of an original image to be printed on a first type of paper;
generating a first scanned image of the print out;
generating a copy of an inverse of the print out on a second type of paper;
generating a second scanned image of the copy; and
analyzing defects on the print out, the first scanned image, the copy, and the second scanned image to identify defects caused by the job function and the type of paper.
14. The non-transitory computer-readable medium of claim 13 , wherein the analyzing comprises:
comparing the original image to the first scanned image to identify defects generated by a print function, a scan function and the first type of paper;
comparing the first scanned image to the second scanned image to identify defects generated by the first type of paper, the second type of paper, the print function, and the scan function;
identifying a first set of defects from the defects that are flipped as a first type of defect associated with the scan function;
identifying a second set of defects from the defects that are mirrored as a second type of defect associated with the print function;
identifying a third set of defects from the defects that stayed as a third type of defect associated with the first type of paper; and
identifying a fourth set of defects from the defects that are new as a fourth type of defect associated with the second type of paper.
15. The non-transitory computer-readable medium of claim 11 , wherein the visualization illustrates the known defect and presents an option for a user to continue with the job request.
16. The non-transitory computer-readable medium of claim 11 , wherein the visualization comprises an option for a user to fix the known defect with a maintenance operation executed by the device.
17. The non-transitory computer-readable medium of claim 16 , wherein the maintenance operation identifies components that are associated with the job request that can be cleaned, performs a cleaning operation, and resets a machine state associated with the components after the cleaning operation is performed.
18. The non-transitory computer-readable medium of claim 16 , wherein the maintenance operation identifies a component associated with the job request that is to be replaced.
19. The non-transitory computer-readable medium of claim 11 , wherein the visualization identifies which maintenance operations will fix which known defects.
20. A method of operating a device, comprising:
receiving, by a processor, associated with the device, a job request;
determining, by the processor, a known defect that has been catalogued as part of a diagnostic routine and a maintenance routine to identify defects, wherein the know defect is based on at least one of: a job function, a type of paper, and a machine state; and
presenting, by the processor, a visualization of the known defect on a display of a user interface before executing the job request, wherein the visualization illustrates the known defect and presents an option for a user to ignore the known defect and continue with execution of the job request.