Sorting cast and wrought aluminum

• US 10,722,922 B2
• Filed: 04/04/2019
• Issued: 07/28/2020
• Est. Priority Date: 07/16/2015
• Status: Active Grant

• Current Assignee: Sortera Technologies Incorporated
• Original Assignee: UHV Technologies Incorporated
• Inventors: Kumar, Nalin, Garcia, Jr., Manuel Gerardo, Maun, Isha Kamleshbhai
• Primary Examiner(s): Fox, Charles A
• Assistant Examiner(s): Kumar, Kalyanavenkateshware
• Application Number:
  US16/375,675
• Publication Number:
  US 20190247891A1
• Time in Patent Office: 481 Days
• CPC Class Codes: B07C 2501/0054 Sorting of waste or refuse
• CPC Class Codes: B07C 5/04 Sorting according to size sorting devices as a component of the automatic supply and removal of workpieces B23Q7/12
• CPC Class Codes: B07C 5/34 Sorting according to other particular properties material testing per se G01N; quality control G07C3/14
• CPC Class Codes: B07C 5/342 according to optical properties, e.g. colour according to radiation transmittivity B07C5/3416
• CPC Class Codes: B07C 5/3422 using video scanning devices, e.g. TV-cameras

Abstract

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification determining that the materials are composed of either wrought aluminum or cast aluminum.

Claims

1. A system for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the system comprising:

an image capturing device configured to produce image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

a conveyor system configured to convey the first mixture past the image capturing device;

a data processing system comprising a machine learning system configured to classify certain ones of the first mixture as wrought aluminum scrap pieces based on the image data of the first mixture, wherein the classifying of certain ones of the first mixture is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces; and

a sorter configured to sort the classified certain ones of the first mixture from the first mixture as a function of the classifying of certain ones of the first mixture, wherein the system is configured to sort the first mixture without a use of x-ray spectroscopy to analyze compositions of the materials.

2. The system as recited in claim 1, wherein the library of observed characteristics were captured by a camera configured to capture images of the homogenous set of samples of the wrought aluminum scrap pieces as they were conveyed past the camera.

3. The system as recited in claim 1, wherein the image capturing device is a camera configured to capture visual images of the first mixture of materials comprising wrought and cast aluminum scrap pieces to produce the image data, and wherein the observed characteristics are visually observed characteristics.

4. The system as recited in claim 1, wherein the machine learning system comprises an artificial intelligence neural network.

5. The system as recited in claim 1, wherein the classifying of certain ones of the first mixture is based on a comparison of the first knowledge base to a second knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of cast aluminum scrap pieces.

6. The system as recited in claim 1, wherein the system is configured to sort the wrought aluminum scrap pieces from the cast aluminum scrap pieces based on the classifying of certain ones of the first mixture as wrought aluminum scrap pieces.

7. A system for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the system comprising:

an image capturing device configured to produce image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

a conveyor system configured to convey the first mixture past the image capturing device;

a data processing system comprising a machine learning system configured to classify certain ones of the first mixture as wrought aluminum scrap pieces based on the image data of the first mixture, wherein the classifying of certain ones of the first mixture is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces; and

a sorter configured to sort the classified certain ones of the first mixture from the first mixture as a function of the classifying of certain ones of the first mixture, wherein the sorting by the sorter of the classified certain ones of the first mixture from the first mixture produces a second mixture of materials that comprises the first mixture minus the classified certain ones of the first mixture, wherein the second mixture of materials contains an aggregate amount of magnesium of less than 1%.

8. The system as recited in claim 7, wherein the second mixture of materials has a composition of metals appropriate for manufacturing cast aluminum parts.

9. The system as recited in claim 7, wherein the second mixture of materials contains an aggregate amount of aluminum at a largest percentage relative to aggregate amounts of other metals.

10. A system for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the system comprising:

an image capturing device configured to produce image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

a conveyor system configured to convey the first mixture past the image capturing device;

a data processing system comprising a machine learning system configured to classify certain ones of the first mixture as wrought aluminum scrap pieces based on the image data of the first mixture, wherein the classifying of certain ones of the first mixture is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces; and

a sorter configured to sort the classified certain ones of the first mixture from the first mixture as a function of the classifying of certain ones of the first mixture, wherein the sorting by the sorter of the classified certain ones of the first mixture from the first mixture produces a second mixture of materials that comprises the first mixture minus the classified certain ones of the first mixture, wherein the second mixture of materials contains an aggregate amount of magnesium of less than 0.5%.

11. A method for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the method comprising:

producing image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

assigning with a machine learning system a first classification to certain ones of the first mixture of materials as wrought aluminum scrap pieces based on the image data of the first mixture, wherein the first classification is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces; and

sorting the certain ones of the first mixture of materials from the first mixture as a function of the first classification, wherein the sorting produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of magnesium of less than 1%.

12. The method as recited in claim 11, further comprising conveying the first mixture of materials past an image capturing device configured to produce the image data.

13. The method as recited in claim 11, wherein the library of observed characteristics were captured by a camera configured to capture images of the homogenous set of samples of the wrought aluminum scrap pieces as they were conveyed past the camera.

14. The method as recited in claim 11, wherein the image capturing device is a camera configured to capture visual images of the first mixture of materials to produce the image data, and wherein the observed characteristics are visually observed characteristics.

15. The method as recited in claim 11, wherein the machine learning system comprises an artificial intelligence neural network.

16. The method as recited in claim 11, further comprising melting the second mixture to produce a metal composition appropriate for manufacturing into cast aluminum parts.

17. A method for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the method comprising:

producing image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

assigning with a machine learning system a first classification to certain ones of the first mixture of materials as wrought aluminum scrap pieces based on the image data of the first mixture, wherein the first classification is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces; and

sorting the certain ones of the first mixture of materials from the first mixture as a function of the first classification, wherein the sorting produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of magnesium of less than 0.5%.

18. A method for classifying and sorting a first mixture of materials comprising wrought and cast aluminum scrap pieces, the method comprising:

producing image data of the first mixture of materials comprising wrought and cast aluminum scrap pieces;

assigning with a machine learning system a first classification to certain ones of the first mixture of materials based on the image data of the first mixture of materials; and

sorting the certain ones of the first mixture of materials from the first mixture as a function of the first classification assigned to the certain ones of the first mixture of materials, wherein the sorting of the certain ones of the first mixture of materials from the first mixture produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of magnesium of less than 1%.

19. The method as recited in claim 18, further comprising conveying the first mixture of materials past an image capturing device configured to produce the image data.

20. The method as recited in claim 18, wherein the first classification is based on a first knowledge base containing a previously generated library of observed characteristics captured from a homogenous set of samples of wrought aluminum scrap pieces.

21. The method as recited in claim 20, wherein the library of observed characteristics were captured by a camera configured to capture images of the homogenous set of samples of the wrought aluminum scrap pieces as they were conveyed past the camera.

22. The method as recited in claim 18, wherein the image capturing device is a camera configured to capture visual images of the first mixture of materials to produce the image data, and wherein the observed characteristics are visually observed characteristics.

23. The method as recited in claim 18, wherein the first mixture contains materials other than wrought and cast aluminum scrap pieces.

24. The method as recited in claim 18, wherein the machine learning system assigns the first classification to certain ones of the first mixture of materials as wrought aluminum scrap pieces based on the image data of the first mixture.