Patent · US10737446B2 · B2 · US

Process control of a composite fabrication process

(11) Publication number

US10737446B2

(21) Application number

15/581,432

(22) Filing date

2017-04-28

(30) Priority date

2017-04-28

(43) Publication date

2020-08-11

(45) Date of grant

2020-08-11

(51) IPC

B25J 9/16; B29C 70/38; B29K 105/08; B32B 41/00; G01N 21/84; G05B 19/418; G05B 19/42

(52) CPC

B29C Shaping or joining of plastics; shaping of material in a plastic state, not otherwise provided for; after-treatment of the shaped products, e.g. repairing: 70/386, 70/38
B25J Manipulators; chambers provided with manipulation devices: 9/1684
B29K Indexing scheme associated with subclasses B29B, B29C or B29D, relating to moulding materials or to materials for {moulds, } reinforcements, fillers or preformed parts, e.g. inserts: 2105/0872
G01N Investigating or analysing materials by determining their chemical or physical properties: 2021/8472, 21/8851
G05B Control or regulating systems in general; functional elements of such systems; monitoring or testing arrangements for such systems or elements: 19/41875, 19/4207, 2219/32191, 2219/32194, 2219/37198, 2219/37208
G06T Image data processing or generation, in general: 2207/30108, 7/0004, 7/0006

(73) Assignee

Boeing Co

(72) Inventors

Jeffery Lee Marcoe; Jan Wei Pan

(54) Title

Process control of a composite fabrication process

(57) Abstract

A system for process control of a composite fabrication process comprises an automated composite placement head, a vision system, and a computer system. The automated composite placement head is configured to lay down composite material. The vision system is connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material. The computer system is configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions based on the inconsistencies.

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Claims (23)

A system for process control of a composite fabrication process comprising: an automated composite placement head configured to lay down composite material; a vision system connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material; a computer system configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and a display, wherein the computer system is configured to show the image data on the display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.
The system of claim 1, wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material.
The system of claim 2, wherein the computer system is configured to make the number of metrology decisions while the automated composite placement head is laying down the composite material.
The system of claim 1, wherein the number of metrology decisions comprises modifying an inconsistency allowance threshold, wherein the computer system is configured to employ a probabilistic approach to modify the inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on at least one property of the inconsistencies identified in the image data, wherein properties of inconsistencies include at least one of size, density, location, inconsistency type, or randomness.
The system of claim 4, wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.
The system of claim 1, wherein the composite material is a part of a component, wherein the computer system is configured to compare locations of the inconsistencies identified in the image data to a design of the component.
The system of claim 1, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.
A method comprising: automatically imaging a composite material, during or after laying down the composite material, using a vision system to form image data; identifying, by a computer system, inconsistencies in the composite material visible within the image data in real-time; making, by the computer system, a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and displaying the image data in real-time with a width and a length superimposed over each of the inconsistencies that is visible within displayed image data.
The method of claim 8 further comprising: storing data for the inconsistencies in a database; building machine learning datasets and probabilistic information using the database; and using the machine learning datasets and the probabilistic information to forecast a quality of a portion of a component containing the composite material.
The method of claim 8, wherein making the number of metrology decisions includes: sending out a warning when an inconsistency of the inconsistencies identified in the image data violates an inconsistency allowance threshold.
The method of claim 10, wherein the composite material is part of a component, wherein the inconsistency allowance threshold takes into account at least one of a quantity of inconsistencies identified in a prior level of composite material of the component, types of inconsistencies identified in a prior level of composite material of the component, or locations of inconsistencies identified in a prior level of composite material of the component.
The method of claim 8, wherein making the number of metrology decisions includes: modifying an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on properties of the inconsistencies identified in the image data including at least one of locations of the inconsistencies, a quantity of the inconsistencies, a density of the inconsistencies, or a measure of randomness of the inconsistencies.
The method of claim 12, wherein the composite material is part of a component, and wherein the inconsistency allowance threshold is modified based on a design of the component.
The method of claim 13, wherein the inconsistency allowance threshold is modified based on historical performance data of other components.
The method of claim 8 further comprising: assigning an inconsistency type, by the computer system, to each of the inconsistencies identified in the image data.
The method of claim 8 further comprising: measuring the inconsistencies identified in the image data.
The method of claim 8, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.
A method comprising: creating image data of a composite material using a vision system, wherein the image data is created at least one of during or after laying down the composite material; identifying in real-time, by a computer system, inconsistencies in the composite material visible within the image data; and displaying the image data on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.
The method of claim 18 further comprising: making, by the computer system, a number of metrology decisions based on the inconsistencies, historical performance data, and a design of a component wherein the composite material is a part of the component.
The method of claim 19, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.
The method of claim 18 further comprising: modifying, by the computer system, an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on the inconsistencies identified in the image data.
The method of claim 21, wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.
The method of claim 18, wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material.

Description

The present disclosure relates generally to inspection and, more specifically, to the inspection of composite materials. Still more particularly, the present disclosure relates to using inspection data for process control of a composite fabrication process.

Composite materials are laid down by an automatic material placement process into layers, called plies. After laying down a ply, the ply is manually inspected for inconsistencies. The inconsistencies may occur as part of a composite manufacturing process and may include foreign object debris (FOD), fuzz balls, resin balls, twisted tows, folded tows, slit tape tow “chips,” missing tows, damaged tows, wrinkles, puckers, end of ply inconsistencies, gaps, laps, or any other undesirable feature introduced in the ply. Each component has a tolerance for an acceptable size of inconsistencies. After inspection, a size of inconsistencies may be compared to the tolerance for the component.

A manual inspection of a composite ply may take an undesirable amount of time to complete. Additional plies are not laid down until an inspection is completed. Thus, the manual inspection of the ply may add an undesirable amount of time to an overall manufacturing time.

For large components, accessing the composite ply for the manual inspection may be undesirably difficult. For some large parts, lifting platforms may be used. Moving the lifting platforms relative to the large parts may add an undesirable amount of time to the inspection process.

Citations (18)

US5562788A
US6064429A
US7236625B2
WO2005036634A2
US8068659B2
US7688434B2
US7576850B2
US7678214B2
US7424902B2
US7712502B2
US20060108048A1
US8524021B2
US8770248B2
WO2010132998A1
EP2730914A1
EP3007022A2
US20160341671A1
US20170030886A1

Record as JSON

{
  "publication_number": "US10737446B2",
  "country": "US",
  "kind": "B2",
  "title": "Process control of a composite fabrication process",
  "abstract": "A system for process control of a composite fabrication process comprises an automated composite placement head, a vision system, and a computer system. The automated composite placement head is configured to lay down composite material. The vision system is connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material. The computer system is configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions based on the inconsistencies.",
  "claims": [
    "1. A system for process control of a composite fabrication process comprising: an automated composite placement head configured to lay down composite material; a vision system connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material; a computer system configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and a display, wherein the computer system is configured to show the image data on the display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.",
    "2. The system of claim 1, wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material.",
    "3. The system of claim 2, wherein the computer system is configured to make the number of metrology decisions while the automated composite placement head is laying down the composite material.",
    "4. The system of claim 1, wherein the number of metrology decisions comprises modifying an inconsistency allowance threshold, wherein the computer system is configured to employ a probabilistic approach to modify the inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on at least one property of the inconsistencies identified in the image data, wherein properties of inconsistencies include at least one of size, density, location, inconsistency type, or randomness.",
    "5. The system of claim 4, wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.",
    "6. The system of claim 1, wherein the composite material is a part of a component, wherein the computer system is configured to compare locations of the inconsistencies identified in the image data to a design of the component.",
    "7. The system of claim 1, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.",
    "8. A method comprising: automatically imaging a composite material, during or after laying down the composite material, using a vision system to form image data; identifying, by a computer system, inconsistencies in the composite material visible within the image data in real-time; making, by the computer system, a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and displaying the image data in real-time with a width and a length superimposed over each of the inconsistencies that is visible within displayed image data.",
    "9. The method of claim 8 further comprising: storing data for the inconsistencies in a database; building machine learning datasets and probabilistic information using the database; and using the machine learning datasets and the probabilistic information to forecast a quality of a portion of a component containing the composite material.",
    "10. The method of claim 8, wherein making the number of metrology decisions includes: sending out a warning when an inconsistency of the inconsistencies identified in the image data violates an inconsistency allowance threshold.",
    "11. The method of claim 10, wherein the composite material is part of a component, wherein the inconsistency allowance threshold takes into account at least one of a quantity of inconsistencies identified in a prior level of composite material of the component, types of inconsistencies identified in a prior level of composite material of the component, or locations of inconsistencies identified in a prior level of composite material of the component.",
    "12. The method of claim 8, wherein making the number of metrology decisions includes: modifying an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on properties of the inconsistencies identified in the image data including at least one of locations of the inconsistencies, a quantity of the inconsistencies, a density of the inconsistencies, or a measure of randomness of the inconsistencies.",
    "13. The method of claim 12, wherein the composite material is part of a component, and wherein the inconsistency allowance threshold is modified based on a design of the component.",
    "14. The method of claim 13, wherein the inconsistency allowance threshold is modified based on historical performance data of other components.",
    "15. The method of claim 8 further comprising: assigning an inconsistency type, by the computer system, to each of the inconsistencies identified in the image data.",
    "16. The method of claim 8 further comprising: measuring the inconsistencies identified in the image data.",
    "17. The method of claim 8, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.",
    "18. A method comprising: creating image data of a composite material using a vision system, wherein the image data is created at least one of during or after laying down the composite material; identifying in real-time, by a computer system, inconsistencies in the composite material visible within the image data; and displaying the image data on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.",
    "19. The method of claim 18 further comprising: making, by the computer system, a number of metrology decisions based on the inconsistencies, historical performance data, and a design of a component wherein the composite material is a part of the component.",
    "20. The method of claim 19, wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.",
    "21. The method of claim 18 further comprising: modifying, by the computer system, an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on the inconsistencies identified in the image data.",
    "22. The method of claim 21, wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.",
    "23. The method of claim 18, wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material."
  ],
  "description_excerpt": "The present disclosure relates generally to inspection and, more specifically, to the inspection of composite materials. Still more particularly, the present disclosure relates to using inspection data for process control of a composite fabrication process.\n\nComposite materials are laid down by an automatic material placement process into layers, called plies. After laying down a ply, the ply is manually inspected for inconsistencies. The inconsistencies may occur as part of a composite manufacturing process and may include foreign object debris (FOD), fuzz balls, resin balls, twisted tows, folded tows, slit tape tow “chips,” missing tows, damaged tows, wrinkles, puckers, end of ply inconsistencies, gaps, laps, or any other undesirable feature introduced in the ply. Each component has a tolerance for an acceptable size of inconsistencies. After inspection, a size of inconsistencies may be compared to the tolerance for the component.\n\nA manual inspection of a composite ply may take an undesirable amount of time to complete. Additional plies are not laid down until an inspection is completed. Thus, the manual inspection of the ply may add an undesirable amount of time to an overall manufacturing time.\n\nFor large components, accessing the composite ply for the manual inspection may be undesirably difficult. For some large parts, lifting platforms may be used. Moving the lifting platforms relative to the large parts may add an undesirable amount of time to the inspection process.",
  "cpc": [
    "B29C 70/386",
    "B25J 9/1684",
    "B29C 70/38",
    "B29K 2105/0872",
    "G01N 2021/8472",
    "G01N 21/8851",
    "G05B 19/41875",
    "G05B 19/4207",
    "G05B 2219/32191",
    "G05B 2219/32194",
    "G05B 2219/37198",
    "G05B 2219/37208",
    "G06T 2207/30108",
    "G06T 7/0004",
    "G06T 7/0006"
  ],
  "ipc": [
    "B25J 9/16",
    "B29C 70/38",
    "B29K 105/08",
    "B32B 41/00",
    "G01N 21/84",
    "G05B 19/418",
    "G05B 19/42"
  ],
  "assignees": [
    "Boeing Co"
  ],
  "inventors": [
    "Jeffery Lee Marcoe",
    "Jan Wei Pan"
  ],
  "filing_date": "2017-04-28",
  "publication_date": "2020-08-11",
  "grant_date": "2020-08-11",
  "priority_date": "2017-04-28",
  "application_number": "US-201715581432-A",
  "family_id": "61965756",
  "cited_by_count": 4,
  "citations": [
    "US5562788A",
    "US6064429A",
    "US7236625B2",
    "WO2005036634A2",
    "US8068659B2",
    "US7688434B2",
    "US7576850B2",
    "US7678214B2",
    "US7424902B2",
    "US7712502B2",
    "US20060108048A1",
    "US8524021B2",
    "US8770248B2",
    "WO2010132998A1",
    "EP2730914A1",
    "EP3007022A2",
    "US20160341671A1",
    "US20170030886A1"
  ]
}

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