Quality forecasting engine

• US 5,761,093 A
• Filed: 05/08/1997
• Issued: 06/02/1998
• Est. Priority Date: 05/08/1997
• Status: Expired due to Fees

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Potter, Scott G., Urbish, Glenn F., Mullen, William Boone III
• Primary Examiner(s): Trammell, James P.
• Application Number:
  US08/854,130
• Time in Patent Office: 390 Days
• Field of Search: 364/550, 364/551.01, 364/552, 364/554, 364/555, 364/468.01, 364/468.02, 364/468.03, 364/468.1-468.14, 364/468.16, 364/468.17 , 364/488-491
• US Class Current: 700/107
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/31324 Distributed real time knowledge, database
• CPC Class Codes: G05B 2219/32194 Quality prediction
• CPC Class Codes: G05B 2219/32196 Store audit, history of inspection, control and workpiece data into database
• CPC Class Codes: G05B 2219/32203 Effect of material constituents, components on product manufactured
• CPC Class Codes: G05B 2219/45035 Printed circuit boards, also holes to be drilled in a plate
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

An automated method of predicting the number of defects that will occur during the manufacturing of a new electronic assembly design on a specific assembly line. Defective components are counted (214) as they are manufactured on an assembly line (210). The counted defects are stored in a historical database (220) and the defect rate is calculated. A bill of material for the new electronic design (230) is enumerated and information associated with each component is extracted from a library database (240) into a quality forecasting engine (250). The quality forecasting engine imports the enumerated bill of material and imports the stored defect rate from the historical database to perform a calculation of a predicted number of defects (260) for said new electronic assembly using the extracted library information.

Claims

1. An automated method of predicting the number of defects that will occur during the manufacturing of a new product design on a specific assembly, line, comprising:

identifying and counting the components that are manufactured on said assembly line during a time period;

counting the number of defects that are associated with each of said identified components;

storing the counted defects in a first historical database;

calculating the defect rate for each of said identified components and storing said defect rate in the first historical database;

enumerating a bill of material for said new electronic assembly design;

extracting information associated with a component from a library database into a quality forecasting engine, said information comprising part number, part description and part geometry;

the quality forecasting engine importing the enumerated bill of material and the stored defect rate from the first historical database; and

the quality forecasting engine performing a comparison of the imported enumerated bill of material information to the extracted information and to the imported stored defect rate, and calculating a predicted number of defects for said new electronic assembly.

2. The method as described in claim 1, further comprising an exception handler step, prior to the final step, wherein factors for components that are not resident in the first historical database or in the library database are input into the quality forecasting engine.

3. The method as described in claim 1, wherein the step of calculating a predicted number of defects comprises calculating a predicted number of defects for every component in the bill of material.

4. The method as described in claim 1, wherein the step of extracting and the step of importing comprises reformatting said information and said defect rate.

5. The method as described in claim 1, wherein said enumerated bill of material contains a listing of every component in said new electronic assembly design.

6. The method as described in claim 1, wherein if a component in said new electronic assembly design can not be compared with a stored defect rate for an equivalent component resident in said first historical database, then a stored defect rate from an equivalent component in a second historical database is imported into the quality forecasting engine.

7. An automated method of predicting the number of defects that will occur during the manufacturing of a new electronic assembly design on a specific assembly line, comprising:

identifying and counting the components that are manufactured on said assembly line during a time period;

counting the number of defects that are associated with each of said identified components;

storing the counted defects in a first historical database;

calculating the defect rate for each of said identified components and storing said defect rate in the first historical database;

enumerating a bill of material for every component in said new electronic assembly design;

extracting information associated with a component from a library database into a quality forecasting engine, said information comprising part number, part description and part geometry;

the quality forecasting engine importing the enumerated bill of material and the stored defect rate from the first historical database;

inputting into the quality forecasting engine factors for components that are not resident in the first historical database or in the library database; and

the quality forecasting engine performing a comparison of the imported enumerated bill of material information to the extracted information and to the imported stored defect rate, and calculating a predicted number of defects for every component in the bill of material of said new electronic assembly.

8. The method as described in claim 7, wherein if a component in said new electronic assembly design can not be compared with a stored defect rate for an equivalent component resident in said first historical database, then a stored defect rate from an equivalent component in a second historical database is imported into the quality forecasting engine.