US20020055925A1 - Automatic quality control method for production line and apparatus therefor as well as automatic quality control program - Google Patents

Automatic quality control method for production line and apparatus therefor as well as automatic quality control program Download PDF

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Publication number
US20020055925A1
US20020055925A1 US09/985,364 US98536401A US2002055925A1 US 20020055925 A1 US20020055925 A1 US 20020055925A1 US 98536401 A US98536401 A US 98536401A US 2002055925 A1 US2002055925 A1 US 2002055925A1
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Prior art keywords
quality
production
production line
improvement
factor
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US09/985,364
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English (en)
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Haruhiko Kondo
Tomoaki Kubo
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Toshiba Corp
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Individual
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Assigned to KABUSHIKI KAISHA TOSHIBA reassignment KABUSHIKI KAISHA TOSHIBA ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KONDO, HARUHIKO, KUBO, TOMOAKI
Publication of US20020055925A1 publication Critical patent/US20020055925A1/en
Priority to US11/293,307 priority Critical patent/US7181355B2/en
Abandoned legal-status Critical Current

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32177—Computer assisted quality surveyance, caq
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32196—Store audit, history of inspection, control and workpiece data into database
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32201—Build statistical model of past normal proces, compare with actual process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32216—If machining not optimized, simulate new parameters and correct machining
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to a quality control method for a production line which automatically carries out quality control of the line, which can be applied to various types of production lines such as lines for color-cathode ray tubes and semiconductor devices, and an apparatus used for the method, as well as an automatic quality control program.
  • Production lines are designed for various types of products such as color cathode ray tubes and semiconductor devices. In the production lines, the quality of the products is controlled while they are produced.
  • the product quality data includes the results of tests carried out on products in accordance with their type, for example, the results of performance tests of the product or the results of the examination of the appearance.
  • the just-mentioned quality control method can only be performed by operators highly skilled in manipulation, adjustment and operation of the production line. That is, operators, who have extensive and long-term experience regarding the product and production on the production line, lean an empirical rule specially for operating the production line and have obtained skills for improving the production line.
  • the object of the present invention is to provide an automatic quality control method for a production line, which can effectively make a full use of a huge amount of data by overcoming the limits of the data processing capabilities of human systems or the ambiguities innate to empirical and intuitive methods of humans, and the apparatus therefor, as well as an automatic quality control program.
  • an automatic quality control method for a production line comprising: monitoring a plurality of production condition data for manufacturing products from a production line and product quality data indicating quality of manufactured products; storing the production condition data and product quality data thus monitored in a database; checking the production condition data to detect whether or not there is an event which deteriorates the quality of products; extracting, if a quality deteriorating event is detected, a quality deteriorating factor which causes the quality deteriorating event and improvement contents of production conditions against the quality deteriorating factor on the basis of the quality deteriorating event and the production condition data; collating the quality deteriorating factor and improvement contents thus extracted with improvement examples pre-stored for possible quality deteriorating factors and confirming the validity of the quality deteriorating factor and improvement contents; executing a simulation of manufacture of a product in the production line based on the quality deteriorating factor and improvement contents thus extracted, and verifying the correctness of the quality deteriorating factor and the validity of the improvement contents, extracted from the
  • an automatic quality control apparatus for a production line, comprising: a production line configured to produce products; a first database configured to store a plurality of production condition data for producing the products and product quality data indicating quality of the products; a monitoring section configured to monitor a plurality of product condition data for production the products from the production line and further monitor the product quality data indicating the quality of the products, to store them in the first database; an extraction section configured to check the production condition data to detect whether or not there is an event which deteriorate the quality of products, and to extract a quality deteriorating factor which causes the quality deteriorating event and improvement contents of production conditions regarding the quality deteriorating factor on the basis of the quality deteriorating event and the production condition data; a second database configured to store, in advance, improvement examples for the quality deterioration factor; a validity confirming section configured to confirm the validity of the quality deteriorating factor and improvement contents by collating the quality deteriorating factor and improvement contents extracted by the extracting section with the improvement examples
  • an automatic quality control program for a production line comprising: monitoring a plurality of production condition data for manufacturing color cathode ray tubes from a production line and product quality data indicating quality of manufactured products; storing the production condition data and product quality data thus monitored in a database; checking the production condition data to detect whether or not there is an event which deteriorate the quality of products; extracting, if a quality deteriorating event is detected, a quality deteriorating factor which causes the quality deteriorating event and improvement contents of production conditions against the quality deteriorating factor on the basis of the quality deteriorating event and the production condition data; collating the quality deteriorating factor and improvement contents thus extracted with improvement examples pre-stored for possible quality deteriorating factors and confirming the validity of the quality deteriorating factor and improvement contents; executing a simulation of manufacture of a product in the production line based on the quality deteriorating factor and improvement contents thus extracted, and verifying the correctness of the quality deteriorating factor and the validity of
  • an automatic quality control method for a production line which can effectively make a full use of a huge amount of data by overcoming the limits of the data processing capabilities of human systems or the ambiguities innate to empirical and intuitive methods of humans, and the apparatus therefor, as well as an automatic quality control program.
  • FIG. 1 is a block diagram showing a structure of a first embodiment of an automatic quality control apparatus for a production line, according to the present invention
  • FIG. 2 is a diagram showing a production line for a color-cathode ray tube, which is applied to the first embodiment of the automatic quality control apparatus for a production line, according to the present invention
  • FIG. 3 is a diagram showing a procedure of an automatic quality control program in the first embodiment of the automatic quality control apparatus for a production line, according to the present invention
  • FIG. 4 is a schematic diagram showing production condition data and product quality data in the first embodiment of the automatic quality control apparatus for a production line, according to the present invention
  • FIG. 5 is a schematic diagram showing a quality improvement history database in the first embodiment of the automatic quality control apparatus for a production line, according to the present invention
  • FIG. 6 is a block diagram showing a structure of a second embodiment of an automatic quality control apparatus for a production line, according to the present invention.
  • FIG. 7 is a block diagram showing a structure of a third embodiment of an automatic quality control apparatus for a production line, according to the present invention.
  • FIG. 1 is a block diagram showing a structure of an automatic quality control apparatus for a production line.
  • a production line 1 is an example of a generally known production line for a color cathode ray tube.
  • the production line 1 includes a panel mask assembly process 2 , a black body application process 3 , a micro-filter application process 4 , a fluorescent material application process 5 , a sealing/enclosing/exhaustion process 6 , a yoke assembly adjustment process 7 and an examination process 8 .
  • the production conditions for the color cathode ray tube in the production line 1 are, for example, the outside temperature, humidity, the hot water ejection time for ejecting hot water to the glass panel of the color cathode ray tube, the kinetic viscosity of the solution used for applying the fluorescent material, and the amount of the fluorescent material applied. Other than these, there are a great number of production conditions for the color cathode ray tube.
  • a production condition control section 9 controls each of a plurality of production conditions in the production line 1 for producing color cathode ray tubes.
  • the production line 1 is a line for semiconductor devices.
  • the production line 1 for semiconductor devices includes, for example, a film forming process, a resist application process, an exposure process, development process, an etching process and a resist removing process.
  • a production condition control section 9 of the production line for semiconductor devices controls, for example, the temperature, humidity, resist viscosity, application force of the resist, and the application amount of the resist, with regard to the production line 1 for semiconductor devices.
  • the production line 1 is not limited to the manufacture of color cathode ray tubes or semiconductor devices, but it can be a line for a variety of types of products.
  • An arithmetic operating section 10 is connected to a program memory 11 , a production history database 12 and a quality improvement history database 13 .
  • the program memory 11 stores an automatic quality control program.
  • the details of the automatic quality control program which will be described in detail later, include the following seven procedures.
  • step #1 in which a plurality of production condition data for manufacturing color cathode ray tubes are monitored from the production line 1 and product quality data indicating the qualities of manufactured products are monitored;
  • step #2 The second procedure (step #2) in which the production condition data and product quality data thus monitored are stored in the production history database 12 ;
  • step #3 in which the production condition data are monitored and whether or not there is an event which deteriorates the quality of products is detected;
  • step #4 The fourth procedure (step #4) in which if a quality deteriorating event is detected, a quality deteriorating factor which causes the quality deteriorating event and improvement contents of production conditions against the quality deteriorating factor are extracted on the basis of the quality deteriorating event and the production condition data;
  • step #5 The fifth procedure (step #5) in which the quality deteriorating factor and improvement contents thus extracted are collated with improvement examples pre-stored for possible quality deteriorating factors to confirm the validity of the quality deteriorating factor and improvement contents;
  • step #6 The sixth procedure (step #6) in which a simulation of manufacture of a product is executed in the production line 1 based on the quality deteriorating factor and improvement contents thus extracted, to verify the correctness of the quality deteriorating factor and the validity of the improvement contents, extracted from the result of the simulation;
  • step #7 The seventh procedure (step #7) in which if the correctness of the quality deteriorating factor and the validity of the improvement contents are verified, the production conditions for the production line 1 are revised in accordance with the improvement contents.
  • the arithmetic operating section 10 is designed to execute the automatic quality control program stored in the program memory 11 , and includes a monitoring section 14 , an extracting section 15 , a validity confirming section 16 , a verifying section 17 , a feedback control section 18 and a regional feedback control section 19 .
  • the monitoring section 14 monitors production condition data from each of a plurality of processes in the line 1 for color cathode ray tube shown in, for example, FIG. 2, such as the black dye application process 3 , the micro-filter application process 4 and the fluorescent material application process 5 .
  • the monitoring section 14 monitors product quality data from each of a plurality of processes in the line 1 for color cathode ray tube shown in, for example, FIG. 2, such as the fluorescent material application process 5 and the examination process 8 .
  • the monitoring section 14 monitors the sensor signals and converts them into digital signals (digital sensor signals 14 b ), and stores the digital signal in the production history database 12 .
  • the monitoring section 14 carries out pattern recognition 14 c on the images, and stores them in the production history database 12 .
  • the production condition data 20 include the serial number (serial No.) of each color cathode ray tube, the date, the outside temperature, the humidity, the hot water ejecting time, the kinetic viscosity of the fluorescent material slurry, the application amount of the fluorescent material slurry and the like, as shown in FIG. 4.
  • the production condition data 20 include the serial number (serial No.) of each semiconductor device, the date, the temperature, the humidity, the viscosity of the resist, the application force of the resist and the like.
  • the production condition data 20 include the flow amount of the process gas and the pressure thereof.
  • the product quality data 21 indicate the quality of a produced color cathode ray tube.
  • the product quality data 21 include the judgment result data, the examination result data and the quality data.
  • the judgment result data indicates whether or not the appearance and function of the produced color cathode ray tube are respectively those as designed in advance.
  • the quality data indicates the yield in the manufacture of color cathode ray tubes, and the percent defective in the color cathode ray tubes.
  • the extraction section 15 checks the product quality data 21 stored in the production history database 12 , and detects whether or not there is a quality deterioration event (error code) in the manufacture of the color cathode ray tubes.
  • the items to be detected while checking the product quality data 21 are a sudden variation in the occurrence rate of the quality deterioration event as well as whether or not the yield of the color cathode ray tubes stays at the level of the original yield.
  • Whether or not a yield stays at its original level can be detected, for example, by obtaining the yield of color cathode ray tubes from the occurrence of an error code and watching if the yield stays at its original level along with time elapse.
  • the extracting section 15 executes a data mining algorithm on the bases of the quality deterioration event and the production condition data, and extracts the improvement contents.
  • Typical and specific examples of the data mining algorithm in terms of the statistical method are correlation analysis, multiple regression analysis and variance analysis.
  • AI artificial intelligence
  • an example thereof is an analysis of the significance of a factor by neural network leaning.
  • machine leaning method an example thereof is a process of classification based on various types of indexes such as a square value of ⁇ , the entropy of data and the purity of data.
  • the verifying section 17 executes a simulation of a phenomenon regarding the production line 1 according to the quality deterioration factor and the improvement contents extracted by the extracting section 15 , that is, a simulation for analyzing a basic physiochemical phenomenon regarding the manufacture of color cathode ray tubes. From the result of the simulation, the correctness of the quality deterioration factor and the validity of the improvement contents are verified.
  • the verifying section 17 verifies if the quality deterioration factor extracted by the extracting section 15 is correct.
  • the section further verifies, when the production condition data of the production line 1 are revised to the improvement contents extracted by the extracting section 15 , whether or not a trouble occurs in the production line 1 .
  • the apparatus of the present invention performs the quality control operation when a sudden variation of the occurrence rate of the quality deterioration event or staying of a yield at its original level occurs.
  • the monitoring section 14 monitors production condition data from each of a plurality of processes in the line 1 for color cathode ray tube shown in, for example, FIG. 2, such as the black dye application process 3 , the micro-filter application process 4 and the fluorescent material application process 5 .
  • the production condition data include, as shown in FIG. 4, for example, the serial number (serial No.), the date, the outside temperature, the humidity, the hot water ejecting time, the kinetic viscosity of the fluorescent material slurry, the application amount of the fluorescent material slurry, the machine number of the device used for a respective process, the physical property of the liquid agent and the like.
  • the monitoring section 14 monitors product quality data from each of a plurality of processes in the production line 1 for color cathode ray tube shown in, for example, FIG. 2, such as the fluorescent material application process 5 and the examination process 8 .
  • the product quality data from the fluorescent material application process 5 includes, for example, data regarding splashing of the fluorescent material while applying the fluorescent material slurry onto the glass panel of a color cathode ray tube, attachment of dust, irregularity of the application of the fluorescent material and bubbles created while applying the slurry.
  • Three colors of fluorescent materials, namely, R (red), G (green) and B (blue) are applied.
  • the monitoring section 14 creates an electronic file 14 a (electronified document) from the documents, and stores it in the production history database 12 .
  • the monitoring section 14 monitors the sensor signals and converts them into digital signals (digital sensor signals 14 b ), and stores the digital signal in the production history database 12 .
  • the monitoring section 14 carries out pattern recognition 14 c on the images, and stores them in the production history database 12 .
  • the monitoring section 14 converts the sensed amount into a numerical value (conversion of sensed amount into numeral 14 d ) according to a specific algorithm, and stores it in the production history database 12 .
  • the extracting section 15 in step #3, sections the product quality data 21 stored in the production history database 12 and detects if there is a sudden variation in the occurrence rate of the quality deterioration event, or whether or not the yield of the color cathode ray tubes stays at the level of the original yield.
  • the extracting section 15 detects that there is a sudden variation in the occurrence rate of the quality deterioration event, or the yield of the color cathode ray tubes stays at the level of the original yield, the extracting section 15 , in step #4, executes a data mining algorithm on the bases of the quality deterioration event and the production condition data 20 , and extracts the quality deterioration factor which causes the quality deterioration event and the improvement contents of the production condition data 20 with regard to the quality deterioration factor.
  • the extracting section 15 checks the product quality data 21 stored in the production history database 12 and detects that there is a sudden variation in the occurrence rate of the error code indicating the irregularity of the red fluorescent material, the extracting section 15 executes the data mining algorithm on the bases of the irregularity of the red fluorescent material (quality deterioration event) and the production condition data 20 , and extracts the quality deterioration factor which causes the irregularity of the red fluorescent material and the improvement contents thereof.
  • the extracting section 15 extracts that the irregularity of the red fluorescent material frequently occurs, as well as the improvement contents therefor, that is, the temperature at the point when the glass panels are loaded and the kinetic viscosity of the red fluorescent material are adjusted.
  • the validity confirming section 16 collates the quality deterioration factor and the improvement contents extracted from the extracting section 15 with the quality improvement history data pre-stored in the quality deterioration history database 13 , in order to confirm the validity of the quality deterioration factor and the improvement contents.
  • the verifying section 17 executes a simulation of a phenomenon regarding the production line 1 according to the quality deterioration factor and the improvement contents extracted by the extracting section 15 , that is, a simulation for analyzing a basic physiochemical phenomenon regarding the manufacture of color cathode ray tubes. From the result of the simulation, the correctness of the quality deterioration factor and the validity of the improvement contents are verified.
  • the verifying section 17 verifies if the quality deterioration factor extracted by the extracting section 15 is correct.
  • the section further verifies, when the production condition data of the production line 1 are revised to the improvement contents extracted by the extracting section 15 , whether or not a trouble occurs in the production line 1 .
  • the verifying section 17 executes a simulation of the fluorescent material application process 5 , and obtains the following simulation results. That is, when glass panels having a very low temperature, which is caused by a low outside temperature, are loaded, the temperature of each glass panel becomes uneven from one place to another within it until it reaches the fluorescent material application process 5 . When a fluorescent material having a high kinetic viscosity is injected in the above-described state, an irregularity is created in the fluorescent material film.
  • the verifying section 17 confirms the correctness of the quality deterioration factor and the validity of the improvement contents when the deterioration factors (low outside temperature and high kinetic viscosity) for the past quality deterioration contents (frequent occurrence of irregularity of the red fluorescent material) and the improvement contents (extension of hot water ejection and revision of the upper limit of the kinetic viscosity), which are stored in the quality improvement history database 13 , match with the simulation results described above.
  • the feedback control section 18 transmits a feedback control signal for revising the production conditions for the production line 1 in accordance with the improvement contents, to the production condition control section 9 when the correctness of the quality deterioration factor and the validity of the improvement contents are verified by the verifying section 17 .
  • the feedback control section 18 transmits the feedback control signal indicating the improvement contents, that is, the extension of the hot water ejecting time for stabilizing the temperature of the glass panel of each color cathode ray tube, and the lowering of the upper limit value of the kinetic viscosity of the fluorescent material slurry.
  • the production condition control section 9 extends the hot water ejecting time in the fluorescent material application process 5 and lowers the upper limit value of the kinetic viscosity of the fluorescent material slurry.
  • the regional feedback control section 19 receives the product quality data 21 stored in the production history database 12 and the quality deterioration event in the production line 1 , which is extracted by the extracting section 15 , controls the production line 1 regionally, for example, controls only the temperature of the resist to an appropriate value, and notifies a warning or the like if necessary depending on the quality deterioration event.
  • the production condition data 20 and the product quality data 21 in the production line 1 are monitored, and stored in the production history database 12 . While checking the product quality data 21 , if a sudden variation in the occurrence rate of the quality deterioration event or the staying of a yield at its original level is detected in the production line 1 , the quality deterioration factor and the improvement contents are extracted on the basis of the quality deterioration event and the production condition data 20 . Then, the extracted results are collated with the quality improvement history data pre-stored so as to confirm the validity thereof, and further a simulation of a phenomenon regarding the production line 1 according to the quality deterioration factor and the improvement contents is executed so as to verify the correctness thereof. When the validity and correctness are verified, the production conditions are revised in accordance with the improvement contents.
  • FIG. 6 is a block diagram showing a structure of an automatic quality control apparatus of a production line.
  • a warning section 30 and a revision expediting section 31 are provided in place of the feedback control section 18 shown in FIG. 1.
  • the warning section 30 and the revision expediting section 31 are operated when the validity confirming section 16 cannot confirm the validity of the quality deterioration factor and the improvement contents, and the verifying section 17 cannot verify the correctness of the quality deterioration factor and the validity of the improvement contents.
  • the warning section 30 output a warning indicating that it has not been able to confirm the validity and correctness.
  • the revision expediting section 31 presents candidates for the quality deterioration factor and requests revision of the production conditions of the line 1 for improving the quality deterioration factor.
  • the automatic quality control program stored in the program memory 11 includes a procedure in which when the validity of the quality deterioration factor and the improvement contents cannot be confirmed in the fifth procedure, and the correctness of the quality deterioration factor and the validity of the improvement contents cannot be verified in the sixth procedure, a warning indicating that it has not been able to confirm the validity and correctness is output.
  • the program further includes a procedure in which candidates for the quality deterioration factor are presented and revision of the production conditions of the line for improving the quality deterioration factor is requested.
  • the warning section 30 outputs a warning indicating that it has not been able to confirm the validity and correctness.
  • the revision expediting section 31 presents candidates for the quality deterioration factor and requests revision of the production conditions of the line 1 for improving the quality deterioration factor.
  • the second embodiment can enhance the robustness of the system in a cooperation with human system and can increase the rates of the operations of the examination, finding and improvement of the quality deterioration factor, as compared to the conventional operations which are conducted only by the human system.
  • FIG. 7 is a block diagram showing a structure of an automatic quality control apparatus of a production line.
  • This apparatus includes a leaning section 32 .
  • the improvement example is additionally stored in the quality improvement history database 13 .
  • the automatic quality control program stored in the program memory 11 includes a procedure in which when the production line 1 is improved by revising the production conditions of the line 1 in accordance with the improvement contents by the seventh procedure, the improvement example is leaned.
  • the feedback control section 18 transmits a feedback control signal for revising the production conditions for the production line 1 in accordance with the improvement contents, to the production condition control section 9 when the correctness of the quality deterioration factor and the validity of the improvement contents are verified by the verifying section 17 .
  • the production condition control section 9 controls each of a plurality of production conditions in the production line 1 for producing color cathode ray tubes.
  • the warning section 30 outputs a warning indicating that it has not been able to confirm the validity and correctness.
  • revision expediting section 31 presents candidates for the quality deterioration factor and requests revision of the production conditions of the line 1 for improving the quality deterioration factor.
  • the leaning section 32 When the production line 1 is improved by revising the production conditions of the line 1 in accordance with the improvement contents as the revision is expedited by the revision expediting section 31 or the feedback control section 18 , the leaning section 32 additionally stores the improvement example in the quality improvement history database 13 .
  • the self-leaning of the quality improvement method and the enhancement of the performance can be achieved. From these results, it is possible to detect the quality deterioration factor in an early stage and therefore it become able to make an improvement at an early stage. Therefore, with the present invention apparatus, a production line having a high and stable reliability can be established.

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Cited By (21)

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WO2004049081A1 (ja) * 2002-11-25 2004-06-10 Bridgestone Corporation 製造評価の管理システムおよび管理方法
WO2004059406A1 (ja) * 2002-12-25 2004-07-15 Nhk Spring Co., Ltd. 品質問題の統計的解決作業管理システムとそれを用いた作業管理方法
US20050159835A1 (en) * 2003-12-26 2005-07-21 Kentaro Yamada Device for and method of creating a model for determining relationship between process and quality
US20080215473A1 (en) * 2002-01-23 2008-09-04 Christopher Cashman Method for Positively Identifying Livestock and Use Thereof In Legal Instruments Relating Thereto
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