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WO2002014967A2 - Procede permettant l'execution d'un processus de production automatise - Google Patents

Procede permettant l'execution d'un processus de production automatise Download PDF

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Publication number
WO2002014967A2
WO2002014967A2 PCT/EP2001/009414 EP0109414W WO0214967A2 WO 2002014967 A2 WO2002014967 A2 WO 2002014967A2 EP 0109414 W EP0109414 W EP 0109414W WO 0214967 A2 WO0214967 A2 WO 0214967A2
Authority
WO
WIPO (PCT)
Prior art keywords
parameters
production
model
influencing variables
variables
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2001/009414
Other languages
German (de)
English (en)
Other versions
WO2002014967A3 (fr
Inventor
Marco Rosazza
Luciano Paravella
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
TECCON Srl
Original Assignee
TECCON Srl
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from DE10040731A external-priority patent/DE10040731A1/de
Application filed by TECCON Srl filed Critical TECCON Srl
Publication of WO2002014967A2 publication Critical patent/WO2002014967A2/fr
Publication of WO2002014967A3 publication Critical patent/WO2002014967A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • 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
    • 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]
    • G05B19/41885—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 modeling, simulation of the manufacturing system
    • 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
    • G05B2219/32015—Optimize, process management, optimize production line
    • 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
    • G05B2219/32017—Adapt real process as function of changing simulation model, changing for better results
    • 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
    • G05B2219/42—Servomotor, servo controller kind till VSS
    • G05B2219/42135—Fuzzy model reference learning controller, synthesis, tune rule base automatically
    • 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
    • G05B2219/42—Servomotor, servo controller kind till VSS
    • G05B2219/42137—Automatic tune fuzzy controller
    • 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 invention relates to a method for carrying out an automated production process according to the preamble of claim 1.
  • the task of controlling a production facility can be more or less difficult, depending on the complexity of the respective process and the number of variables involved.
  • the task can take on highly critical aspects when it comes to particularly sophisticated automated systems. This fact is of particular importance. Because the automation of production processes is one of the strategic goals that is of the utmost importance for modern industry.
  • automation has its limits due to the complexity that arises. When automation is at a certain level of complexity, control and flexibility are lost and the benefits diminish. This also applies to automated production processes in which a large number of physical or chemical parameters of the product or the production process as well as disturbances - more or less - are measured continuously and used in a control loop to control the production process.
  • the invention is intended to eliminate the lack of innovative development, ler between the high level of automation technology, on the one hand and still conventional system the aid of learning is, which are applied by the äetriebsleuten in making decisions for control and command of the s roduktionsreaes.
  • the parameters are physical or chemical
  • the production parameters are physical
  • the influencing variables also include the parameters of the raw materials and auxiliary materials. These parameters as well as the process parameters have to be considered. It must be determined whether the properties of the raw or auxiliary materials vary so widely that ongoing monitoring and inclusion in the virtual process is required. However, the properties and parameters of raw materials and auxiliary materials can also be included in the virtual process if there is enough scope for product planning and - or production planning - that the real process can only be adapted to the product that is actually desired changing raw materials or auxiliary materials is possible.
  • quality monitoring is preferably carried out in the sense that the resulting products are given a classification which characterizes the quality level or the committee without the otherwise usual testing of the individual product or a sample of the product.
  • a quality check and classification of the finished product which hampers the material flow and is labor intensive, is not necessary.
  • comparison of the influencing variables or parameter of the virtual model and the measured influencing variables and / or parameters of the real production process is additionally used to determine faults in the process and / or the informative value of the model for the Process constantly verified. While the size of the deviations detected when the warning signal is output make it necessary to check the model and the process and the interdependency, a pronounced course of the deviations over time (trend) is an indication that a malfunction has occurred within the real process Remedial action is required if the previously defined trust range, which is specific to the relevant influencing variable or characteristic variable, is left.
  • comparison of the influencing variables or parameters of the virtual mode and the measured influencing variables and / or parameters of the real production process is used in order to intervene in a controlling or regulating manner in the process.
  • the comparison of the process data with the data of the virtual process has the advantage that individual measurement data are not used for the intervention in the process, but that the respective controlling intervention takes into account all effects of the intervention due to the stored dependency model. In this way, negative effects of the controlling intervention can be avoided. Inadmissible regulation fluctuations and vibrations are avoided with regulation.
  • the proximity of the process model to the real process is achieved according to claims 6 and 7.
  • a novel process control is created, which is composed of several steps and with which from the multitude of process parameters or product parameters those at most three parameters are selected which are as reliable indicators as possible that the process works in the soil area, or the product is manufactured with the required properties. It is particularly important to first determine the type and number of trials and test series required so that statistically reliable statements about the dependencies can be obtained. The selection of the production parameters (parameters and influencing variables) that go into the model must also be determined according to whether suitable sensors are available.
  • Claim 9 provides suitable aids for determining and representing the mutual dependencies between the influencing variables and the
  • the dependencies can also be such that they cannot be grasped mathematically; in this case the dependencies are determined by experiment. in particular, it will be found that between the influencing variables and the
  • Claim 5 provides a suitable method for this.
  • the resulting dependency model can be formulated mathematically, preferably linearly (claim 10) or as a neural network (claim 10)
  • the dependency model is unique and fixed, i.e. it is not changed.
  • a reliable but manageable model that is suitable for electronic data processing is preferably produced by the method according to one of claims 12 to 14.
  • mutually dependent production parameters parameters which are a measure of the quality and / or quantity of the product or the production process or parameters which influence the parameters which can be achieved in the production process
  • Substitute parameters returned are combined. (Principal Components).
  • the network of dependencies can be traced back from a multi-dimensional coordinate system to a two-dimensional or three-dimensional dependency without loss of meaningfulness and sensitivity of the model.
  • the selected influencing variables and / or characteristic variables or their substitute variables are compared with the corresponding influencing variables and / or characteristic variables or the substitute variables transformed therefrom measured in the real process and the mutual dependencies of the influencing variables are updated.
  • This update can take place continuously, but preferably takes place at selected intervals in order to rule out that systematic errors in the production process or the measurement process are included in the update.
  • the advantage of the invention lies in the fact that the model of the process to be carried out, including the product, is continuously updated with the actually running process and is described by only a few influences and parameters with previously defined and preferably continuously updated interdependency. It is therefore possible to include the process directly in product planning and production planning and to adapt it accordingly; in other words: in the computer that controls the real process, a virtual process runs simultaneously, taking into account the previously selected influencing variables and parameters. There is a constant comparison between the real process situation and the virtual process situation. This means that particular attention must be paid to the selection of suitable sensors that are able to reproduce the real process data, ie influencing variables and parameters. and on the other hand the real process is controlled as a function of the setpoints predetermined by the virtual process.
  • the invention is illustrated by examples and by means of the figures
  • Production processes in this invention are:
  • a production process through which raw and auxiliary materials are converted into a desired end product, can be represented by a schematic model which, based on the set physical or chemical parameters of the product and / or the production process, the disturbance variables (such as air humidity, Air temperature, air pressure, electrical voltage and the like) and is represented by the monitored parameters (e.g. temperatures, pressures).
  • the control of this process is usually done by manually or automatically filing a certain number of variable parameters that are believed to represent the production process and are critical to the production process and / or the product. It is precisely these variable parameters that are appropriately visualized and statistically evaluated, e.g. through alarm signals, averaging, trend formation, etc., should allow the operator to master the control of the process and consequently also the possible variations of the end product and thereby adapt to the changing market requirements.
  • Production systems of this type can today be represented as virtual systems in an orthogonal coordinate system with the aid of "Computer Integrated Manufacturing” (C1M).
  • C1M Computer Integrated Manufacturing
  • each area of an automatic control to be executed is to be regarded as an autonomous functional area at the respective level of the CIM, intended to be fully integrated as part of a uniform automation system.
  • planning is carried out from top to bottom and implementation from bottom to top, so that the automation develops in steps, but in a uniform framework, i.e. H. can be planned and carried out.
  • a plant for the production of continuous, synthetic fibers is shown in a diagram with the process stages: storage of the polymer chips, drying of the polymer, spinning, winding, automatic transport of the bobbins, intermediate storage, selection and packaging (not shown).
  • the process therefore consists on the one hand of a continuous and on the other hand of a discontinuous process, the weighting of both process components being 50:50.
  • the functional representation of this process according to the GIM principles only the first two levels are present (represented).
  • the levels of the continuous process are shown on the left and the levels of the discontinuous process on the right.
  • the discontinuous process relates in particular to the automated handling of the product (the Do the washing up).
  • a further subdivision with the subsections was made on level 1 in terms of engineering considerations of the functions
  • the representation appears to be relatively meaningless. Only data, but no information, are available, the data being the values signaled point by point by the display devices, e.g. As temperature, the sum of interrelated data can be referred to as information.
  • the data that accrue in level 1 of industrial practice can also represent some typical information, such as temporal data, trends, average values, extreme values; however, this information is not sufficient to identify and pursue the correct process strategy. So far, the data and the information compiled from it have only been used to generate an alarm signal and to trigger the necessary measures to deal with the alarm (management and alarm). It is clear here that the functions shown in FIG. 2 on the higher levels II, III and IV, namely in particular:
  • Material, warehousing, logistics, procurement can still only be perceived by humans and this human component of automation must be further developed in order to enable the producers to control the production plant.
  • the invention provides that the overall process is recorded in a model and that this model is stored in the process control and evaluated to control the process. For this purpose, it is first determined which physical and / or chemical parameters of the product and / or the production process characterize the product or the process so precisely that the quality of the product is within the specification and the quantity required is within the specified quantities. These parameters are called parameters. These parameters are influenced by adjustable and non-adjustable product parameters and / or process parameters. These so-called influencing variables must also be determined in detail.
  • FIG. 8 shows the measured values of an influencing variable X and a characteristic variable Y in a two-dimensional coordinate system. It was found in the test series that when different values for X are set, the measured values for Y are in a narrow range with a longitudinal extension. To reduce the two-dimensionality, the measured values can now be transformed into another coordinate system. For this purpose, an axis of the new coordinate system, namely the coordinate XX through the area in the direction of the longitudinal extent, that is z. B. placed on the median value of the measured values. This gives a new component of the process, which is referred to here as XX.
  • This component XX is no longer dependent on the other component YY which is perpendicular thereto and also not — in three-dimensional space — on the vertical component ZZ.
  • the components transferred into this coordinate system are no longer identical to the original parameters or their measured values, but are connected to them via the directional components of the new coordinates with respect to the original coordinates.
  • the other influencing variables or parameters are to be loaded (loaded) with the direction components of the new coordinates.
  • the parameter Y does not occur in its dependence on the one-part size X, or only with its scatter in relation to the direction assumed for the new coordinate system, e.g. Median appearance.
  • Fig. ⁇ shows in a three-dimensional coordinate system such measurement values for a product, which represent the properties x, y and z of the product with two different setting variables. It can be seen that the majority of the measured values each lie in two clouds, each of which forms a narrow area with a clear longitudinal extension. The directional component of this longitudinal extension has a spatial position in the direction of each of the coordinates. The dimensionality is now reduced by transforming the measured values or analysis values (depending on what is shown in the diagram) to a new main axis of a new coordinate system, which lies in the longitudinal extent, for example on the median of one of the clouds.
  • the zero point of the new coordinate system is preferably placed on the longitudinal extension of the other cloud, so that both clouds lie with their longitudinal extension in one of the coordinate planes.
  • the original parameters are transformed into new components that are loaded with the directional components of the new coordinate system.
  • principal component analysis see e.g. Christian Holm, Principal Components & Multidimensional Fit, in http://rkb.home.cern.ch
  • a neural network can also be formed which, on the basis of the determined dependencies, allows, in the absence of a mathematical model, the generation of movements within this neural network itself.
  • the mathematical model or this neural network is continuously updated on the basis of experience, since the solutions determined by the network due to a change are constantly monitored using suitable measurements within the production process and the corrections are in turn stored within the mathematical model or within the neural network and saved and subsequently taken into account and evaluated accordingly.
  • this update is not carried out continuously, but is initiated in each individual case. It must be avoided that errors in the production process are included in the model process. If e.g. B. a part has become defective in the production process, e.g. B. a sensor delivers incorrect measured values, this error must not enter into the model process in the sense that a different kind of dependency is stored in the model process.
  • level 5 shows the levels 2 and 3 with the additional automation elements created by this invention, networking of the data flow, the information obtained therefrom and the resulting ones Control options shown again.
  • These are in particular the following information complexes of level 2 obtained from the data of levels 0 and 1 and the following strategic decision elements resulting from this on level 3:
  • this matrix serves as the basis for controlling the process as a whole and the end product obtained from it in a control loop and for automatically optimizing it according to predefined rules and at specified times by constantly adding and updating the dependency structure of the influencing variables laid down in the matrix ,
  • the invention with its individual elements is shown again schematically in FIG.
  • a series of tests on the real process and the product being produced must be carried out using a suitable selection of sensors to determine a grid of the production parameters, ie the influence sizes and the parameters. From these production parameters, those are selected that have the greatest influence on the quality of the product and / or the process.
  • the production parameters or a group of production parameters or a plurality of groups of production parameters are each transformed into a different coordinate system in which at least one dependency ratio has been eliminated.
  • This network of dependencies is specified as a model for a process computer.
  • This process computer makes it possible to make a prediction of the result, in particular the quality of the process or product, for each planned input value on the basis of the fed-in parameters and dependency.
  • the prediction provided by the model takes into account a large number of influencing variables and parameters in the previously entered complexity and operating ranges.
  • the invention thus represents a departure from the previously customary method in which the individual measured variables (influencing variables or parameters) are isolated and incorporated into the network of action and counteraction of all parameters regardless of the embedding of this parameter.
  • This prediction by the model allows the influencing variables that were set for the desired prediction result in the model to be given to the real process.
  • the results of the real process now supplied by the sensors can be compared with the results with the prediction of the model continuously or at time intervals. This in particular makes it possible to set up a control loop for the real production process and Adjust the input (input data) so that the actual result and the prediction result match as closely as possible.
  • the comparison of the prediction result and the actual result can also give reason to check the plausibility of the comparison result.
  • the model contains a module that specifies the limits within which a deviation between the model result and the real result can be expected. If the defined area of plausibility is left, an intervention in the real process is necessary in order to remedy any errors. A violation of the plausibility exists in particular if the comparison result has a trend over time, i. H. grows continuously, even though the input (input data) remains constant.
  • this block can also define the influence that input changes may have. If certain values that have to be determined beforehand are exceeded here, in addition to checking the real process, a check of the model is also necessary in order to determine whether all influencing variables and parameters have been taken into account in a weighting that corresponds to reality.

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  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • General Factory Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Procédé permettant l'exécution d'un processus de production automatisé, selon lequel le processus de production est commandé en fonction de la mesure de paramètres physiques ou chimiques du produit et / ou du processus de production, ainsi que de grandeurs de perturbation (paramètres de produit / de processus). Les grandeurs qui caractérisent de manière suffisamment précise la qualité et / ou la quantité du produit et / ou du processus de production et les paramètres de produit / processus pouvant être ajustés, ou ne pouvant pas être ajustés mais cependant modifiés, qui ont une influence sur les grandeurs caractéristiques (grandeurs d'influence) sont déterminés. Grâce à l'évaluation de modifications voulues ou accidentelles des grandeurs d'influence, la dépendance mutuelle entre les grandeurs d'influence est déterminée et mise en mémoire en temps que modèle de dépendance (réseau neuronal). Le modèle de dépendance permet le calcul et le réglage des grandeurs caractéristiques optimales et des grandeurs d'influence réglables associées. Le modèle de dépendance (réseau neuronal) est mis à jour automatiquement en continu par évaluation des données de processus résultant de la production.
PCT/EP2001/009414 2000-08-17 2001-08-15 Procede permettant l'execution d'un processus de production automatise Ceased WO2002014967A2 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
DE10040731.5 2000-08-17
DE10040731A DE10040731A1 (de) 2000-08-17 2000-08-17 Verfahren zur Durchführung eines automatisierten Produktionsprozesses
DE10057346 2000-11-19
DE10057346.0 2000-11-19

Publications (2)

Publication Number Publication Date
WO2002014967A2 true WO2002014967A2 (fr) 2002-02-21
WO2002014967A3 WO2002014967A3 (fr) 2002-12-27

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102004046217A1 (de) * 2004-09-22 2006-04-06 Daimlerchrysler Ag Verfahren zur Analyse eines Produktionssystems
DE102005006575A1 (de) * 2005-02-11 2006-09-07 Battenberg, Günther System zum Erfassen und Bewerten bedienungsabhängiger Vorgänge und/oder Komponenten in automatischen Produktions- und Prüfabläufen
DE10208044B4 (de) * 2002-02-25 2008-10-09 Infineon Technologies Ag Verfahren und Anordnung zum Überwachen eines Herstellungsprozesses
CN110873699A (zh) * 2018-08-30 2020-03-10 广东生益科技股份有限公司 粘结片的在线质量控制方法、装置、系统和存储介质
CN111615672A (zh) * 2018-01-15 2020-09-01 科思创德国股份有限公司 用于改进化学品生产过程的方法
WO2021094272A1 (fr) * 2019-11-14 2021-05-20 Sms Group Gmbh Procédé et système de détermination de corrélations entre des défauts de produit détectés et des variables d'état détectées d'un système de production
CN113303489A (zh) * 2021-06-02 2021-08-27 红云红河烟草(集团)有限责任公司 一种制丝过程烟叶水分准确控制的方法
US11308245B2 (en) 2019-01-08 2022-04-19 Caterpillar Inc. Systems and methods for facilitating evaluation of characteristics related to quality

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5305221A (en) * 1990-05-04 1994-04-19 Atherton Robert W Real world modeling and control process for integrated manufacturing equipment
US5586021A (en) * 1992-03-24 1996-12-17 Texas Instruments Incorporated Method and system for production planning
US5646870A (en) * 1995-02-13 1997-07-08 Advanced Micro Devices, Inc. Method for setting and adjusting process parameters to maintain acceptable critical dimensions across each die of mass-produced semiconductor wafers
DE69722612D1 (de) * 1997-09-04 2003-07-10 Univ Groningen Methode zur Modellierung und/oder Steuerung eines Herstellungsverfahrens, die ein neuronales Netz anwendet und Regler für ein Herstellungsverfahren

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10208044B4 (de) * 2002-02-25 2008-10-09 Infineon Technologies Ag Verfahren und Anordnung zum Überwachen eines Herstellungsprozesses
DE10208044B8 (de) * 2002-02-25 2009-01-22 Infineon Technologies Ag Verfahren und Anordnung zum Überwachen eines Herstellungsprozesses
DE102004046217A1 (de) * 2004-09-22 2006-04-06 Daimlerchrysler Ag Verfahren zur Analyse eines Produktionssystems
DE102005006575A1 (de) * 2005-02-11 2006-09-07 Battenberg, Günther System zum Erfassen und Bewerten bedienungsabhängiger Vorgänge und/oder Komponenten in automatischen Produktions- und Prüfabläufen
CN111615672A (zh) * 2018-01-15 2020-09-01 科思创德国股份有限公司 用于改进化学品生产过程的方法
CN110873699A (zh) * 2018-08-30 2020-03-10 广东生益科技股份有限公司 粘结片的在线质量控制方法、装置、系统和存储介质
US11308245B2 (en) 2019-01-08 2022-04-19 Caterpillar Inc. Systems and methods for facilitating evaluation of characteristics related to quality
WO2021094272A1 (fr) * 2019-11-14 2021-05-20 Sms Group Gmbh Procédé et système de détermination de corrélations entre des défauts de produit détectés et des variables d'état détectées d'un système de production
CN113303489A (zh) * 2021-06-02 2021-08-27 红云红河烟草(集团)有限责任公司 一种制丝过程烟叶水分准确控制的方法
CN113303489B (zh) * 2021-06-02 2022-08-12 红云红河烟草(集团)有限责任公司 一种制丝过程烟叶水分准确控制的方法

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