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WO1996010220A1 - Procede conçu pour influer sur des processus cycliques - Google Patents

Procede conçu pour influer sur des processus cycliques Download PDF

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Publication number
WO1996010220A1
WO1996010220A1 PCT/DE1995/001346 DE9501346W WO9610220A1 WO 1996010220 A1 WO1996010220 A1 WO 1996010220A1 DE 9501346 W DE9501346 W DE 9501346W WO 9610220 A1 WO9610220 A1 WO 9610220A1
Authority
WO
WIPO (PCT)
Prior art keywords
determined
properties
characteristic
machine
raw material
Prior art date
Application number
PCT/DE1995/001346
Other languages
German (de)
English (en)
Inventor
Karl Hehl
Michael Manfred Gierth
Oliver Kay Wybitul
Original Assignee
Arburg Gmbh & Co.
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
Application filed by Arburg Gmbh & Co. filed Critical Arburg Gmbh & Co.
Publication of WO1996010220A1 publication Critical patent/WO1996010220A1/fr

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • 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
    • B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
    • B29C45/17—Component parts, details or accessories; Auxiliary operations
    • B29C45/76—Measuring, controlling or regulating
    • B29C45/766—Measuring, controlling or regulating the setting or resetting of moulding conditions, e.g. before starting a cycle
    • 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
    • G05B2219/32188—Teaching relation between controlling parameters and quality parameters
    • 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
    • G05B2219/32194—Quality prediction
    • 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
    • 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
    • G05B2219/45—Nc applications
    • G05B2219/45244—Injection molding
    • 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
    • G05B2219/49—Nc machine tool, till multiple
    • G05B2219/49061—Calculate optimum operating, machining conditions and adjust, adapt them
    • 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 influencing cyclically running processes according to claim 1.
  • the method can preferably be used in the plastics processing and metalworking industries, for example in connection with plastic injection molding machines, blow molding machines, die casting machines, aluminum die casting machines, but also in connection with presses or welding equipment, provided that cyclical processes are carried out.
  • Process key figures are discrete numerical values which characterize the respective process curve course within the individual process phases (correlation coefficient, extrema, integrals, mean values, etc.). The respective process key figures can thus be correlated with the associated quality features in a mathematical-statistical form.
  • QM f (PKZ)
  • DE-A 35 05 554 discloses a method for examining pressure profiles in order to automatically determine changes in the pressure profile which, when certain threshold values are exceeded, give a signal for a change in the switching times, e.g. between the injection phase and holding pressure phase for the following cycle.
  • a signal for a change in the switching times e.g. between the injection phase and holding pressure phase for the following cycle.
  • DE-A 4002398 relates various temperatures in the area of the machine to a temperature of a manufactured part, the temperature of the part in turn having certain properties of the Partly connected. Although this can speed up the set-up of the machine, expert knowledge is still required so that the temperature of the part actually corresponds to certain characteristic properties. This means that no analytical relationships between process variables and properties are established.
  • a weight regulator is also known, in which the actual weight of injection molded parts determined after the injection molding process cast parts compared to a target weight is selected as a control variable for the holding pressure in the holding pressure phase of the injection molding machine. There is no optimization.
  • a method is also known from EP-B 455 820, in which countermeasures are initiated on the basis of stored expert knowledge when defective parts occur.
  • the countermeasures are the stored empirical experiences of an expert, so that a partially specific change depending on the actually occurring conditions is not possible.
  • an optimal working point is determined iteratively by simulation methods.
  • the relationships between machine setting variables, process parameters and characteristic properties are not dealt with, instead an expert system is provided, which is usually an optimization of the working point as carried out empirically by an experienced adjuster.
  • Process parameters for individual cycle sections are determined in EP-A 457 230. However, there is no correlation between the individual parameters.
  • the present invention is based on the object of optimizing a method of the type mentioned at the outset, taking into account the complex relationships between individual method parameters and the properties of the parts to be produced. This object is achieved by a method having the features of claim 1.
  • characteristic properties initially being understood to mean properties of the manufactured parts, that is to say any, attributive (e.g. burner) or variable (e.g. dimensions, weight) properties.
  • this also includes minimizing the reject rate, which is influenced by the characteristic properties of the products.
  • the characteristic properties of the manufactured products also include economic aspects, such as a stable course of the manufacturing process or a minimization of the cycle time while maintaining certain boundary conditions of the processes.
  • the raw material properties also influence the characteristic properties, so that e.g.
  • the regranulate content of recycled plastic material can be decisive for the properties of the end product.
  • the indirect relationship between machine setting parameters and characteristic properties via the process key figures can be determined in the course of the method or can be specified with knowledge of this relationship, for example by previous manufacturing processes. Based on the result, an optimal operating point for the operation of the machine can then be determined.
  • the method and the device are explained below using an injection molding machine, preferably an injection molding machine for processing plastic materials, such as plastics or powder materials.
  • an injection molding machine for processing plastic materials, such as plastics or powder materials.
  • the method can also be used without problems in other areas in which cyclical processes are used for the production (master shaping) or processing (shaping) of products.
  • the method is basically divided into a test phase T and a production phase P.
  • a working point is preferably also set on the machine at the beginning of the test phase.
  • An operating point is understood to be an adjustment of the machine setting variables, which in the first place allows the production of products at least in a reasonably reasonable manner.
  • sampling is then carried out, which can also be carried out systematically according to a sampling plan.
  • the essential machine setting variables MG can be varied (step S1).
  • Raw material properties RE such as the proportion of regranulate, can also be varied in order to determine the influence of the raw materials on the characteristic properties.
  • Characteristic properties are any, attributive (eg burner) or variable (eg dimensions, weight) properties, but can also be process stability, cycle time, reject rate or costs.
  • Process curve profiles include not only the immediately resulting curve profiles, e.g. Understand pressure-time, pressure-path or temperature-time profiles, but also the process curve profiles that can be calculated from them, e.g. Derivatives, integrals and the like.
  • step S3 the process curve profiles PKV recorded in step S3 are broken down into variable process phases PPH resulting as a function of the respective process curve profiles, so that they can be described in sections (step S4).
  • process curve profiles such as temperature profiles, it may not be necessary to break them down into process phases, since they are largely linear, for example.
  • the entire process curve is then viewed as one process phase.
  • process key figures PKZ which are determined on the basis of known analysis of variance and regression, neural networks or similar analyzes (step S5).
  • process curve profiles PKV within individual process phases PPH is carried out using discrete individual values (process key figures PKZ), which are essentially based on the rules of differential calculation (absolute and local extremes, turning points, integrals, mean values, ).
  • FIG. 3 the mold internal pressure Pwi is initially plotted over time t.
  • the mold pressure sensor receives a first pressure signal at time t1, which then rises gradually until the tool is filled in the area of the lower processing limit UVG.
  • the pressure then rises continuously until the maximum mold internal pressure Max (Pwi) is reached at the upper processing limit OVG.
  • the mold internal pressure then gradually drops, which can result in a different drop, as a comparison of FIGS. 3 and 4 shows.
  • the mold internal pressure Pwi is also plotted there over time t.
  • the hydraulic pressure Phy and the screw travel s are also plotted during each injection cycle.
  • the lower processing limit UVG and upper processing limit OVG are the beginning and end of the spray cycle.
  • various process key figures PKZ can be determined in this area, such as:
  • Screw path s (Pwi 0VG), in which the cavity pressure is the upper one
  • AVG (ds / dt) mean value of the first derivative of the screw path over time
  • a process model vector is then determined with this key figure vector.
  • this process model vector is represented as a straight line equation as follows, which now allows a relationship between the characteristic property QM and the process characteristic number. In the simplest case, this results e.g. to the following process model vector
  • step S10 certain characteristic properties such as dimensional accuracy, weight, process stability, cycle time or the like are entered as the predetermined characteristic property Q M Soll (step S10).
  • an optimal operating point can now be determined as a function of the predefined characteristic property while observing the predefined characteristic property QMs 0 n (step S12).
  • the tolerances dQM zu ] are specified for the characteristic properties in step S11. However, these are not tolerance bands for process curves, but tolerances of the characteristic properties QM. If an optimal working point cannot be reached because, for example, due to linear or non-linear dependencies between different characteristic properties QM or machine setting variables MG, the change to achieve an optimum at the same time if other properties deteriorate (step S13), an optimizer 19 tries to achieve a sub-optimum as a compromise solution based on the relationship between characteristic properties, process parameters and machine setting variables (step S14).
  • the optimizer therefore tries to determine an almost optimal working point AP su b while adhering to the tolerances dQM for all predetermined characteristic properties Q s 0 n using known analysis of variance, regression, neural networks or similar methods. If this almost optimal operating point can be determined, the various setting variables can be set accordingly (step S15). However, if this operating point cannot be determined, system changes are determined based on the known relationship, changes are made immediately or proposed by the control to the user, so that a working point AP can be achieved while maintaining the tolerances of all the predetermined characteristic properties (step S16).
  • step S10 can also be given machine setting MGs 0 i or more machine setting MG which must be optimized to] due to the known relationship and the DQM into account the tolerances in step S10.
  • Limit values dMG to ⁇ for machine setting variables MG or permissible deviations dPKZ to ⁇ can also be specified in step S11, so that an optimum for operating the machine is determined taking into account multiple dependencies.
  • step S7 an evaluation corresponding to steps S3, S4, S5 is preferably carried out cycle-synchronously, ie series process curve profiles SPKV are also continuously recorded (step S7).
  • series process curve profiles are preferably broken down cycle-synchronously into series process phases SPPH (step S8) and series production key figures SPKZ are determined therefrom for production, which identify the series process curve profiles and are related to certain characteristic properties (step S9).
  • These specific characteristic properties SQM can be determined on-line, for example by calculation; but they can also be determined off-line, for example, in an analogous manner, even if they are costly.
  • the resulting calculable characteristic property (s) SQM Der can be determined in a known manner (step S17). This can be used to determine a new optimum working point APn for each cycle (step S18), so that an adjustment of the Machine setting variables MG can be made again for each cycle. If you want to further automate this process, the machine setting variables MG can be set automatically in order to achieve the working points AP opt , AP SUD , AP, APn (step S 19). It is even possible to set up a closed control loop in that the machine setting variables MG are automatically returned so that, depending on the optimal operating point determined, the machine setting variables are automatically set directly by the machine.
  • step S6 Furthermore, based on the function determined in step S6
  • the machine parameter that has the most lasting influence on reducing the difference value is selected on the basis of the process characteristic number (s) without exerting a negative influence on other process parameters.
  • the process key figures are then defined and also calculated within these process phases, which, as already explained above, allow a correlation to the characteristic properties QM.
  • the key process indicators that can be determined with the mostly existing sensor system of the machine have been found to be those determined from the following process curve profiles, which initially represent a process indicator vector which must then be evaluated as already explained above:
  • a cycle during the production of molded parts on an injection molding machine will first be described here by way of example.
  • material is fed continuously to the injection molding unit 30 of an injection molding machine, which material is first injected into a mold cavity 32 via injection means 31 during an injection phase.
  • the mold cavity is initially further pressurized by axial movement of the injection means during a compression phase.
  • the control of the injection molding machine then usually switches over to a pressure control which regulates the holding pressure during a holding pressure phase, which is necessary for obtaining dimensionally stable molded parts.
  • the injection molded part solidifies and then forms into the finished molded part during the formation phase.
  • the process key figures PKZ, SPKZ of the above process key figure vector are formed within the following process areas:
  • FIG. 1 shows a device which is suitable for carrying out the method.
  • An injection molding unit 30 injects material into an mold cavity 32 of an injection mold 34 in a mold closing unit 33 via an injection means 31 such as, for example, a screw conveyor or an injection piston.
  • the device has sensor means 11, 12 such as an internal mold pressure sensor and a temperature sensor in the area of the injection molding unit, via the sensor means a multiplicity of process parameters are determined during each process, that is to say during each cycle, and means 13 for recording process curve profiles PKV become the process parameters during of a process.
  • the device has input means 14 which enable various values to be input into storage means 16.
  • characteristic properties QM of the products manufactured during this phase determined by conventional methods during the sampling phase. However, some of these values can also be determined automatically and fed directly into the device. However, certain nominal values of predetermined characteristic properties QM S0 -n, which are characteristic of a nominal value vector of the characteristic properties, for example, must also be entered or read in from data media.
  • the device comprises means 15 for determining a relationship between the characteristic properties QM, SQM and the process curve profiles PKV or series process curve profiles SPKV.
  • the process curves are broken down into the process phases, preferably numerically, according to the specifications made above.
  • the process key figures PKZ and series process key figures SPKZ are determined, which are correlated with the characteristic properties through this relationship.
  • the relationship determined in the test phase is stored in storage means 16.
  • the machine setting variables MG can be influenced via adjusting devices 17, 18.
  • the setting device 18 influences Adjustment of a control valve 36, for example the pressure of a piston-cylinder unit driving the screw conveyor and thus the internal pressure of the tool, which is detected by the sensor means 11.
  • the setting device 17 influences the heating tapes 35 supplied current and thus the temperature, which is detected by the sensor means 12.
  • An optimizer 19 determines an optimal working point AP opt on the basis of the determined relationship between characteristic properties and machine setting variables and taking the specifications into account. If this optimal working point cannot be reached due to the boundary conditions, the optimizer 19 tries to determine an almost optimal working point AP ⁇ ) - ,. If this is also not possible, he suggests system changes or, if possible, makes these system changes himself. Depending on the determined operating point, the setting devices 17, 18 are then actuated.

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  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Mechanical Engineering (AREA)
  • Injection Moulding Of Plastics Or The Like (AREA)

Abstract

L'invention concerne un procédé conçu pour influer sur des processus cycliques. Après détermination, par des essais, des propriétés caractéristiques de produits fabriqués au cours de la phase d'essai, et après détermination simultanée d'une pluralité d'allures de courbes de processus, ledit procédé consiste à établir une relation entre les chiffres caractérisant les allures de courbes de processus et les propriétés caractéristiques et/ou les propriétés des matières premières des pièces. Une autre solution consiste à déterminer cette relation au préalable. Lorsque certaines propriétés caractéristiques souhaitées (QMsoll) et des tolérances admissibles sont prédéterminées, il est possible de déterminer les chiffres significatifs pour les propriétés caractéristiques afin de détecter ainsi les valeurs de réglage de la machine qui déterminent un point de fonctionnement optimal de la machine. Ceci permet d'optimiser le procédé en tenant compte des relations complexes entre les différents paramètres de procédé et les propriétés des pièces à fabriquer.
PCT/DE1995/001346 1994-09-28 1995-09-28 Procede conçu pour influer sur des processus cycliques WO1996010220A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DEP4434654.9 1994-09-28
DE4434654A DE4434654C2 (de) 1994-09-28 1994-09-28 Verfahren zur Beeinflussung zyklisch ablaufender Prozesse

Publications (1)

Publication Number Publication Date
WO1996010220A1 true WO1996010220A1 (fr) 1996-04-04

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WO (1) WO1996010220A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6670399B2 (en) 1999-12-23 2003-12-30 Neurochem (International) Limited Compounds and methods for modulating cerebral amyloid angiopathy
US7101879B2 (en) 2000-11-03 2006-09-05 Massachusetts Institute Of Technology Treatments for neurotoxicity in Alzheimer's Disease
CN115136136A (zh) * 2020-03-23 2022-09-30 杰富意钢铁株式会社 产品信息确定方法、制造方法、系统以及产品信息确定装置

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DE19834797C2 (de) * 1998-08-01 2002-04-25 Christian Kuerten Verfahren und Vorrichtung zur zustandsabhängigen Prozeßführung bei der Verarbeitung von Kunststoffen
WO2001067193A2 (fr) * 2000-03-06 2001-09-13 Siemens Aktiengesellschaft Dispositif et procede pour introduire des parametres pour machines et pour effectuer des simulations et des observations
DE10119853A1 (de) * 2001-04-24 2003-01-09 Bayer Ag Hybridmodell und Verfahren zur Bestimmung von mechanischen Eigenschaften und von Verarbeitungseigenschaften eines Spritzgiessformteils
US6914537B2 (en) 2001-05-25 2005-07-05 Toshiba Machine Co., Ltd. Method for monitoring operation data of an injection-molding machine
DE102006009947B4 (de) * 2006-03-03 2009-05-07 Aweba Werkzeugbau Gmbh Aue Gussform sowie Vorrichtung und Verfahren zum Überwachen einer Gussform
DE102006031268A1 (de) 2006-07-06 2008-01-10 Krauss Maffei Gmbh Vorrichtung und Verfahren zur benutzerspezifischen Überwachung und Regelung der Produktion
DE102010002174A1 (de) * 2010-02-22 2011-08-25 Robert Bosch GmbH, 70469 Verfahren zur Regelung eines Spritzgießprozesses
DE102012200568A1 (de) * 2012-01-16 2013-07-18 Oskar Frech Gmbh + Co. Kg Steuerungsvorrichtung für Gießkolbenvorschubbewegung
IT201900005646A1 (it) * 2019-04-12 2020-10-12 Inglass Spa “Metodo implementato via software per elaborare risultati di una simulazione realizzata con software di analisi agli elementi finiti”

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EP0167631A1 (fr) * 1983-12-28 1986-01-15 Fanuc Ltd. Machine de moulage par injection
US4816197A (en) * 1988-04-12 1989-03-28 Hpm Corporation Adaptive process control for injection molding
EP0418398A1 (fr) * 1989-03-28 1991-03-27 Fanuc Ltd. Appareil utilise dans des machines de moulage a injection, pouvant differencier des produits acceptables de produits a rejeter
WO1991014562A1 (fr) * 1990-03-28 1991-10-03 Moldflow Pty. Ltd. Commande de machine de moulage a injection
EP0566738A1 (fr) * 1990-11-30 1993-10-27 Fanuc Ltd. Procede de reglage des conditions d'un moulage par injection
WO1993004839A1 (fr) * 1991-09-12 1993-03-18 Engel Maschinenbau Gesellschaft M.B.H. Procede de commande d'une machine de production, en particulier une machine de moulage par injection

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6670399B2 (en) 1999-12-23 2003-12-30 Neurochem (International) Limited Compounds and methods for modulating cerebral amyloid angiopathy
US7101879B2 (en) 2000-11-03 2006-09-05 Massachusetts Institute Of Technology Treatments for neurotoxicity in Alzheimer's Disease
CN115136136A (zh) * 2020-03-23 2022-09-30 杰富意钢铁株式会社 产品信息确定方法、制造方法、系统以及产品信息确定装置

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DE4434654A1 (de) 1996-04-04

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