WO2011048231A1 - Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils - Google Patents

Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils Download PDF

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Publication number
WO2011048231A1
WO2011048231A1 PCT/ES2009/070453 ES2009070453W WO2011048231A1 WO 2011048231 A1 WO2011048231 A1 WO 2011048231A1 ES 2009070453 W ES2009070453 W ES 2009070453W WO 2011048231 A1 WO2011048231 A1 WO 2011048231A1
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WO
WIPO (PCT)
Prior art keywords
compensating
ram
falls
machine tools
network
Prior art date
Application number
PCT/ES2009/070453
Other languages
English (en)
Spanish (es)
Inventor
Andrés BUSTILLO IGLESIAS
Maritza Correa Valencia
Rodolfo Elias Haber Guerra
Original Assignee
Universidad De Burgos
Consejo Superior De Investigaciones Científicas
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 Universidad De Burgos, Consejo Superior De Investigaciones Científicas filed Critical Universidad De Burgos
Priority to ES201290001A priority Critical patent/ES2398814B1/es
Priority to CN2009801610844A priority patent/CN102483622A/zh
Priority to PCT/ES2009/070453 priority patent/WO2011048231A1/fr
Priority to DE112009005232T priority patent/DE112009005232T5/de
Publication of WO2011048231A1 publication Critical patent/WO2011048231A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q11/00—Accessories fitted to machine tools for keeping tools or parts of the machine in good working condition or for cooling work; Safety devices specially combined with or arranged in, or specially adapted for use in connection with, machine tools
    • B23Q11/001—Arrangements compensating weight or flexion on parts of the machine
    • B23Q11/0028—Arrangements compensating weight or flexion on parts of the machine by actively reacting to a change of the configuration of the machine
    • 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
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia

Definitions

  • the present invention is encompassed in the field of machine tools and specifically in those consisting of a ram, such as milling machines.
  • Said invention is an intelligent device and the method using said device to compensate for ram falls through the use of probabilistic calculation, in particular by means of Bayesian networks, and an internal model control, specifically a neuro-fuzzy adaptive control device. .
  • X axis is the longitudinal axis of the bench
  • Y axis is the longitudinal axis of the column
  • Z axis is the longitudi nal axis ⁇ ram.
  • the present invention is charac ⁇ terizada established and in the independent claims, mien ⁇ after the dependent claims describe other characteristics of the same.
  • the present invention relates to a Inteli device ⁇ people for compensation falls ram in machine tools comprising the following ele ⁇ ments: a machine tool with computer numerical control (CNC), means for the application of probabilistic calculation and means for control by internal model.
  • CNC computer numerical control
  • the present invention relates to a method for compensating ram falls on machine tools using an intelligent device according to claim 1 comprising the following steps:
  • Figure 1 represents a perspective of a machine tool.
  • Figure 2 represents the structure of a Bayesian network augmented by tree of the variables.
  • Figure 3 shows a diagram of smart vo ⁇ dispositi showing paths exchange information between elements forming said dispo ⁇ sitivo.
  • an intelligent device is described and the procedure carried out therein by means of a calculation system that allows to achieve the optimum compensation of the ram's fall (1.1).
  • Said calculation system is based on a distributed system and prediction calculation ⁇ ten sion of compensating cylinders and control machinable Zado.
  • the elements forming the device intelli ⁇ people are:
  • CNC Computer Numerical Control
  • the machine tool specifically milling machine, is responsible for the final milling or finishing of the outer surface of the ram (1.1) that will be incorporated into other milling machines.
  • the CNC (1.3) is responsible for starting up the smart device by means of a virtual key programmed in the programmable logic controller (PLC, acronym of the English name "Programmable Logic Controller" (1.2) of the machine.
  • PLC programmable logic controller
  • This virtual key must be pressed by the operator when a ram (1.1) has been placed for final machining on this machine. Then the virtual key sends to the workstation (2) the machining program from which extract the values of the variables preci ⁇ sa for calculating voltage cylinders com ⁇ thinkers and so starts the intelligent device presented here.
  • the CNC (1.3) is responsible for, during the machining of the ram (1.1), modify in real time the machining conditions according to the variation proposed by the neuro-fuzzy adaptive control device (3).
  • CNC (1.3) After starting the intelligent device CNC (1.3) receives from the workstation (2) ten ⁇ sion (TCC) the operator must apply to cilin ⁇ compensators ders before machining and sample the operator in his screen. If the workstation (2) cannot discern the appropriate voltage, the CNC (1.3) shows the operator the probabilities for each type of CBT so that it is he who makes the decision of the voltage to be applied.
  • the workstation (2) with Bayesian Networks application provides the voltage value to apply to compensating cylinders (TCC) based on known variables. This value allows indi ⁇ TCC uprightly obtain the appropriate curvature of the ram (1.1) to be machined, as removing the compensating cylinders after machining has previously applied the appropriate voltage, the ram (1.1) curves only.
  • the prediction of CBT is carried out based on the value of 9 variables that we call predictor variables.
  • the value of the predictor variables is obtained by the application of both the ram machining program (1.1) included in the CNC (1.3) of the machine and the Database (4) of Company Orders.
  • DAA active shock absorber
  • the network structure is built on the 9 predictor variables obtained before starting the machining process and a tenth variable, the CBT, called the class variable.
  • the 9 variables used in the network are characteristics of the manufacturing process, the design of the ram (1.1) and the subsequent operating conditions of the ram (1.1) to be machined.
  • FEM Finite Element Method
  • TCC is continuous variable defi nition ⁇
  • variables in a Bayesian network must have a discrete number of states, this variable is discretized according to the attached table below values.
  • a Laplace correction is applied to the Bayesian network to thereby assign a proportion of participation to cases that are not present at the time of training but may appear in the future.
  • Event or state of nature i (xi) ⁇ tua is whether in the future on which we want to know certain information.
  • a priori probability of a state x (p (xi)): probability that a state of nature i occurs from the initial information, that is to say without any evidence.
  • Study or sample information this is usually the extra information that can be obtained after conducting a study or test.
  • the results of a study or sample can be represented by different res ⁇ indicated.
  • the set of findings is called evidence and is usually noted as e.
  • Probability Aposteriori of xi given e (p (xi
  • e) P (e, x ⁇ )
  • Step 1 Calculate I (Xi, X j
  • C) with i ⁇ j; i, j l, ..., n.
  • Step 2 Construct a complete non-directed graph whose nodes correspond to the predictor variables: Xi, ..., X n . Assign each edge by connecting the variables Xi and X a weight given by I (Xi, X j
  • Step 3 Assign the two edges of greatest weight to the tree to be built.
  • Step 4 Examine the next edge of greater weight, and add it to the tree unless it forms a cycle, in which case the next edge of greater weight is discarded and examined. Repeat step 4 until seleccio ⁇ nar n-1 edges.
  • Step 5 Transform the tree undirected resul tant ⁇ one run, choosing a variable as root, to then direct the other edges.
  • Step 6 Build a TAN model by adding a no ⁇ do labeled as C and then an arc from C to each predictor variable X ⁇ .
  • the cross-validation method or K-fold cross-validation was used, according to its English name, and a file with data Experimental
  • the initial data set is divided into K subsets, of the K subsets a single subset is saved as validation data to test the model, and the other Kl subsets are used as training data.
  • the process is repeated K times, partitions, where each of the K subsets is used exactly once as validation data.
  • the results of the parti tions ⁇ averaged, or otherwise combined to produce a single estimate of the accuracy of classi ⁇ er, K 10 was taken.
  • the output of the model after validation is a contingency table or confusion matrix, which summarizes the results of data well and poorly classified ⁇ two for each state of the class and a general precision value that indicates the percentage of success with that the model will classify the new data presented to the network.
  • the application is ready for use, questions can be asked to the Bayesian network on different types of questions to find the posterior probability of the different possible answers.
  • the type of question is the razo ⁇ tioning predictive or causal inference where the prediction of effects sought.
  • the Bayesian network is asked "What is the probability of each state of the TCC class given certain manufacturing requirements?"
  • the manufacturing requirements will be the variables LMC, PCC, DAA, FAA, PAA, CIF, TCIF, ECC and PAAA already defined above.
  • the Ba ⁇ yesiana network would be asked about the probability of CBT according to the following values: P (TCC
  • the network calculates the following probabilities of CBT, expressed here as both by one: low voltage 0.02; average tension 0.89; medium-high voltage 0.09 and high voltage 0.00. With these requirements the states of the class with higher probabilities identify the best voltage to apply that corresponds to the average voltage since its probability is 89%. This reasoning is correct since it corresponds to values obtained in the experimental tests.
  • one of the TCC states is predominant, probability greater than 80%, it is transferred to the CNC (1.3) of the machine, if it is not, the probabilities ⁇ des for each state of the TCC class are warned to the operator for To analyze the situation.
  • the neuro-blurred adaptive control device (3) is the device that receives information from the machine's internal sensors (1.3) or, additionally, from external sensors installed in it. In addition, it monitors and modifies variables of the CNC (1.3) related to the execution of the part program such as the speed of advance and the speed of rotation in order to optimize the machining time and minimize the error due to the flexion of the ram (1.1 ).
  • This adaptive control device is a computer or an embedded control system connected to the CNC (1.3), in the specific example proposed here by a network medium, which sends the desired part program to the CNC (1.3) run.
  • this device receives information from the sensors included in the machine tool with its CNC (1) through the network medium.
  • This device implements a control mode ⁇ internally (BMI, which stands for the English name "Infernal Control Model”) based on neuro- fuzzy controllers for, from the tension applica ⁇ car is due to compensating cylinders and real-time machining conditions, modify the speed of advance or rotation of the milling machine that mechanizes the ram (1.1), that is, modify the machining conditions provided for in the machining program, sending the new conditions to the CNC for implementation .
  • BMI which stands for the English name "Infernal Control Model”
  • the controller is based on fuzzy logic and corpora ⁇ precontrol module based on networks neuron Ies (neuro-fuzzy controller).
  • the parameters defining the neuro-fuzzy controllers are DETERMI ⁇ nan so that minimizes an Index of merit.
  • Blurred logic is based on fuzzy sets, whose elements are associated with a membership function that indicates to what extent the element is part of that fuzzy set.
  • Forms functions qu ⁇ nence most typical are trapezoidal, triangular and siana gaus-. That is, the fuzzy logic is based on heuristic rules of the form IF (antecedent) THEN ( ⁇ sequent), where the antecedent and consequent are also fuzzy sets, either pure or result of operating with them.
  • the rules that determine the rennovation ⁇ nence of the elements of fuzzy sets is based, in the case of the ram (1.1) in the designer's experience and on information provided by the Bayesian networks.
  • the network medium is responsible for joining the three allowing the elements described above Chri ⁇ cation therebetween and minimizing the delay in communications between them.
  • Said network medium can be a field bus, belonging to the Profibus family, or others depending on the type of network: mpi, ethernet, internet, etc.
  • the network medium is characterized by the maximum delay that it introduces in the transmission of monitoring and control signals between the CNC (1.3) and the control medium, which will be called L mec iio re d -
  • This delay in preferred embodiments of the invention, is less than 60 seconds.
  • a preferred use of the network medium for the probabilistic calculation allows several elements to work at the same time for the execution of said calculation, so that the calculation time is minimized.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

La présente invention concerne un dispositif intelligent et le procédé faisant appel à ce dispositif pour compenser les chutes de bélier par utilisation d'un calcul probabiliste, c'est-à-dire au moyen de réseaux bayésiens, et d'un contrôle par modèle interne, c'est-à-dire au moyen d'un dispositif de commande adaptative neurofloue.
PCT/ES2009/070453 2009-10-23 2009-10-23 Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils WO2011048231A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
ES201290001A ES2398814B1 (es) 2009-10-23 2009-10-23 Dispositivo inteligente y procedimiento para la compensación de caídas de carnero en máquinas herramienta
CN2009801610844A CN102483622A (zh) 2009-10-23 2009-10-23 智能装置以及补偿机床冲头凹陷的方法
PCT/ES2009/070453 WO2011048231A1 (fr) 2009-10-23 2009-10-23 Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils
DE112009005232T DE112009005232T5 (de) 2009-10-23 2009-10-23 Intelligente Vorrichtung und Verfahren zur Kompensation des Werkzeugschieberdurchgangs beiWerkzeugmaschinen

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/ES2009/070453 WO2011048231A1 (fr) 2009-10-23 2009-10-23 Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils

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WO2011048231A1 true WO2011048231A1 (fr) 2011-04-28

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PCT/ES2009/070453 WO2011048231A1 (fr) 2009-10-23 2009-10-23 Dispositif intelligent et procédé pour la compensation de chutes de bélier dans des machines-outils

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CN (1) CN102483622A (fr)
DE (1) DE112009005232T5 (fr)
ES (1) ES2398814B1 (fr)
WO (1) WO2011048231A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102749850A (zh) * 2012-07-20 2012-10-24 富阳登城塑料机械有限公司 一种基于梯形图在间歇式预发机上运用模糊控制的实现方法

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112965441B (zh) * 2021-02-01 2022-03-15 新代科技(苏州)有限公司 一种控制器通讯延迟补偿方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5214592A (en) * 1989-10-19 1993-05-25 Toshiba Kikai Kabushiki Kaisha Machine tool position correcting method and apparatus
US20030187624A1 (en) * 2002-03-27 2003-10-02 Joze Balic CNC control unit with learning ability for machining centers
EP1659468A2 (fr) * 2004-11-16 2006-05-24 Rockwell Automation Technologies, Inc. Interface d'exécution universelle pour simulation à base d'agent et systèmes de contrôle
WO2009109673A1 (fr) * 2008-03-03 2009-09-11 Consejo Superior De Investigaciones Científicas Procédés de commande fondé sur une logique flou pour des processus de perçage

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5214592A (en) * 1989-10-19 1993-05-25 Toshiba Kikai Kabushiki Kaisha Machine tool position correcting method and apparatus
US20030187624A1 (en) * 2002-03-27 2003-10-02 Joze Balic CNC control unit with learning ability for machining centers
EP1659468A2 (fr) * 2004-11-16 2006-05-24 Rockwell Automation Technologies, Inc. Interface d'exécution universelle pour simulation à base d'agent et systèmes de contrôle
WO2009109673A1 (fr) * 2008-03-03 2009-09-11 Consejo Superior De Investigaciones Científicas Procédés de commande fondé sur une logique flou pour des processus de perçage

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102749850A (zh) * 2012-07-20 2012-10-24 富阳登城塑料机械有限公司 一种基于梯形图在间歇式预发机上运用模糊控制的实现方法

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Publication number Publication date
CN102483622A (zh) 2012-05-30
ES2398814B1 (es) 2014-01-29
DE112009005232T5 (de) 2012-12-20
ES2398814A1 (es) 2013-03-21

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