Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K26/00—Working by laser beam, e.g. welding, cutting or boring
  • B23K26/02—Positioning or observing the workpiece, e.g. with respect to the point of impact; Aligning, aiming or focusing the laser beam
  • B23K26/03—Observing, e.g. monitoring, the workpiece
  • B23K26/032—Observing, e.g. monitoring, the workpiece using optical means
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K26/00—Working by laser beam, e.g. welding, cutting or boring
  • B23K26/02—Positioning or observing the workpiece, e.g. with respect to the point of impact; Aligning, aiming or focusing the laser beam
  • B23K26/03—Observing, e.g. monitoring, the workpiece
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
  • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
  • B23K26/00—Working by laser beam, e.g. welding, cutting or boring
  • B23K26/02—Positioning or observing the workpiece, e.g. with respect to the point of impact; Aligning, aiming or focusing the laser beam
  • B23K26/03—Observing, e.g. monitoring, the workpiece
  • B23K26/034—Observing the temperature of the workpiece
• • 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
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/33—Director till display
  • G05B2219/33025—Recurrent artificial neural network
• • 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
  • G05B2219/33—Director till display
  • G05B2219/33027—Artificial neural network controller
• • 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
  • G05B2219/33—Director till display
  • G05B2219/33038—Real time online learning, training, dynamic network
• • 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
  • G05B2219/34—Director, elements to supervisory
  • G05B2219/34153—Linear interpolation
• • 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
  • G05B2219/37—Measurements
  • G05B2219/37217—Inspect solder joint, machined part, workpiece, welding result
• • 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
  • G05B2219/45138—Laser welding
• • 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 method for quality control of the seam on metal sheets or strips butt-welded with a laser, in which a large number of sensor data is measured by at least two sensors arranged around the welding location and in which the sensor data is processed by at least one summarizing and correlating measurement data processing for quality assessment
• Weld seam are supplied as an input variable, in which the sensor data are measured by at least one sensor that detects the welding plasma and is located at the welding location, in which the sensor data are measured by at least one sensor that detects the welding seam geometry and that is located in the correlating and summarizing position
• Measured data processing the large number of sensor data of the at least two sensors are each fed as an input variable to at least one data preprocessing, in which the results of the data preprocessing for For the purpose of obtaining the sensor data at the same location, they are each stored in a storage unit.
• a disadvantage of this method is that with a growing number of measured process variables, due to their mutual influences in the welding process, the analysis is made more difficult by rule-based modeling.
• a disadvantage of this method is that it is not possible to assess the influence of individual measurement data on the seam quality.
• the known neural network only delivers good / bad decisions, the user not knowing what influence the individual measurement data have on the decision-making process.
• the object of the present invention is to carry out a prompt quality control of weld seams, which enables a realistic analysis of the welding result.
• the stored data are fed as input variables to at least one trainable, artificial neural network with an essentially hierarchical network structure, that the at least one trainable, artificial neural network with an essentially hierarchical network structure at least two essentially independent, trainable, artificial neural subnets are formed, that the first artificial neural subnet is formed from at least two independent, artificial neural subnets, that the results of the data preprocessing are fed as input variables to the first artificial neural networks, that the second artificial neural subnetwork, the results of the first artificial neural subnetworks are supplied as input variables, and that the result of the at least one artificial neural network is used for quality control.
• the method according to the invention enables timely quality control of the seam of a laser weld by using at least one hierarchical artificial neural network for processing a large number of sensor data. It has been shown that the sensor data on the welding plasma and the weld seam geometry are the most meaningful for this.
• the sensor data associated with a weld seam location are fed in parallel to the artificial neural network by the storage units, as a result of which the artificial neural network can promptly carry out the local correlation of the signals associated with the respective weld seam locations.
• the abundance of data in the first artificial neural subnets is reduced to a relevant minimum and in the second neural subnet these data are correlated with one another.
• a method in which a sensor is additionally used for detecting the gap geometry, in which the sensor is arranged in front of the welding location, is preferred.
• this sensor With the help of this sensor, the size of the edge offset before the weld can be detected, whereby a statement about the quality of the weld is possible depending on the edge offset.
• the position of the sheets relative to each other could be readjusted during the welding process.
• a quality statement about the weld as a function of the joint gap is possible using a method in which an additional sensor arranged in front of the welding location is used for the detection of the joint gap. With With the help of this sensor, the size of the joint gap before the weld is detected. This information can also be used to readjust the joint gap during the welding process.
• a sensor is used for the detection of the weld seam temperature just behind the welding location.
• each first artificial neural subnet is formed from three layers, in which the first layer is formed from exactly one neuron, the second layer is formed from a multiplicity of neurons and the third layer from exactly a neuron is formed. This also automates a statement about the probability of an error using a process parameter.
• the second, artificial neural subnetwork is formed from three layers, in which the first layer is formed from a multiplicity of neurons, the second layer is formed from a multiplicity of neurons and the third layer is formed from exactly one neuron. Because there are a large number of input neurons, the outputs of the large number of the first neuronal subnetworks can be fed in parallel to the second common artificial neuronal subnetwork. This enables a parallel correlation of the individual sensor data with each other, whereby a quality statement about the weld seam is possible, taking into account a large number of sensor data.
• One third layer output neuron provides a signal which enables a quality statement about the seam of the laser welding.
• the learning process of the artificial neural network is carried out with the aid of a back propagation learning algorithm, in which the first artificial neural subnetworks are adjusted at a learning rate ⁇ between 0.01 and 0.1 and a momentum ⁇ between 0.1 and 0.6 and in which the second artificial neural subnet is adjusted at a learning rate ⁇ and a momentum ⁇ , which are essentially adapted to the gradient course of an error function of the output of the artificial neural subnet.
• the error function of the output of the artificial neural subnet is e.g. formed by determining the sum of the squares of the differences between the actual and target output.
• the weighting of the individual network elements of the subnetworks is adjusted during the learning phase so that this error function reaches a minimum.
• the first and the second subnetwork are adapted in one go.
• the network configuration found before the training remains unchanged even during the test phase.
• the learning rate and the momentum of the second neural subnetwork can be adapted to the gradient course of this error function, with the result that the global minimum of the error function is found with a high probability and local minima of the error function become transitions.
• the sensor data measured by a sensor arranged in front of the welding site is preferably fed as an input variable to a fault suppression, the results of the fault suppression are fed as input variables to an essentially freely definable window averaging and the difference between the results of the window averaging is formed.
• the error suppression filters out sensor data that arise due to incorrect measurements.
• the window averaging is used to suppress noise flows in the sensor data. By forming the difference value, it is possible to make a statement about the gap width.
• the sensor data measured by a plasma intensity sensor can be fed as an input variable to a window transformation.
• the window transformation makes it possible to extract from the measured plasma intensity data the data relevant for the assessment of the seam torture.
• the sensor data measured by a geometry sensor are fed as an input variable to a window determination.
• the window averaging can local as well as tendency signal changes can be specifically assessed.
• a further embodiment of the method according to the invention is a method in which, for the purpose of feature extraction of the sensor data representing the edge offset, the sensor data measured by a geometry sensor are fed as an input variable to an average value transformation and the result of the average value transformation is fed to a window determination.
• the mean value transformation it is possible to clean up the signal by the total mean value.
• the window averaging makes it possible to filter out any changes in the edge offset.
• the edge offset can be measured both before and after welding. In particular, with circular welding, it is possible to measure the edge offset before and after welding with the help of only one geometry sensor.
• the results of the first artificial neural subnetworks are each normalized in the value range and are fed to the second artificial neuronal subnetwork.
• the maximum value of this value range can e.g. mean a maximum probability of error of the locally measured process parameter.
• Another object of the invention is a device, which is characterized in that at least two sensors for detecting sensor data are arranged around the welding site, wherein the sensor that detects the welding plasma is arranged at the welding site, and wherein the sensor that detects the seam geometry is arranged after the welding site and that Sensor data each serve as an input quantity for data preprocessing, that storage units store the results of the data preprocessing for the same reference, that the entries of the storage units serve as parallel input quantities for an essentially trainable, artificial neural network structure and that the result of the neural network structure is used for the qualitative assessment of the weld seam serves. Due to the parallel acquisition of the sensor data and the calculation by an essentially trainable, artificial neural network, a timely assessment of the weld seam act is possible.
• a geometry sensor is arranged in front of the welding location.
• a pyrosensor can also be arranged at the welding site. That a gap sensor is arranged in front of the welding location is also advantageous.
• the arrangement of several sensors makes it possible to record a large number of process parameters during welding. In addition, the quality statement depends on how many different process parameters are measured, which means that the device must be designed in such a way that a large number of sensor signals can be recorded and processed.
• the invention is based on a
• 2 shows a hierarchical network structure of an artificial neural network
• 3 shows a structure of the first artificial neural subnetwork
• FIG. 6 shows a schematic representation of a method according to the invention.
• Figure 1 shows a first embodiment of a device for quality control of a seam of butt welded sheets or strips with a laser.
• Two sheets or strips 100, 102 which are to be butt-welded to one another are transported with a transport and joining device, not shown, with a predetermined joint gap 104 under a welding head 112 of a laser welding system in the direction F.
• the sheets 100, 102 are butt-welded to one another in a weld 106 with a laser beam L.
• Sensors 108, 110, 114 and 116 are arranged along the weld seam 106 or the joint gap 104.
• the sensor 108 detects the geometry of the joint gap 104 in the lead of the weld.
• the vertical edge offset of the sheets is measured by sensor 108.
• the sensor 110 detects the gap width of the joint gap 104.
• the distance between the sheets 100, 102 is measured by sensors suitable for this purpose, which work, for example, according to the light section or transmitted light method.
• the sensor 114 is used to record the plasma intensity of the laser welding beam L.
• the geometry sensor 116 is used to record the Edge misalignment as well as the seam incidence of the weld seam 106 in the wake of the weld.
• the geometry of the joint gap 104 and that of the weld seam 106 can be detected by only one sensor.
• the welding temperature can be determined using a pyro sensor (not shown).
• the sensor data recorded by sensors 108, 110, 114 and 116 are queried by data preprocessing units 118, 120, 122 and 124 at regular intervals.
• the query frequency for the individual data preprocessing units is between a few Hertz and a few kilohertz.
• the data measured by the sensor 108 are read in by the data preprocessing unit 118 at regular intervals.
• the arithmetic mean of this sensor data over the window width is calculated with the aid of a window averaging.
• the calculated arithmetic mean values of the individual windows are adjusted for the total mean value.
• the sensor data of the gap sensor 110 for the position of the right and left edge of the joint gap 104 read in by the data preprocessing unit 120 are reconstructed by means of interpolation and linear polynomials, since the sensor signals are subject to strong deviations from the actual seam course due to incorrect measurements.
• An arithmetic mean is formed from the reconstructed sensor data with the aid of window averaging. From the arithmetic mean values of the sensor data of the right and the left edge of the foot ⁇ 104 obtained in this way, the Difference formed, which gives information about the size of the gap widening.
• the sensor data measured by the plasma sensor 114 are read in by the data preprocessing unit 122, which are processed in accordance with the procedure shown in FIG.
• the sensor data are fed to the input 400.
• the arithmetic mean of the last 10 measured sensor data is calculated in unit 402 using a window averaging.
• the difference between the currently measured sensor value and the result of the unit 402 is calculated in the unit 404.
• the overall mean value of the output signal of the unit 404 is calculated in the unit 406.
• the output value of unit 406 becomes n of unit 408 the global
• unit 410 Standard deviation calculated.
• unit 410 the arithmetic mean of the last 10 results of unit 404 is calculated using window averaging.
• Unit 412 calculates the local standard deviation using the results of units 410 and 404.
• the maximum difference between the output of unit 404 and the output of unit 410 is calculated in unit 414.
• the result of the data preprocessing is calculated in the unit 416 in such a way that the result of the unit 414 is multiplied by the result of the unit 412 and the value thus calculated is divided by the result of the unit 408.
• the sensor data of the sensor 116 read in by the data preprocessing unit 124 are subjected to a window transformation for averaging.
• the window width can be set so that there are both local and tendency changes in the seam incidence of the weld seam 106 in the Make the mean calculation noticeable. With a window width of ten data points, there is a greater change in the arithmetic mean value with a brief change in the seam incidence than with a window width of forty data points.
• the data processing units 118 The data processing units 118,
• Values calculated 120, 122 and 124 are stored in storage units 119, 121, 123 and 125. With the aid of these storage units, it is possible to supply the data of all sensors belonging to one and the same weld seam point to the artificial neural network 128 at the same time. Since the sensor data of a weld seam point are measured by the sensors 108 to 116 at different times, the sensor data are queried by the data preprocessing units 118, 120, 122 and 124 at different intervals and the data preprocessing of the different sensor data each require a different computing effort, the data are available a weld seam point at the output of the data preprocessing units 118, 120, 122 and 124. By storing the data in units 119,
• the memory units 119, 121, 123 and 125 are driven by a common clock signal CLK.
• CLK common clock signal
• the stored data, which belong to a weld seam point, are supplied to the trainable, artificial neural network 128 when the clock signal is present.
• the data is converted into the Data preprocessing units 118, 120, 122 and 124 em output signal 130 calculated, with the help of which a statement is made about the quality of the weld.
• FIG. 2 shows the trainable, artificial neural network 128 with an essentially hierarchical network structure.
• the neural network 128 consists of a multiplicity of first artificial neural subnets 218, 220, 222 and 224 and a second artificial neural subnet 242.
• the results of the data preprocessing units 118, 120, 122 and 124 are each transferred to a first artificial neural subnet 218, 220, 222 or 224 fed.
• the network structure of the first artificial neural subnetworks 218, 220, 224 and 226 is shown in FIG. 3. It consists of an input layer 316, a hidden layer 318 and an output layer 320.
• the input layer 316 consists of an input neuron 300.
• the input value 301 of the first artificial neural subnet which is the output value of data preprocessing, is transferred from the input neuron 300 to the neurons of the hidden ones Layer 318 distributed with different weights.
• the hidden layer 318 consists of a multiplicity of neurons 302-312.
• the size of the output signal is determined from the weighted input signal of each individual neuron.
• the activation function can be, for example, a sigmoidal function or a tangent hyperbolic function.
• the size of the output signal is calculated as follows from the sum of the weighted input signals and a threshold value as the input value of an activation function:
• Xi. Input signal of the neuron
• ⁇ - threshold value of the neuron.
• the output signals of the neurons of the hidden layer 318 are fed to the neuron of the output layer 314.
• the size of the output signal 316 is again calculated using weights of the input signals, an activation function and a threshold value.
• the outputs of the first artificial neural subnetworks 218-224 are each normalized in the value range. Using these value range normalizations, the outputs of the first artificial neural subnetworks 218-224 are standardized to a value range of, for example, 0-1.
• the outputs of the first artificial neural subnetworks 218-224 can thus be regarded as local error values.
• an initial value of the artificial neural subnet 224 of FIG. 1 means that the probability of error in the event of a seam incidence is 100%.
• An initial value of 0.5 of the artificial neural subnet 218 means that the probability of error ⁇ es is assigned to the first artificial neural subnet 218 Process parameter edge offset, which was measured by a geometry sensor, is 50%.
• the individual local error probabilities are fed to the second artificial neural subnet 242 in parallel.
• the structure of the second artificial neural subnet 242 is shown in FIG. 5.
• the second artificial neural subnet 242 consists of an input layer 516, a hidden layer 518 and an output layer 520.
• the input layer 516 consists of a multiplicity of neurons 532, 534, 536 and 538.
• the input values of the neurons of the input layer 516 of the second artificial neural subnet 242 are the respective output values of the value range normalizations of the first artificial neural subnets 232-238.
• the size of the output signals is in turn calculated in neurons 532-538 with the aid of an activation function and a weighting of the input signal and a limit value, as is further shown in FIG. 3 has been described above.
• a large number of neurons 502-512 are located in the hidden layer 518.
• the output signals of all neurons 532-538 of the input layer 516 are fed to these neurons.
• the inputs of all neurons 502 to 538 are weighted.
• the output signals of neurons 502 to 538 are calculated using an activation function and a limit value.
• the output layer 520 there is only one neuron 514, to which the output signals of the neurons 502 512 of the hidden layer 518 can be supplied.
• the input signals are weighted, the sum of the weighted input signals is added with a threshold value and the result serves as an input variable for an activation function.
• the resulting output signal is used to assess the quality of the weld.
• the individual weightings of the input signals of the neurons in the first artificial neural subnetworks 218-224 and in the second artificial neuronal subnetworks 242 are set up during a training phase in such a way that the statement of the artificial neural network 128 simulates that of a manual viewer.
• a reference / actual value comparison can be carried out at the output of the artificial neural network 128 with the aid of reference welds, and the weightings are set with the aid of the back propagation learning algorithm.
• the second artificial neural subnet 242 determines an output value 244 from the large number of input values, which enables a statement to be made about the welding seam act.
• the interaction between the individual process parameters is taken into account by the second artificial neural subnet 242. It is quite possible that the error probability of the process parameter seam incidence is 80%, but due to the interaction with other process parameters the overall error probability of the weld seam is 10%.
• sensor data 600 are detected by different sensors, such as, for example, geometry sensors, gap width sensors, pyro sensors and also plasma sensors.
• This sensor data will be the Data analysis fed through artificial neural networks 602.
• a seam assessment 604 is possible on the basis of the values determined by the artificial neural networks. It is also possible to make a statement about the condition of the welding system, for example the feed rate, the seam cooling, the welding performance or the pressing force.
• the results of the seam assessment as well as the sensor data are stored in a database 608.
• the data records stored in database 608 are used for product and system evaluation. These data also serve as evidence in the event of product liability. This data can also serve as evidence for a quality certification.
• the results of the seam assessment 604 are used to set up a control loop 610.
• the data are used on the one hand to control the system technology 603a and on the other hand to control the laser technology 603b.
• the system technology includes settings of the welding system, such as the pressure force, the supply of protective gases and the after-cooling, as well as the feed rate of the sheets to be welded.
• the regulation of the laser technology 603b includes the regulation of the welding power, the welding temperature, the power distribution and the focus position of the welding beam.

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• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Optics & Photonics (AREA)
• Plasma & Fusion (AREA)
• Mechanical Engineering (AREA)
• General Engineering & Computer Science (AREA)
• Manufacturing & Machinery (AREA)
• Quality & Reliability (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Laser Beam Processing (AREA)
• Lining Or Joining Of Plastics Or The Like (AREA)

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• 1999
  • 1999-11-27 DE DE19957163A patent/DE19957163C1/de not_active Expired - Fee Related
• 2000
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  • 2000-11-10 CN CN00814905A patent/CN1384772A/zh active Pending
  • 2000-11-10 AU AU25055/01A patent/AU2505501A/en not_active Abandoned
  • 2000-11-10 JP JP2001541641A patent/JP2003516860A/ja active Pending
  • 2000-11-10 CA CA002390873A patent/CA2390873A1/en not_active Abandoned
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