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Bubble Detector in Polyurethane Applications Based on a Microwave System Albert Redo-Sanchez, Javier Tejada, and Xavier Bohigas
Abstract—This paper discusses a device that detects bubbles in glue depositions on a windshield glass, which may cause water leakages inside the cockpit. This device is inexpensive, more reliable than other existing solutions, and implements a specific signal treatment and a bubble size calculation model. The device is based on microwave radiation system in the X-band and has been developed and implemented in real production in the final assembly area of the automotive industry. Index Terms—Industrial control, microwave devices, microwave power transmission, quality control, signal analysis. Fig. 1.
Example of bubbles inside the glue once the glue is dry.
I. I NTRODUCTION
I
NCREASING the automation of manufacturing processes in the automotive industry continuously requires the development of devices and systems to control the quality and performance of these manufacturing processes. In particular, one area of special interest is the automation of the glass gluing process in the final assembly of a car. This process (glass gluing and final assembly) is usually carried out by operators who insert the windshield glass into the frame with the help of semiautomatic manipulators. Usually, glue is deposited over the glass by automatic robot installations. The glue is contained in barrels of 200 L and is pumped and dosed with special systems [1]. One of the biggest problems we face in glass gluing applications is the presence of bubbles (Fig. 1) that may appear during the change of barrels. Traditionally, this problem has been checked with pressure probes that are only sensitive to big bubbles (> 5-mm diameter). Their reliability descends with time because the pressure probe is in contact with the glue and a film of dry glue grows over the contact surface and reduces the coupling. In the pressure probe system, only big bubbles passing near the contact surface are detected as a lowpressure spike [2]. Removing this film requires to completely disassemble the probe system from the main pipe periodically, which requires stopping the production line.
Manuscript received June 8, 2005; accepted October 28, 2005. This work was supported by ASM-Dimatec Ingenieria S.A., Spain. The associate editor coordinating the review of this paper and approving it for publication was Dr. Dwight Woolard. A. Redo-Sanchez is with the Center for Terahertz Research, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (e-mail:
[email protected]). J. Tejada is with the Facultat de Fisica, Universitat de Barcelona, 08028 Barcelona, Spain (e-mail:
[email protected]). X. Bohigas is with the Escola Tecnica Superior d’Enginyeria Industrial, Universitat Politecnica de Catalunya, 08028 Barcelona, Spain (e-mail:
[email protected]). Digital Object Identifier 10.1109/JSEN.2006.877999
The use of ultrasonic transducers was also proposed [3]–[5] and tested, but the main problem was that the transducer must be in contact with the material, similar to the pressure probe system. This is caused because the proper frequency to use within the glue does not propagate in solid materials. Thus, the coupling between the pipe and the glue is very bad, and it makes very difficult to generate an ultrasound wave and to force it to go through the glue. Since the transducers need to be in contact with the glue, the same problems as those that occur with the pressure probe were expected to be present. Recently, systems based on artificial vision have been developed and implemented. These systems have a very high cost, are very difficult to set up, and are used only for applications with a large cycle time, in which inspection can be performed after complete glue deposit over the glass. Systems tested to control at the same time as glue is deposited have been mechanically unstable and are not able to be controlled during turns [6]–[8]. Other systems can only operate after the glue has been deposited [9], reducing the cycle time available. Actually, there is no reliable and low-cost device to check these errors, and an operator with visual inspection almost always performs these checks. The device discussed functions as a quality control device for adhesive and mastic applications in the automotive industry focused in the windshield gluing process. In this application, it is important to check if there are bubbles at the exit of the glue passing through the nozzle because these bubbles can generate water leaks into the cockpit. When the device was tested in production, it was also able to detect dry particle suspended in the glue, which is an effect related to the bad storage and status of the glue, which is another important quality parameter to be controlled. These suspended particles reduce the sticky properties of the glue and may cause the windshield to detach from the frame. A water leak in the windshield and the detachment of the windshield from the frame are two serious quality control problems for the manufacturers.
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Fig. 3. Electronic signal processing blocks.
characteristics determined by its permittivity at the working frequency of our device (10.5 GHz) [12], [13]. Fig. 2. Device drawing. (1) Signal source at 10.5 GHz. (2) Microwave receiver. (3) Aluminum block. (4) X-band waveguide. (5) Nylon tube. (6) Plugs.
III. D EVICE D ESIGN The device discussed herein allows performing such control with low cost, low maintenance, and high reliability. The device is based on the use of low-power microwave radiation in the X-band. Microwave radiation was found to be a good probe for achieving these goals after successful tests performed previous to the complete development of the device. Microwave devices that inspect air bubbles in fluids are used for medical purposes [10], but the viscosity of these fluids is much lower than the glue, and they are not able to inspect it. II. P RINCIPLE OF O PERATION Our system works at a very high pressure (from 100 to 150 bars), so small bubbles (sub-millimeter diameter) will expand to a big volume when they go out by the nozzle, which is at atmospheric pressure. This quick expansion or explosion causes a lack of material being deposited that may cause the water to leak into the cockpit. Our device was designed with standard components existing in the market to keep its construction and maintenance inexpensive. For instance, for emission and receiving components, X-band radio-link devices were used with a working frequency of 10.5 GHz. Both components are attached to the extremes of an X-band waveguide in which a hole is machined perpendicularly to the propagation direction of radiation. A nylon tube is inserted through the waveguide. The glue flows through the nylon tube and is inspected inside the waveguide. At the extremes of the nylon tube, two plugs are attached to connect the pipes (Fig. 2). The principle is based on a transmission method: a signal source (number 1 in Fig. 2) is placed in one extreme of a waveguide (number 4 in Fig. 2), and a receiver (number 2 in Fig. 2) is placed in the opposite extreme. Between them, the nylon tube (number 5 in Fig. 2) is fixed perpendicular to the waveguide. The signal recorded is the transmitted power, which is very sensitive to the homogeneity of the material [11]. The power received changes if any perturbation (bubbles, suspended or dry particles) is present in the flow. This change depends on which kind of perturbation is present and on the propagation characteristics of the material. For instance, the tests showed that for polyurethane, the microwave attenuation due to bubble is lower than the attenuation due to the cleaning paste. This is because each material has its own propagation
Two MACOM MA87728-MO1 voltage-controlled oscillator transceivers for the X-band are used as microwave transceivers. One of the transceivers is used as an emitter (number 1 in Fig. 2) and the other is used as a receiver (number 2 in Fig. 2). For the receiver, the original mixer diode is replaced with a detecting diode 1N21D (also from MACOM). The waveguide (number 4 in Fig. 2) is 55 mm long, 10 mm high, and 23 mm wide. A hole with a diameter of 22 mm is machined perpendicularly in the middle of the waveguide to place a nylon tube (number 5 in Fig. 2) of 50 mm long, 22 mm outer diameter, and 10 mm inner diameter. This nylon tube allows the glue to pass through the waveguide and be inspected. All these parts are attached to an aluminum block (number 3 in Fig. 2), and two plugs are attached at the extremes of the nylon tube to connect the pipelines. The device is protected and shielded with an aluminum case. The frequency is fixed by the provider of the emitter, which is 10.5 GHz. The dielectric properties of the materials to be inspected were studied to determinate if excessive attenuation could represent a problem. The results showed that this frequency was suitable for the purposes of the device. The dielectric characterization of the material was performed with the open coaxial probe kit Agilent 85070C [14]–[18]. This probe kit requires also the use of the HP83651B signal generator and the HP8510C network analyzer. For signal treatment, a specific electronic circuit was designed to clean, filter, and amplify the original signal coming from the receiver. This electronic stage is called conditioning electronics (Fig. 3). Another electronic circuit called bubble seeker was designed to be extremely sensitive to bubble patterns and follows the first circuit (conditioning). The two signals coming from conditioning and derivative electronics are recorded with a computer to get information about bubble size with specific signal inspection software (Fig. 4). There are also 16 digital input/output signals to communicate with an industrial programmable logic control (PLC). IV. S IGNAL P ROCESSING Two signal processes are implemented by electronics and by software. Electronic processing is required because of the weakness of the signal (< 1 V) and the interferences existing in an industrial environment. A specific electronic board was
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Fig. 4. Signal examples of a bubble. (a) Signal from the derivative electronics. (b) Signal from the conditioning electronics. The y-axis is the voltage (in volts), and the x-axis is the time (in seconds).
designed to reduce environmental interferences and amplify the signal. In a first stage of the conditioning electronics processing, the signal is filtered and amplified to 5 V (conditioned signal). In a second stage, the isolating electronics is implemented to avoid having undesired coupled signals from ground, electromagnetic interferences, switching devices, etc. The bubble seeker electronics is designed to enhance the signal when a bubble occurs. This enhancement is achieved by performing the derivative on the signal coming from the conditioning electronics within a specific range of frequency (5–20 Hz). Basically, it is a bandpass filter from 5 to 20 Hz followed by an operational amplifier configured to perform the derivative of the signal. This process allows amplifying and enhancing the variations of the signal due to bubble pattern and not to high frequency noise. The resulting output signal is also processed by the same isolating electronics with the conditioned signal. These two signals (in volts) are processed by a PC with digital filtering in a first stage. Afterward, a bubble detection algorithm is performed, and finally, a size model is applied to retrieve bubble size. This size model is obtained empirically. The final result is displayed by the software, and if the size is bigger than a threshold (size threshold), an error message is sent to the PLC. This size threshold is configurable by the final user with the configuration and monitoring software also developed for this application. This specific software was designed and implemented to allow the final user to set many of the control parameters: bubble detection sensitivity, signal-size model, size threshold, and PLC communication configuration. In Fig. 4, there is an example of a signal given by a bubble. Fig. 4(a) shows the conditioned signal, and Fig. 4(b) shows the signal coming from the derivative electronics. The derivative signal has bigger oscillations when the conditioned signal
shows a bubble, but there are also other oscillations in the derivative signal that do not correspond to a bubble signal pattern. To disregard signals of bubbles from others that are not, a bubble detection algorithm has been implemented in the software. It is based on the construction of a density matrix with the signal coming from the bubble seeker electronics. To build this density matrix, the signal is indexed in a vector, and then each component of the vector is replaced by 1 if the value is higher than a threshold or 0 if it is lower. The components of the density matrix are evaluated as mkl = Nkl /|tk − tl |, with Nkl being the number of component equal to 1 (spikes) between time positions tk and tl . The threshold to set a component of the vector to 1 or 0 is configurable, so the system will count more spikes if the threshold is low (high density) and will count less if it is higher (low density). To get this threshold, the amplitude of noise must be checked and then the threshold be set between 1.5 and two times the noise amplitude. These factors have been observed to perform well for this device. It was observed that oscillations due to bubbles had a high density. It means that the element of the density matrix that represents the time interval where a bubble is contained will have a high value. Inversely, if we search in the matrix for the highest values, we will be able to give the time interval of the bubble (duration) retrieving the times corresponding to the matrix indexes. We can get the position of the next bubble searching for the next highest value in the matrix; this bubble will be smaller than the first one. Sorting the components of the matrix beginning with the highest and ending with the smallest, we will get the positions from the biggest bubble to the smallest by just retrieving the positions associated with the matrix indexes. This algorithm was designed for this application and has been shown to be faster and more stable than other algorithms tested, such as pattern recognition with neural networks, Fourier analysis, or wavelet transform.
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Once we have the position and duration of a bubble, we must be able to get a size to determinate if it is a big bubble or a small one. To get the bubble size, an empirical model is applied according to the signal’s profile recorded in the laboratory tests. Conditioned signals like that shown in Fig. 4 drove us to try with a Gaussian model. In this Gaussian model, the shape of the perturbation caused by the bubble is modeled into a Gaussian shape, i.e., 2 t − t0 (1) ∆S = S0 exp − τ where S0 is the maximum value of the signal with respect to the background and τ is the time of transit. Then, a proportional relationship between the integral of the perturbation and the size of the bubble (V ) is modeled as V = C˜
Fig. 5. Experimental relation and adjustment of signal size for cleaning paste. The x-axis corresponds to the integral of the signal due to the bubble (in volts second), and the y-axis is its volume size (in cubic centimeter). A linear relation is expected, and the parameters of the regression are shown.
∞ ∆S dt = C S0 τ.
(2)
0
Assuming that the bubbles do not interfere with each other, the integral can be extended in the infinite range and C is the proportional constant between the parameters of the bubble (strength of the signal S0 and time of transit τ ) and its size. This proportional constant will depend on the inspected material, so a calibration for each material will be necessary. These parameters are also recorded and adjusted by the software. V. R ESULTS The device was tested and calibrated for two kinds of materials, namely 1) cleaning paste and 2) polyurethane glue (used in the windshield gluing process). To calibrate the device, it is required to inject a known air volume into the material and register the signal (S0 , τ ) corresponding to the bubbles. A representation of the volume versus the integral of the signal will give us a characteristic curve that we expect to be a linear fit if the model is right. The way to inject air inside the material was different for each material due to its different viscosities. For the cleaning paste (which has a lower viscosity than polyurethane), we simply injected a known volume of air by opening a valve with a nonreturn system attached to the inlet plug. Then the pressure was measured, and using the ideal gases expression, we got the volume inside the circuit. The result of this calibration is shown in Fig. 5 with a good match with the theory. In this figure, the x-axis is the integral of the Gaussian fit of the signal (in volts second), and the y-axis is the volume of the injected bubble (in cubic centimeter). The model seems to fit well for these data, and a proportional relationship between size and integrated signal is clear. For the polyurethane, the way to inject air was not the same as in the cleaning paste because of its higher viscosity. Rather, we injected an unknown volume of air directly into the barrel. To get an estimation of the volume going out of the nozzle, we had to apply the glue over many plates, wait until the glue was dry, and estimate physically the size of the bubble, calculating the lack of material. This was not as precise as in the cleaning
Fig. 6. Experimental relation and adjustment of signal size for the polyurethane glue. The x-axis corresponds to the integral of the signal due to the bubble (in volts second), and the y-axis is its volume size (in cubic centimeter). A linear relation is expected, and the parameters of the regression are shown.
paste and is the main cause of the error bars in Fig. 6. The correlation is worse than in the cleaning paste, but an increasing tendency is clear and even a linear relationship is feasible. As before, the x-axis is the integrated signal (in volts second) and the y-axis is the volume (in cubic centimeter) of air measured after the material is dried. Another important feature to know is the probability of detection for a given size. It means that if a bubble of given size goes through the device, we want to know the probability to detect it and get the correct size. To get the statistics of this probability for the polyurethane, we established five ranges, namely R1 (< 0.01 cm3 ), R2 (0.01−0.05 cm3 ), R3 (0.05−0.10 cm3 ), R4 (0.10−0.12 cm3 ), R5 (> 0.12 cm3 ). We then performed many applications (∼ 500) recording the signal and the bubbles from which we calculated the volume. Then we assigned every bubble to one of those ranges and searched in the signal for a detection alarm and its size (calculated by the software). Previously, the detection threshold value of the device was set to two times the level of noise. We had two possibilities, namely 1) the result is correct so the device detected the bubble and the software gave a correct size; or 2) the device did not
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the exact size of a small bubble. The main goal is to detect with precision bubbles above a size bigger than the minimum size of 100% detection probability. This system was implemented in a real production line with good results. From the experience to implement our device in real production, we learned how to evolve and expand the device itself and the software and electronic processing to a wider range of applications. This device is interesting not only for automotive manufacturers but also for glue and mastic manufacturers to perform their own quality checks and inspections. In this case, some modifications on the software must be performed. ACKNOWLEDGMENT Fig. 7. Missed detection versus false detection for different sizes R1 (< 0.01 cm3 ), R2 (0.01–0.05 cm3 ), R3 (0.05–0.10 cm3 ), R4 (0.10–0.12 cm3 ), and R5 (> 0.12 cm3 ). Above 0.12 cm3 , the bubble is always detected. This probability descends as the size of the bubble descends. The number of tests is 500.
detect the bubble or, if it was detected, the software did not give a correct size. So for every range, we have a number of correct and bad detections, and if we calculate the proportion between the correct detections and the number of all bubbles, we get an estimation of this probability. This probability is related to the threshold value, so if the threshold changes, this probability must be checked. The detection probability measurement was done for the polyurethane, and the result is shown in Fig. 7. In this figure, we see that all bubbles bigger than 0.12 cm3 are always detected, but below this size, the device fails to detect some of the bubbles. Reasonably, this sensitivity reduces as the size of the bubble reduces too. The number of tests to perform such statistics is 500. The false alarm percentage is given by the ratio between the number of false detection and the total number of detections. The result of using the same data for Fig. 7 to calculate this percentage yields to a false alarm percentage of < 1% for region R5 and 23% for region R1. For regions R2, R3 and R4, the false alarm percentage is 15%, 7%, and 3%, respectively. The threshold value and the performance evaluation are done in the factory line once the system has been installed and all the components are integrated with the installation. Once it is done, the system only requires visual inspection of the nylon tube every 3 months to check for dry glue that may reduce the effective tube diameter. VI. C ONCLUSION Systems based on microwave devices could be a good solution to implement into quality control processes. They can be compact, reliable, and inexpensive to implement and use compared with existing solutions based on artificial vision, laser devices, pressure probe devices, or human visual inspection. Although for our device the size model does not fit very well for polyurethane, the achieved precision is enough for glue inspection purposes. The sensibility is enough to catch the big bubbles that are the real problem, and it is less important to get
This device has been developed for ASM-Dimatec Ingenieria S.A. and is protected by the patent PCT/ES03/00386 “Microwave System for Detecting Bubbles,” international publication WO 2004/011918 A1 (5/2/2004). The authors would like to thank the people of the company for their comments and suggestions and also for their support in the testing and production of this device. The authors would also like to thank Dr. E. Molins for his useful suggestions and ideas. R EFERENCES [1] ASM-Dimatec Ingenieria S.A. [Online]. Available: http://www. asmgrupo.com [2] S. L. Eines. [Online]. Available: http://www.eines.es [3] Valmet Automation Inc., “Method and measuring arrangement for measuring gas content of fluid,” World Patent 00/03235, Jan. 20, 2000. [4] Toshiba Corporation, “Flow measuring device,” Japan Patent 10019620, Jan. 23, 1998. [5] Persson Teckomatorp, “Method and device for liquid leakage indication,” World Patent 97/14943, Apr. 24, 1997. [6] Keyence. [Online]. Available: http://www.keyence.com [7] Edixia. [Online]. Available: http://www.edixia.fr [8] Omron. [Online]. Available: http://www.omron.com [9] Isra Vision Systems. [Online]. Available: http://www.isravision.com [10] Microwave Medical Systems Inc., “Microwave system for detecting gaseous emboli,” U.S. Patent 5 198 776, Mar. 30, 1993. [11] D. Li, C. E. Free, K. E. G. Pitt, and P. G. Barnwell, “A simple method for accurate loss tangent measurement of dielectrics using a microwave resonant cavity,” IEEE Microw. Wireless Compon. Lett., vol. 11, no. 3, pp. 118–120, Mar. 2001. [12] C. C. Courtney, “Time-domain measurement of the electromagnetic properties of materials,” IEEE Trans. Microw. Theory Tech., vol. 46, no. 5, pp. 517–522, May 1998. [13] C. C. Courtney and W. Motil, “One-port time-domain measurement of the approximate permittivity and permeability of materials,” IEEE Trans. Microw. Theory Tech., vol. 47, no. 5, pp. 551–555, May 1999. [14] A. Boughriet, Z. Wu, H. McCann, and L. E. Davis, “The measurement of dielectric properties of liquids at microwave frequencies using open-ended coaxial probes,” in Proc. 1st World Congr. Ind. Process Tomography, Buxton, U.K., Apr. 1999, pp. 318–322. [15] J. A. Jargon and M. D. Janezic, “Measuring complex permittivity and permeability using time domain network analysis,” in Proc. Microw. Symp. Dig., IEEE MTT-S Int., Jun. 1996, vol. 3, pp. 1407–1410. [16] C.-L. Li and K.-M. Chen, “Determination of electromagnetic properties of materials using flanged open-ended coaxial probe—Full wave analysis,” IEEE Trans. Instrum. Meas., vol. 44, no. 1, pp. 19–27, Feb. 1995. [17] J. Baker-Jarvis, M. D. Janezic, P. D. Domich, and R. G. Geyer, “Analysis of an open-ended coaxial probe with lift-off for nondestructive testing,” IEEE Trans. Instrum. Meas., vol. 43, no. 5, pp. 711–718, Oct. 1994. [18] D. Misra, M. Chabbra, B. R. Epstein, M. Mirotznik, and K. R. Foster, “Noninvasive electrical characterization of materials at microwave frequencies using an open-ended coaxial line: Test of an improved calibration technique,” IEEE Trans. Microw. Theory Tech., vol. 38, no. 1, pp. 8–14, Jan. 1990.
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Albert Redo-Sanchez was born in Reus, Spain, in 1976. He received the B.S., M.S., and Ph.D. degrees from the Universitat de Barcelona, Barcelona, Spain, in 1999, 2001, and 2004, respectively, all in physics. From December 2000 to July 2004, he was the Manager of the Research and Development Department, ASM-Dimatec Ingenieria S.A. Company, Spain, and was involved in several industrial projects for the automotive sector with a result of four patents. He is currently a Postdoctoral Research Associate at Rensselaer Polytechnic Institute, Troy, NY. His research interests are in the areas of millimeter-wave applications, signal/image processing, computational calculus, and software development.
Javier Tejada is a Professor of condensed matter physics at the Universitat de Barcelona, Barcelona, Spain. He is best known for his experimental work on spin tunneling and molecular magnetism. He coauthored the book Quantum Tunneling of the Magnetic Moment (Cambridge Univ. Press, 1998) with E. Chudnovsky. He holds nine international patents in the field of magnetism and has published over 300 research papers.
Xavier Bohigas was born in Barcelona, Spain, in 1953. He received the Ph.D. degree from the Universitat de Barcelona. Since 1991, he has been an Assistant Professor at the Universitat Politecnica de Catalunya, Barcelona. He is working on the characterization of magnetic materials and the applications of magnetocaloric effect and in the characterization of dielectric materials by microwave spectroscopy.
