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
  • B29C49/00—Blow-moulding, i.e. blowing a preform or parison to a desired shape within a mould; Apparatus therefor
  • B29C49/42—Component parts, details or accessories; Auxiliary operations
  • B29C49/78—Measuring, controlling or regulating
  • B29C49/80—Testing, e.g. for leaks
• • 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
  • B29C49/00—Blow-moulding, i.e. blowing a preform or parison to a desired shape within a mould; Apparatus therefor
  • B29C49/42—Component parts, details or accessories; Auxiliary operations
  • B29C49/78—Measuring, controlling or regulating
  • B29C2049/788—Controller type or interface
  • B29C2049/78805—Computer or PLC control
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
  • B29L—INDEXING SCHEME ASSOCIATED WITH SUBCLASS B29C, RELATING TO PARTICULAR ARTICLES
  • B29L2031/00—Other particular articles
  • B29L2031/712—Containers; Packaging elements or accessories, Packages
  • B29L2031/7158—Bottles

Definitions

• blow molder receives preforms and outputs containers.
• a preform is received into a blow molder, it is initially heated and placed into a mold. Hot air is then blown into the preform causing it to stretch and take the shape of the mold.
• a typical blow molder has between ten (10) and twenty- four (24) molds, allowing it to produce multiple containers in parallel. This increases the product rate of the blow molder, but also increases the rate at which defective containers can be generated when there is a problem with one or more blow molding process parameters. Accordingly, container manufacturers are keen to detect and correct blow molding process problems as efficiently as possible.
• blow-molded containers In the course of manufacturing blow-molded containers, it is desirable to control the blow molder so as to correct for defects, as described above. It is also desirable to tune the blow molder so as to modify the material distribution in the completed containers. This is typically accomplished manually. According to one common technique, an operator of the blow molder ejects a set of completed containers for inspection. A manual "squeeze" test is performed to provide a qualitative indication of whether there is sufficient material at key locations of the container. The operator, or other personnel, then measures section weights of the container by physically dividing the container into circumferential sections. Each section is individually weighted, yielding the section weights. Based on the qualitative results of the squeeze test and the quantitative results of the section weights, the operator modifies input parameters of the blow molder to move material to the appropriate locations within the bottle.
• Figure 1 is simplified block diagram of a blow molder system according to various embodiments
• Figures 2, 3 and 11 provide views of a portion of an inspection system according to various embodiments
• FIGS. 4 to 8 show an emitter assembly of the inspection system according to various embodiments
• Figure 9 shows a sensor of the inspection system according to various embodiments.
• Figure 10 is a simplified block diagram of a sensor circuit board of the inspection system according to various embodiments.
• Figure 12 is a simplified block diagram of a driver board for an emitter assembly 60 of the inspection system according to various embodiments
• Figure 13 is a timing diagram according to various embodiments.
• Figure 14 is a simplified block diagram of the inspection system according to various embodiments.
• Figure 15 shows a staggered vertical array of emitter assemblies according to various embodiments.
• Figure 16 is a flow chart showing a one embodiment of process flow for programming the processor to control the blow molder based on real-time container output.
• Figure 17 is a flow chart showing one embodiment of a process flow for changing the a baseline material distribution of the blow molder.
• Figure 18 is a diagram of an example container illustrating material distribution over a set of example container regions.
• Figure 19 is a screen shot showing one embodiment of a user interface for receiving material distributions, such as the new baseline material distribution, from a user.
• Figure 20 shows an alternate embodiment of a user interface for receiving and/or reporting material distributions.
• a measurement device may be used to measure material distributions of blow molded containers.
• a processor may be programmed to generate a model of the correlations between material distribution and blow molder input parameters utilizing the measured material distributions.
• a user provides the computing device with a desired output material distribution (e.g., a baseline material distribution).
• the processor analyzes the model to determine whether it is robust enough to provide the desired material distribution. If the model is not robust enough to provide the desired material distribution, the user may be prompted to provide an alternative material distribution. If the model is capable of providing the desired material distribution, it may generate a set of input parameters for the blow molder that will bring about the desired material distribution.
• the measurement device may be any suitable type of measurement device capable of measuring a material distribution profile.
• the measurement device finds the material distribution of containers after formation (e.g. , either in or downstream of the blow molder).
• the measurement device may be used to take multiple direct or indirect readings of one or more container characteristics across a profile (e.g., a vertical profile) of the container.
• the container characteristics may comprise, for example, wall thickness (e.g., average 2-wall thickness), mass, volume, etc. Material distribution may be derived from any of these measurements.
• the computer device may utilize the container characteristics found across the profile of the container to derive a material distribution of the container.
• the measurements, and therefore the calculated material distribution need only be taken across the oriented or stretched parts of the container and may exclude non-oriented portions of the container such as, for example, a finish area, a base cup, etc.
• the measurement device includes an on-line inspection system comprising a vertical array of emitter assemblies that cyclically emit light energy in at least two different narrow wavelength bands at a blow molded container as the container passes through an inspection area.
• each emitter assembly may comprise two narrow band light sources: one that emits light energy in a narrow wavelength band that is substantially absorbed by the material of the container in a manner highly dependent on the thickness of the material; and one that emits light energy in another, discrete narrow wavelength band that is substantially transmissive by the material of the container.
• the light sources may be LEDs or laser diodes, for example, having different narrow band emission spectra.
• the inspection system may also comprise a vertical array of broadband photodetectors facing the emitter assemblies, such as in a 1-to-l relationship.
• the light energy that is not absorbed by the container may pass through two sidewalls of the container, where the light energy is sensed by the photodetectors.
• Each broadband photodetector preferably has a broad enough response range to detect light energy from the different light sources of the emitter assemblies.
• the inspection system may also comprise a processor in communication with the photodetectors, where the processor is programmed to determine a characteristic of the inspected container, such as the average 2-wall thickness of the container or some other characteristic, based on output signals from the photodetectors.
• the ratio between the detected light energy of the two narrow band light sources may indicate the 2- wall absorption of the container.
• the processor may utilize this value to generate wall thickness ⁇ e.g., average 2-wall thickness), mass, volume, etc., which, based on the profile, may be used to derive the material distribution.
• the processor may also be programmed to perform other tasks such as, for example, determining which containers should be rejected, determining real time calibration adjustments for the emitters and sensors to maintain calibration, and sending control signals to the blow molder system to adjust parameters of the blow molder, such as heating temperature or other parameters, to close a feedback control loop for the blow molder system.
• the light sources in the emitter assemblies may be cyclically controlled such that during each cycle there is a time period when: only one of the light sources is on; only the other light source is on; and both light sources are off.
• Such a timing architecture may aid the processor in determining the characteristics of the container and for calculating the material distribution and section weights.
• pairs of emitters and sensors may be relatively densely spaced along the vertical span of the containers in the inspection area. Thus, a relatively complete material distribution of the inspected container may be obtained.
• a system such as that described above is set forth in U.S. Patent No. 7,924,421 filed on August 31, 2007 and incorporated herein by reference in its entirety.
• Another type of system that may be used to measure container characteristics for finding material utilizes a broadband light source, a chopper wheel, and a spectrometer to measure the wall thickness of the a container as it passes between the light source and the spectrometer after being formed by a blow molder.
• the broadband light source in such a system may provide chopped IR light energy that impinges the surface of the plastic container, travels through both walls of the container, and is sensed by the spectrometer to determine absorption levels in the plastic at discrete wavelengths. This information may be used, for example, by a processor, to determine characteristics of the plastic bottle, such as wall thickness, material distribution, etc.
• such systems may use an incandescent bulb to generate broadband light within the visible and infrared spectrums of interest.
• the broadband light is chopped, collimated, transmitted through two walls of the plastic container, and finally divided into wavelengths of interest by the spectroscope.
• An example of such a system is described in U.S. Patent No.
• FIG. 1 is a block diagram of a blow molder system 4 according to various embodiments.
• the blow molder system 4 includes a preform oven 2 that typically carries the plastic preforms on spindles through the oven section so as to preheat the preforms prior to blow-molding of the containers.
• the preform oven 2 may comprise, for example, infrared heating lamps or other heating devices to heat the preforms above their glass transition temperature.
• the preforms leaving the preform oven 2 may enter the blow molder 6 by means, for example, of a conventional transfer system 7 (shown in phantom).
• the blow molder 6 may comprise a number of molds, such as on the order of ten to twenty- four, for example, arranged in a circle and rotating in a direction indicated by the arrow C.
• the preforms may be stretched in the blow molder, using air and/or a core rod, to conform the preform to the shape defined by the mold.
• Containers emerging from the blow molder 6, such as container 8 may be suspended from a transfer arm 10 on a transfer assembly 12, which is rotating in the direction indicated by arrow D.
• transfer arms 14 and 16 may, as the transfer assembly 12 rotates, pick up the container 8 and transport the container through the inspection area 20, where it may be inspected by the inspection system described below.
• a reject area 24 has a reject mechanism 26 that may physically remove from the transfer assembly
• container 30 has passed beyond the reject area 24 and may be picked up in a star wheel mechanism 34, which is rotating in direction E and has a plurality of pockets, such as pockets 36, 38, 40, for example.
• a container 46 is shown in Figure 1 as being present in such a star wheel pocket.
• the containers may then be transferred in a manner known to those skilled in the art to conveyer means according to the desired transport path and nature of the system.
• the blow molder system 4 may produce containers at a rate of 20,000 to 100,000 per hour.
• the input parameters of the blow molder 4 may ultimately affect the characteristics of the generated containers. Input parameters may include, for example, perform oven 2 settings, such as temperature, the pressure of air utilized to conform the perform to the mold, etc.
• FIGS 2 and 3 illustrate an inspection system 50 according to various embodiments of the present invention.
• the inspection system 50 may be an in-line inspection system that inspects the containers as they are formed, as fast as they are formed ⁇ e.g., up to 100,00 containers per hour), without having to remove the containers from the processing line for inspection and without having to destroy the container for inspection.
• the inspection system 50 may determine characteristics of each container formed by the blow molder 4 ⁇ e.g., average 2-wall thickness, mass, volume, and/or material distribution) as the formed containers are rotated through the inspection area 20 by the transfer assembly 12 following blow molding.
• Figure 2 is a perspective view of the inspection system 50 and Figure 3 is a front plan view of the inspection system 50.
• the inspection system 50 may comprise two vertical arms 52, 54, with a cross bar section 56 therebetween at the lower portion of the arms 52, 54.
• One of the arms 52 may comprise a number of light energy emitter assemblies 60
• the other arm 54 may comprise a number of broadband sensors 62 for detecting light energy from the emitter assemblies 60 that passes through a plastic container 66 passing between the arms 52, 54.
• the container 66 may be rotated through the inspection area 20 between the arms 52, 54 by the transfer assembly 12 (see Figure 1).
• a conveyor may be used to transport the containers through the inspection area 20.
• the emitter assemblies 60 may comprise a pair of light emitting diodes (LEDs) that emit light energy at different, discrete narrow wavelengths bands.
• LEDs light emitting diodes
• one LED in each emitter assembly 60 may emit light energy in a narrow band wavelength range where the absorption characteristics of the material of the container are highly dependent on the thickness of the material of the plastic container 66 ("the absorption wavelength").
• the other LED may emit light energy in a narrow band wavelength that is substantially transmissive ("the reference wavelength") by the material of the plastic container 66.
• the thickness through two walls of the container 66 can be determined at the height level of the emitter-sensor pair. This information can be used in determining whether to reject a container because its walls do not meet specification (e.g., the walls are either too thin or too thick). This information can also be used as feedback for adjusting parameters of the preform oven 2 and/or the blow molder 6 (see Figure 1) according to various embodiments, as described further below.
• Such closely spaced emitter-sensor pairs can effectively provide a rather complete vertical wall thickness profile for the container 66.
• the absorption wavelength narrow band may be around 2350 nm, and the reference wavelength band may be around 1835 nm.
• different wavelength bands may be used.
• the terms “narrow band” or “narrow wavelength band” means a wavelength band that is less than or equal to 200 nm full width at half maximum (FWHM). That is, the difference between the wavelengths at which the emission intensity of one of the light sources is half its maximum intensity is less than or equal to 200 nm.
• the light sources have narrow bands that are 100 nm or less FWHM, and preferably are 50 nm or less FWHM.
• the arms 52, 54 may comprise a frame 68 to which the emitter assemblies 60 and sensors 62 are mounted.
• the frame 68 may be made of any suitable material such as, for example, aluminum. Controllers on circuit boards (not shown) for controlling/powering the emitter 60 and sensors 62 may also be disposed in the open spaces defined by the frame 68.
• the crossbar section 56 may be made out of the same material as the frame 68 for the arms 52, 54.
• the frame 68 may define a number of openings 69 aimed at the inspection area 20. As shown in Figure 2, there may be an opening for each sensor 62. There may also be a
• each emitter assembly 60 Light energy from the emitter assemblies may be directed through their corresponding opening into the inspection area 20 and toward the sensors 62 behind each opening 69.
• FIG 4 is a top plan view of an emitter assembly 60 according to various embodiments.
• the emitter assembly 60 may comprise a first LED contained in a first LED sleeve 80, and a second LED contained in a second LED sleeve 82 (sometimes respectively referred to as “first LED 80" and “second LED 82" for purposes of simplicity).
• One of the LEDs 80, 82 may emit light energy at the reference wavelength and the other may emit light energy at the absorption wavelength.
• the first LED sleeve 80 may contain the LED emitting at the absorption wavelength band and the second LED sleeve 82 may contain the LED emitting at the reference wavelength band.
• the emitter assembly 60 may comprise a beam splitter 84.
• the beam splitter 84 may be a dichroic beam splitter that is substantially transmissive to the light energy from the first LED 80 such that the light energy from the first LED 80 propagates toward the opening 69, and substantially reflective of the light energy from the second LED 82 such that the light energy from the second LED is also directed toward the opening 69.
• the assembly 60 may also comprise a covering 86 for each opening 69.
• the covering 86 may be substantially transmissive for the emitted wavelength bands of the first and second LEDs.
• a screw (not shown) through screw openings 88, 89 may be used to secure the assembly 60 to the frame 68.
• Pins (not shown) in pin openings 90, 91 may be used to align the assembly 60 for improved optical performance.
• Conduit 92 may be used to contain electrical wires for the second LED 82, such that the wires (not shown) for both the first and second LEDs 80, 82 may attach the assembly 60 at a back portion 94 of the assembly 60.
• FIG. 5 provides another view of an emitter assembly 60. This figure shows the first LED 100 and the second LED 102.
• the light energy from each LED 100, 102 may be directed through a one or series of collection and collimating lenses 104, 106, respectively, by highly reflective interior walls 108 of a cylinder casing 110, 112 that respectively encases the LEDs and the lenses.
• Each LED 100, 102 may have an associated circuit board 114, 116 or other type of substrate to which the LEDs 100, 102 are mounted and which provide an interface for the electrical connections (not shown) to the LEDs 100, 102.
• Figures 6-8 show different views of the emitter assemblies 60 according to various embodiments. In Figures 7 and 8, only half (the lower half) of the emitter assemblies 60 are shown for illustration purposes.
• Figure 9 is a diagram of a sensor 62 according to various embodiments.
• the sensor 62 includes a broadband photodetector 120 for sensing the light energy from the emitter assemblies 60.
• the photodetector 120 may be an enhanced InGaAs photodetector.
• Such a photodetector is capable of sensing a broad range of wavelengths, including the wavelength bands emitted by the emitter assemblies 60.
• the sensor 62 may further comprise one or more lenses 122 for focusing the incoming light onto the photodetector 120.
• the detector may also comprise stray light baffles 124.
• the photodetector 120 may have an associated circuit board 126 or other type of substrate to which photodetector 120 is mounted and which provides an interface for the electrical connections (not shown) to the photodetector 120.
• Figure 10 is a simplified block diagram of the sensor 62 and an associated sensor controller circuit board 134.
• the sensor 62 may further comprise a first amplifier 130 for amplifying the signal from the photodetector 120.
• the amplifier 130 may be integrated with the photodetector 120 or on the controller circuit board 126 (see Figure 9).
• the output of the amplifier 130 may then be input to another amplifier 132 on the sensor circuit board 134.
• the sensor circuit board 134 may be located near the sensor 62, such as in the open space in the arm 54, as shown in Figure 11.
• each circuit board 134 may interface with eight sensors 62 so that, for an embodiment having thirty two emitter-sensor pairs, there may be four such sensor circuit boards 134 for the thirty-two sensors.
• the circuit board 134 may comprise an analog-to-digital (A/D) converter 136 for converting the amplified analog signals from the photodetector 120 to digital form.
• A/D converter 136 may be a 16-bit A/D converter.
• the output from the A/D converter 136 may be input to a field programmable gate array (FPGA) 140 or some other suitable circuit, such as an ASIC.
• FPGA field programmable gate array
• the circuit board 134 may communicate with a processor 142 via a LVDS (low voltage differential signaling)
• the processor 142 may be a digital signal processor or some other suitable processor for processing the signals from the sensors 62 as described herein.
• the processor 142 may have a single or multiple cores.
• One processor 142 may process the data from each of the circuit boards 134, or there may be multiple processors.
• the processor(s) 142 may be contained, for example, in an electrical enclosure 144 mounted under the crossbar section 68 of the inspection system 50, as shown in Figure 11. In various embodiments, all or a portion of the one or more processors 142 are components of a computing device.
• FIG 12 is a simplified schematic diagram of a controller 148 for the emitter LEDs according to various embodiments.
• Each LED 100, 102 may have an associated switch 150, which may control when the LEDs are on and off.
• the switches 150 may be implemented as field effector transistors (FETs), for example.
• An adjustable constant current source 154 may drive the LEDs 100, 102.
• the current from the current sources 154 may be adjusted to control the light intensity of the LEDs 100, 102 for calibration purposes, for example.
• Any suitable adjustable current source may be used, such as a transistor current source or a current mirror.
• the current sources 154 may be controlled by signals from a FPGA 158 (or some other suitable programmable circuit) via a digital-to-analog (D/A) converter 160.
• the FPGA 158 may store values to appropriately compensate the intensity levels of the LEDs 100, 102 based on feedback from the processor(s) 142.
• the FPGA 158 may control the LEDs for numerous emitter assemblies 60.
• a single FPGA 158 could control eight emitter assemblies 60, each having two LEDs, as described above.
• the FPGA 158 along with the D/A converter 160, current sources 154, and switches 150 for each of the eight channels could be contained on a circuit board near the emitter assemblies 60, such as in the space defined by the frame 68 of the arm 52, as shown in Figure 11.
• the FPGAs 158 may communicate with the processor 142 in the enclosure (see Figure 11) using a LVDS connection or some other suitable serial or parallel communication link.
• the LEDs 100, 102 may be switched on and off cyclically. During a time period when both LEDs 100, 102 are off, the drive for the LEDs 100, 102 may be adjusted and/or the gain of the amplifiers 130, 132 on the sensor side may be adjusted to compensate for drifts in performance and/or to otherwise keep the emitter-sensor pairs calibrated.
• Figure 13 is a timing diagram showing the system timing architecture for a sampling cycle according to various embodiments. In the illustrated embodiment, the switching cycle has a duration of 20 microseconds, corresponding to a sampling rate of 50 kHz. Of course, in other embodiments, switching cycles having different durations could be used.
• the LEDs 100, 102 of the emitter assemblies 60 preferably take less than 500
• the photodetectors 120 of the sensors preferably have a response time of 500 nanoseconds or less. Further, the recovery time of the photodetectors 120 after turn off is preferably 500 nanoseconds or less.
• the absorption LED in every other emitter assembly 60 e.g., the "odd” ones
• the sensors 62 may detect light energy from more than one emitter assembly 60
• the emitter assemblies 60 may be turned on and off in banks in such a fashion. In the illustrated embodiment, the emitter assemblies 60 are operated it two banks (odd and even), although in other embodiments the emitter assemblies could be operated in more than two banks.
• the A/D converter 136 for each sensor 62 may latch and convert the signal from the photodetector 120 for the condition when the all of the LEDs are off.
• the A/D converter 136 for each sensor 62 may latch and convert the signal from the photodetector 120 for the condition when the even absorption LEDs are on.
• the A/D converter 136 for each sensor 62 may latch and convert the signal from the photodetector 120 for the condition when the even reference LEDs are on.
• blow molder system such as blow molder system 4 of Figure 1
• multiple sensors that are within or operatively associated with the blow molder system may provide information to a computer device (e.g., including processor(s) 142) to enable synchronization of the specific molds and spindles in the blow molder which made the container being inspected and thereby provide valuable feedback information.
• a computer device e.g., including processor(s) 142
• One sensor designated the blow-molder machine step sensor, may emit a signal which contains information regarding the counting of the molds and spindles from their corresponding starting position.
• the total number of molds or spindles may vary depending upon the make and model of blow-molder, but this information is known in advance. This information may be programmed into the system.
• a second signal which is from the blow- molder synchronization sensor, may provide information regarding start of a new cycle of rotating the mold assembly.
• the blow-molder spindle synchronizing sensor provides output regarding the new cycle of rotating the spindle assembly.
• the sensors employed for monitoring machine step mold sync and spindle sync may be positioned at any suitable location within the blow-molder and may be of any suitable type, such as inductive sensors which are well known to those skilled in the art.
• a part-in-place sensor may provide a signal to the processor(s) 142 indicating that a container has arrived at the inspection system 20 and that the light-energy-based inspection should be initiated. At that point, the container transects the beams of emitted light from the multiple discrete-wavelength spectral light sources 60.
• the processor(s) 142 is in communication with the broadband sensors 62 and receives electrical signals from the sensors 62, as described above, in order to perform a comparison of the thickness information contained within the electrical signals with stored information regarding desired thickness. More details regarding such sensors are described in U.S. Patent 6,863,860, issued on March 8, 2005, which is incorporated herein by reference.
• the processor(s) 142 may emits a signal or command to a blow-molder reject mechanism 26, which in turn initiates a rejection signal to operate a container rejection system and discard that container from the conveyer.
• FIG 14 is a diagram illustrating the processor-based control system that may be realized using the inspection system 50 according to various embodiments.
• the signals from the photodetectors 120/sensor circuit boards 134 may be input to the processor 142, including the signals for the conditions when only the absorption LEDs are on, when only the reference LEDs are on, and when all of the LEDs are off.
• the processor 142 can compute or determine the average thickness through 2 sidewalls of the container 66 at each height level of the emitter-sensor pairs. Thus, for example, if there are thirty-two emitter-sensor pairs, the processor 142 can compute the average thickness through 2 sidewalls of the container 66 at thirty-two different height levels on the bottle. This information can be used to determine if a container should be rejected. If a container is to be rejected, the processor 142 may be programmed to send a reject signal to the reject mechanism to the cause the container to be rejected.
• the processor(s) 142 may also compute the mass, volume and/or material distribution of the container as these attributes (or characteristics) are related to thickness.
• the mass or volume of various sections of the inspected container e.g., sections corresponding to the various height levels of the emitter-sensor pair, could also be calculated by the processor 142.
• the processor could also compute container diameter by measuring the time between detection of the leading edge of the container and detection of the trailing edge. This time interval, when combined with container velocity information, provides an indication of container diameter at multiple elevations, sufficient for identification of malformed containers.
• the processor 142 may be programmed to also calculate trending information, such as the average thickness at each height level for the last containers and/or the last y seconds.
• the processor 142 may be programmed to, for example, send a control signal to the preform oven 2 to modify the temperature of its heaters (e.g., raise or lower the temperature).
• the processor 142 may be programmed to also calculate updated calibration data for the emitter assemblies 60 and the sensors 62 based on the signals from the sensor circuit boards 134. For example, the processor 142 may be programmed to compute whether the drive signal from the current sources 154 for the emitter assemblies 60 must be adjusted and/or whether the gain of either of the amplifier stages 130, 132 of the sensor circuit board 134 must be adjusted. The processor 142 may be programmed to transmit the calibration adjustment signals to one or more of the FPGAs 158 of the driver boards 148 for the emitter assemblies 60 and, based on calibration values coded into the FPGAs 158, the FPGAs 158 may control the drive signal from the current source 154. Similarly, the processor may transmit calibration adjustment signals to the FPGAs 140 of the sensor circuit boards 134 and, based on calibration values coded into the FPGAs 140, the FPGAs 140 may control the gain of the amplifier stages 130, 132 to maintain calibration.
• the processor 142 could calculate the average thickness at each height level for the last x containers for a specified mold, spindle, and/or mold-spindle combination.
• the processor 142 could also calculate other related statistical information that may be relevant. This information may be used to detect a defective mold or spindle, or to adjust a parameter of the blow molder 6.
• the system may also include, in some embodiments, a vision system 200 for inspecting the formed containers.
• the vision system 200 may comprise one or more cameras to capture images of the formed containers either from the top, bottom, and/or sides. These images may be passed to the processor 142 and analyzed to detect defects in the formed containers. If a container with defects is detected, the processor 142 may be programmed to send a signal to the reject mechanism to reject the container.
• the vision system could be similar to the vision system used in the AGR Top Wave Pet Wall Plus thickness monitoring system or as described in U.S. Patent 6,967,716, filed on April 21, 2000 which is incorporated herein by reference.
• the output thickness information from the processor(s) 142, as well as the vision-based information for a system that includes a vision system 200, may be delivered to a graphical user interface 202, such as a touch screen display.
• the GUI 202 may provide an operator with information regarding specific containers produced by particular mold and spindle combinations of the blow molder. It is preferred that the values be averaged over a period of time, such as a number of seconds or minutes. In addition or in lieu of time measurement, the average may be obtained for a fixed number of containers which may be on the order of 2 to 2500.
• the GUI 202 may also provide trend information for the blow-molder and individual molds and spindles. In the event of serious problems requiring immediate attention, visual and/or audio alarms may be provided.
• the operator may input certain information to the processor 142 via the GUI 202 to alter calibrations in order to control operation of the processor(s).
• the operator may input process limits and reject limits into the processor(s) 142 for each of the thickness measurement zones of the containers to be inspected.
• the reject limits are the upper and lower thickness values that would trigger the rejection of a container.
• the process limits are the upper and lower values for the time-averaged or number of container averaged thickness that would trigger a process alarm indicator.
• the light emitter assemblies 60 may use one or more laser diodes to emit light energy at the discrete wavelength bands.
• a dichroic beam splitter 84 in the emitter assemblies 60 to merge the discrete narrow band light sources other optical techniques could be used to achieve the same effect. For instance, a bifurcated fiber optic coupler may used to mix the light energy from the two discrete light sources.
• the preferred embodiment uses enhanced InGaAs photodetectors 120, in other embodiments other types of detectors could be used to the same effect.
• PbS detectors could be used to measure a broad range of light in the relevant wavelength ranges.
• the above-described embodiments use vertically aligned LEDs and sensors, an alternative configuration would stagger the mounting of adjacent LEDs/sensor pairs in order to achieve a more densely stacked vertical array of sensors, as shown in the example of Figure 15, which just shows a staggered vertical array of emitter assemblies 60.
• the photodetectors could be similarly staggered.
• the processor 142 may be programmed to control input parameters of the blow molder 4 based on measured characteristics of containers generated by the blow molder 4.
• changes to blow molder input parameters may be determined based on the material distribution of output bottles. For example, it has been discovered that there is a high degree of correlation between the material distribution of a container and the parameters of the blow molder that generated it. That is, the material distribution of a bottle may be used to approximate to a high degree of certainty the blow molder conditions under which the bottle was made (e.g. , oven lamp temperature, pre-blow pressure, pre-blow timing, etc.).
• the R 2 correlation between material distribution and various blow molder input parameters may be about equal to or greater than 90%. It will be appreciated that similarly high degrees of correlation may exist between blow molder system input parameters and other measured bottle characteristics, such as mass or thickness distribution.
• the model described below is derived in terms of material distribution, various other suitable container properties may be used in additional to or instead of material distribution.
• FIG. 16 is a flow chart showing one embodiment of a process flow 1600 for programming the processor 142 to control the blow molder 4 based on real-time container output.
• the system may take measurements and derive the material distribution of one or more containers, for example, as described herein above.
• the operation of the blow molder 4 is tuned (e.g., manually) prior to measuring the one or more containers such that the material distribution of the measured containers is correct. Accordingly, the measured containers may establish a baseline material distribution for the model.
• the processor 142 may record (e.g., store in memory) the material distribution of each container along with values of the input parameters for the blow molder 4 at the time that each container was produced. These values may be entered into a multi-dimensional matrix that may be used, for example, as described herein below.
• the processor 142 may generate a model relating blow molder input parameters and material distribution.
• the processor 142 may utilize the matrix to derive the model of blow molder 4 system parameters versus resulting material distributions.
• the model may be generated using any suitable technique or techniques.
• Example modeling techniques that may be used include, for example, linear regression methods, stepwise regression, principle components regression, etc.
• the relationship between blow molder input parameters and material distribution indicated by the model is a relationship between desired changes in material distribution and corresponding changes in blow molder input parameters.
• the model may be tested upon generation, either against the multi-dimensional matrix itself or against new values captured from newly produced containers.
• Testing the model may involve finding a correlation between the actual data points of the matrix (or those of newly produced containers) and the data points predicted by the model.
• the model may be considered validated if the correlation is greater than a predetermined value (e.g., 90%, 95%, 98%, etc.).
• the processor 1608 may modify input parameters of the blow molder system 4.
• the blow molder system 4 may generate additional containers with the new blow molder system input parameters at 1610.
• the measurement system may measure and/or derive the material distribution of the additional containers at 1602, record (e.g., store in memory) the material distribution and new input parameters at 1604 (e.g., to the multidimensional matrix) and determine, again, if the model validates at 1606. In some embodiments, this process is repeated until the model validates.
• the processor 142 may be programmed to drive the material distribution of produced containers to a baseline material distribution. If the material distribution of produced containers deviates from the baseline by more than a threshold amount, the processor 142 may utilize the model to determine a blow molder system 4 control parameter or parameters that may be modified to move the material distribution of subsequently produced containers back towards the baseline material distribution. For example, the material distribution of containers generated by the blow molder system 4 may drift due to changes in the conditions of or at the blow molder system 4.
• the processor 142 may calculate an error signal representing a difference between the material distribution of generated containers and the baseline material distribution. The error signal may represent a desired change in the material distribution of containers generated by the blow molder system 4.
• the error signal may be provided to the model, which may return changes that can be made to the blow molder system 4 input parameters to bring about the desired changes and drive material distribution back to the baseline.
• the initial baseline material distribution may be based on the containers measured to generate the model.
• the model and/or an additionally generated model may be used to correlate material distribution values to section weights, for example, as described in co-pending U.S. Patent Application Serial No. 13/299,949 filed on November 18, 2011 and incorporated herein by reference in its entirety.
• the processor 142 may be programmed to transition to a new baseline material distribution without generating a new model, as described in Figure 16.
• Figure 17 is a flow chart showing one embodiment of a process flow 1700 for changing the baseline material distribution of the blow molder 4.
• the processor 142 may receive a request from a user for a new baseline material distribution.
• the request may include an indication of a new baseline material distribution.
• the indication may be in any suitable form.
• the user may indicate a desired material distribution by providing a thickness, mass, or other desired value for each a plurality of container regions. Any suitable number of container regions may be used, though in some embodiments, the total number of container regions does not exceed the total number of distinct regions of the container that may be measured by the measurement device (e.g., the number of emitter/sensor pairs of the inspection device).
• the processor 142 may determine whether the new baseline material distribution is supported by the model. For example, the model may or may not validate at the new baseline material distribution. In some embodiments, the model may validate at the new baseline material distribution if an indicator of correlation between the new baseline material distribution and one or more sets of blow molder input parameters is greater than a threshold value. In some implementations, the model may be validated for the new baseline material distribution experimentally. For example, the processor 142 may provide the model with the new material distribution baseline, which returns test values for blow molder parameters (e.g. , in the form of changes to the existing values for the parameters). This process may continue in a feedback loop while the processor 142 also monitors the measured material distributions of containers generated by the blow molder system 4. If the measured material distributions approach the new baseline material distribution, the model may be determined to validate.
• the model may be determined to validate.
• the model is more likely to validate at the new baseline material distribution if the measurements taken at 1602, as described above, involved containers having material distributions equal to or similar to the new baseline material distribution.
• the processor 142 may, at 1706, modify the input parameters of the blow molder 4 to drive the material distribution of output containers towards (e.g., within a threshold of) the new baseline.
• the processor 142 may provide to the model a difference between the new baseline and the previous baseline material distribution.
• the processor 142 may instead provide to the model a difference between the new baseline and the material distributions currently being generated by the blow molder 4.
• the model may provide a set of changes to blow molder input parameters to drive the blow molder 4 towards the new baseline material distribution.
• the processor 142 may implement control limits to limit allowable deviation from the original baseline material distribution and/or the allowable incremental changes to blow molder input parameters. If the deviation from the original baseline material distribution is greater than a threshold value, then the new baseline material distribution may be rejected. Also, for example, if in response to the new baseline material distribution, the model returns changes to blow molder input parameters that are outside of the allowable incremental changes, then the model may not be considered to validate at the new baseline material distribution.
• the new baseline material distribution may be expressed by the user (and received by the processor 142) in any suitable form.
• the new baseline material distribution may be expressed with respect to any suitable number of regions.
• Figure 18 is a diagram of an example container 1800 illustrating a material distribution over a set of example container regions. In Figure 18, four regions are indicated, a Top, an Upper Panel, a Lower Panel and a Base. These regions may correspond to the container sections utilized for taking manual section weights.
• the indication of the new baseline material distribution may include a numerical value for the desired weight for each section.
• Figure 18 indicates a material distribution with the Top at 7 grams, the Upper Panel at 5 grams, the Lower Panel at 5 grams and the Base at 8 grams. In some embodiments, additional container regions are used.
• some embodiments include as many container regions as there are locations on the bottle measured by the measurement device.
• Figure 18 also illustrates a series of container locations 1802 whose thickness and/or other material property is measured by the measurement device.
• each location 1802 corresponds to a container region that may have a value in the new baseline material distribution.
• the new baseline material distribution may be provided graphically or numerically.
• the new baseline material distribution may be expressed as a numerical value for each region.
• the new baseline material distribution may be expressed with a graphical representation for each region.
• a graphical user interface provided to the user may comprise a slider bar or other graphical input mechanism for each region.
• Figure 19 is a screen shot showing one embodiment of a user interface 1900 for receiving material distributions, such as the new baseline material distribution, from a user.
• the interface 1900 may be provided through the user interface 202 described herein above.
• the interface 1900 may comprise a plurality of slider bars 1902, with each slider bar
• the interface 1900 may comprise numerical fields 1904 corresponding to the different container regions.
• the numerical fields 1904 may indicate a numerical value for the mass content of each region given the selected material distribution.
• the user can enter mass contents for each region into the numerical fields 1904 in lieu of using the slider bars 1902.
• the interface 1900 may be used to display a material distribution to the user.
• the processor 142 may populate the interface 1900 by manipulating each of the slider bars 1902 and/or numerical fields 1904 to represent a material distribution.
• Material distributions displayed using the interface 1900 may include, for example, the measured material distributions of materials generated by the blow molder 4, and/or a substitute baseline material distribution, as described herein below.
• Figure 20 shows an alternate embodiment of a user interface 2000 for receiving and/or reporting material distributions.
• the interface 2000 comprises nineteen (19) slider bars 1902 and corresponding numerical fields 1904. Also, the respective slider bars 1902 and fields 1904 receive input (and/or are displayed) in units of thickness instead of weight or mass as shown in Figure 19. It will be appreciated that interfaces, as described herein, may provide any suitable level of granularity. For example, some embodiments may provide a slider bar 1902 and/or numerical field 1904 corresponding to each system sensor.
• the processor 142 may be programmed to implement a substitute baseline material distribution in cases where the new baseline material distribution is not supported.
• the processor 142 may calculate the suitable baseline material distribution utilizing the model.
• the substitute baseline material distribution may represent the achievable material distribution that is closest to the new baseline material distribution.
• the model may be analyzed to identify achievable baseline material distributions that are similar to the new baseline material distribution.
• Candidate baseline material distributions may be compared to the new baseline material distribution.
• the achievable distribution closest to the new baseline material distribution may be selected as the substitute baseline material distribution.
• the distance between material distributions may be expressed in any suitable manner, including, for example, an r-squared difference between the masses of the various container regions.
• the substitute baseline material distribution may be determined experimentally.
• the processor 142 may calculate a difference between the current baseline (or currently generated containers) and the requested new baseline material distribution, which may be referred to as an error or error signal.
• the error may be applied to the model, which returns corresponding changes to be made to blow molder 4 input parameters.
• the corresponding changes are modified, for example, if the changes exceed an allowable incremental change in blow molder parameter.
• the processor 142 may provide to the blow molder system 4 modified input parameters (e.g., input parameters considering the corresponding changes). This may affect the material distributions of actual containers produced by the blow molder 4.
• the processor 142 may continue to monitor the error signal and continue to apply new changes to the blow molder 4 input parameters to drive the output material distribution toward the new baseline material distribution. As, in this example, the model does not support the new baseline material distribution, the processor 142 may not succeed in driving the output of the blow molder 4 to the new baseline material distribution. Instead, the processor 142 may drive the output of the blow molder 4 to the substitute baseline material distribution. The processor 142 may determine that the blow molder 4 has arrived at the substitute baseline material distribution, for example, when the average of the error signal begins to stabilize.
• the processor 142 may be programmed to prompt a user when the new baseline material distribution is not supported by the model (e.g. , via interface 202). For example, the processor 142 may prompt the user to indicate whether to proceed to implement a substitute baseline material distribution. Also, in some embodiments, the processor 142 may derive the substitute baseline material distribution and prompt the user for approval before continuing to produce containers at the substitute baseline material distribution.
• plastic container(s) means any type of container made from any type of plastic material including polyvinyl chloride, polyethylene, polymethyl methacrylate, polyurethanes, thermoplastic, elastomer, PET, or polyolefm, unless otherwise specifically noted.
• the software and firmware code may be executed by a processor (such as the processor 142) or any other similar computing device.
• the software code or specialized control hardware which may be used to implement embodiments is not limiting.
• the processors and other programmable components disclosed herein may include memory for storing certain software applications used in obtaining, processing and communicating information. It can be appreciated that such memory may be internal or external with respect to operation of the disclosed embodiments.
• the memory may also include any means for storing software, including a hard disk, an optical disk, floppy disk, ROM (read only memory), RAM (random access memory), PROM (programmable ROM), EEPROM (electrically erasable PROM) and/or other computer-readable media.
• ROM read only memory
• RAM random access memory
• PROM programmable ROM
• EEPROM electrically erasable PROM
• a single component may be replaced by multiple components and multiple components may be replaced by a single component, to perform a given function or functions. Except where such substitution would not be operative, such substitution is within the intended scope of the embodiments.
• processor 142 may be replaced with multiple processors.

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• Engineering & Computer Science (AREA)
• Manufacturing & Machinery (AREA)
• Mechanical Engineering (AREA)
• Length Measuring Devices By Optical Means (AREA)
• Blow-Moulding Or Thermoforming Of Plastics Or The Like (AREA)
• Investigating Or Analysing Materials By Optical Means (AREA)

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• 2013
  • 2013-02-08 WO PCT/US2013/025406 patent/WO2013119999A1/fr not_active Ceased

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