WO2020226921A1 - Entretien préventif prédictif pour processus de production de formation de récipient - Google Patents

Entretien préventif prédictif pour processus de production de formation de récipient Download PDF

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Publication number
WO2020226921A1
WO2020226921A1 PCT/US2020/030024 US2020030024W WO2020226921A1 WO 2020226921 A1 WO2020226921 A1 WO 2020226921A1 US 2020030024 W US2020030024 W US 2020030024W WO 2020226921 A1 WO2020226921 A1 WO 2020226921A1
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WO
WIPO (PCT)
Prior art keywords
container
suggestion
forming apparatus
sensor
blow molder
Prior art date
Application number
PCT/US2020/030024
Other languages
English (en)
Inventor
Sudha JEBADURAI
Robert COWDEN
Paul DIZINNO
Craig White
Original Assignee
Agr International, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Agr International, Inc. filed Critical Agr International, Inc.
Publication of WO2020226921A1 publication Critical patent/WO2020226921A1/fr

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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
    • G01B11/0691—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of objects while moving
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G01B11/245—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures using a plurality of fixed, simultaneously operating transducers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G01N21/9018—Dirt detection in containers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G01N21/9036—Investigating the presence of flaws or contamination in a container or its contents using arrays of emitters or receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G01N21/9081—Inspection especially designed for plastic containers, e.g. preforms
    • 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
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • 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
    • B29C2949/00—Indexing scheme relating to blow-moulding
    • B29C2949/07—Preforms or parisons characterised by their configuration
    • B29C2949/0715—Preforms or parisons characterised by their configuration the preform having one end closed
    • 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
    • B29C49/02—Combined blow-moulding and manufacture of the preform or the parison
    • B29C49/06—Injection blow-moulding
    • 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
    • B29C49/28—Blow-moulding apparatus
    • B29C49/30—Blow-moulding apparatus having movable moulds or mould parts
    • B29C49/36—Blow-moulding apparatus having movable moulds or mould parts rotatable about one axis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
    • G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
    • G01B11/0616—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating
    • G01B11/0625—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating with measurement of absorption or reflection
    • G01B11/0633—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material of coating with measurement of absorption or reflection using one or more discrete wavelengths
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
    • G01N2021/9063—Hot-end container inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • PET Polyethylene terephthalate
  • other types of plastic containers are commonly produced utilizing a blow molder that heats and stretches PET/plastic preforms to produce formed containers.
  • a preform When a preform is received into a blow molder, it is initially heated and placed into a mold.
  • a rod stretches the preform while air is being blown into the preform causing it to stretch axially and circumferentially, and take the shape of the mold.
  • a typical blow molder has between six (6) and forty-eight (48) or more molds.
  • Blow molders need maintenance to produce quality containers. For example, the molds need to be cleaned, the stretch rods need to be re-aligned, valves need to be cleaned and unclogged, etc. During the time that a blow molder is being serviced for maintenance, however, it is unable to produce containers.
  • the present invention is directed to system and associated methods for predicting preventative maintenance for a blow molder or other type of container-forming apparatus.
  • the system comprises a computer- based predictive preventative maintenance system (PPMS) that makes preventive maintenance predictions and suggestions for the container-forming apparatus based on, for example: (i) operating parameters of the container-forming apparatus as sensed by various sensors of the container-forming apparatus and/or (ii) sensed attributes of finished containers produced by the container-forming apparatus. Based on these input data (as well as other possible input data), the PPMS predicts, using a predictive model, preventative maintenance to be performed on the container-forming apparatus.
  • PPMS computer- based predictive preventative maintenance system
  • the objective of the model can be to (a) reduce or minimize downtime of the container-forming apparatus so that the amount of time that the container-forming apparatus can be used to produce containers is increased or maximized, while (b) producing finished containers that meet or exceed quality standards.
  • Figure 1 is a block diagram showing one embodiment of a blow molder system.
  • Figure 2 is a block diagram of one embodiment of a blow molder control system.
  • Figure 3 illustrates one embodiment of a measuring device that may be associated with the material distribution system.
  • Figure 4 is a block diagram showing one embodiment of a base vision system.
  • Figure 5 is a block diagram showing one embodiment of a sidewall vision system.
  • Figure 6 is a block diagram showing one embodiment of a finish vision system.
  • Figure 7 is a diagram showing example finish dimensions that may be measured utilizing the finish vision system.
  • Figure 8 is diagram showing an image of the container illustrating various methods for determining clarity status.
  • Figure 9 is a diagram showing one embodiment of a base temperature sensor system.
  • Figure 10 is a diagram showing one embodiment of a birefringence sensor system for measuring crystallinity and/or orientation.
  • FIG 11 is a diagram showing one embodiment of a near infrared (NIR)
  • FIG. 1 Various embodiments described herein are directed to systems and methods for providing predictive preventative maintenance for a blow molder or other type of container-forming apparatus.
  • the embodiments of the invention are described primarily below in the context of a blow molder that produces plastic or PET containers, but it should be recognized that the description is applicable to other types of container forming apparatuses.
  • the predictive preventative maintenance system (PPMS) of Figure 2 executes a model that relates, in various embodiments, (a) internal operating parameters of the blow molder, (b) attributes of containers produced by the blow molder, and (c) characteristics of the blow molder to (d) preventive maintenance recommendations for the blow molder that (i) reduces downtime of the blow molder while (ii) maintaining the quality of the produced containers.
  • the preventative maintenance suggestions provided by the PPMS 104 may comprise, for example: that certain molds need to be cleaned or replaced; that certain stretch rods or items pertaining to the stretching assemblies need to be re-aligned or replaced; that certain valves need to be cleaned, unclogged or replaced; that certain worn or deteriorating spindle parts need to be serviced or replaced; that certain preform oven heaters need to be cleaned or replaced; that certain seals or nozzles need to be replaced; that certain mold cooling or heating vents need to be cleaned; that certain cams, transfer bearings, or stretching slides need to be lubricated or replaced; that certain transfer grippers need to be repaired or replaced; and/or that certain air filters need to be replaced.
  • FIG 1 is a block diagram showing one embodiment 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 elements to heat the preforms above their glass transition temperature.
  • Many blow molders 6 utilize preform ovens defining multiple heating elements positioned to heat different portions of the preforms.
  • 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 6, using a fluid (e.g., air or a liquid) and/or a core rod, to conform the preform to the shape defined by the mold.
  • a fluid e.g., air or a liquid
  • a core rod e.g., a fluid
  • an initial pre-blow is utilized to begin the container formation process followed by a high-pressure blow to push the now-stretched walls of the preform against the mold.
  • the molds may be heated (a hot mold process) or cooled (a cold mold process).
  • 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 one or more of the inspection systems described below.
  • a reject area 24 has a reject mechanism 26 that may physically remove from the transfer assembly 12 any containers deemed to be rejected.
  • the blow molder system 4 may include alternate inspection areas.
  • the 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 a conveyer or other transport mechanism according to the desired transport path and nature of the system.
  • the blow molder system 4 may comprise one or more inspection areas in addition to or instead of the inspection area 20.
  • alternate inspection areas may be created by adding additional transfer assemblies, such as transport assembly 12.
  • alternate inspection areas may be positioned on a conveyor or other position down-line from the blow molder 6.
  • the blow molder system 4 may produce containers at a rate of 10,000 to 120,000 per hour, though manufacturers continue to develop blow molders with increasing speed and in some embodiments it may be desirable to run the blow molder system 4 at lower rates.
  • the blow molder system 4 receives various inputs parameters that affect the characteristics of the generated containers.
  • the preform oven 2 may receive an overall temperature input parameter, referred to as a preform temperature set point, as well as additional input parameters that define the distribution of heat between the individual heating elements.
  • Other controllable parameters include, for example, a pre-blow timing, a pre-blow pressure, etc.
  • FIG. 2 is a block diagram of one embodiment of a blow molder control system 100.
  • the blow molder control system 100 may comprise, as shown in Figure 2, the blow molder system 4, a blow molder controller 102, a container inspection system 103, and the PPMS 104.
  • the container inspection system 103 may comprise any number of inspection systems or devices that are positioned to sense characteristics of containers 66 produced by the blow molder system 4.
  • the sensors of the container inspection system 103 are“in-line” sensors so that they sense the attributes of the containers 66 as the containers are being produced, without having to pull containers out of the production line for destructive and/or off-line testing or analysis.
  • Various types of inspection systems and devices are described below in connection with Figures 3 through 11.
  • the blow molder controller 102 and the PPMS 104 may be implemented with one or more processor-based devices, such as a PC(s), a server(s), etc.
  • the blow molder controller 102 and the PPMS 104 could be implemented on the same processor-based device(s) or across separate processor-based devices.
  • the blow molder controller 102 receives, for example, (1) signals from the container inspection system 103 indicating characteristics of the containers 66 after formation by the blow molder system 4 and (2) outputs from sensors 82 of the blow molder system 4.
  • the blow molder sensors 82 may comprise, for example, an oven temperature sensor, a preform feed rate sensor, a timer for generating time stamps for when containers are blown, individual mold temperature sensors, perform temperature sensors, pressure sensor(s) for a forming (e.g., blowing) fluid and hydraulic actuators, etc.
  • the blow molder controller 102 can receive data from the blow molder system 4 indicative of the oven temperature, the preform feed rate, the timestamps for when containers are blown, individual mold temperatures, preform temperatures, etc.
  • the blow molder controller 102 can also receive sensor input from the facility (or plant) in which the blow molder system(s) is housed, such as the ambient temperature, atmospheric pressure, and moisture in the facility.
  • the blow molder controller 102 can generate blow molder input process parameters or changes thereto to cause the blow molder system 4 to generate containers within desired container attribute tolerances. More details about such a blow molder controller are described in U.S. Patent 9,539,756,
  • the PPMS 104 can determine predictive preventative maintenance for the blow molder system 4.
  • the PPMS 104 can generate the predictive preventative maintenance using a data model 111 that determines what preventative maintenance to the blow molder system 4 should be carried out in a way that optimizes the objectives of
  • Various different types of inspection systems may be used for the container inspection system 103, such as, for example, a material distribution system, a base vision system, a sidewall vision system, a finish vision system, a base temperature system, a birefringence system, and/or a near-infrared (NIR) spectroscopy system.
  • a material distribution system can measure a material distribution profile of the container 66.
  • the material distribution system finds the material distribution of containers after formation (e.g., either in or downstream of the blow molder system 4).
  • the material distribution system 103 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 material distribution system may utilize measured 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.
  • Calculations for converting raw measurements to a material distribution may be performed by on-board computing equipment associated with the material distribution system 103 and/or by the blow molder controller 102.
  • the material distribution system 103 may utilize any suitable type of measurement device capable of measuring a material distribution profile.
  • Figure 3 illustrates one embodiment of a measuring device 50 that may be associated with the material distribution system.
  • the measuring device 50 may be an in-line inspection system that inspects the containers as they are formed, as fast as they are formed, without having to remove the containers from the processing line for inspection and without having to destroy the container for inspection.
  • the measuring device 50 may determine characteristics of each container formed by the blow molder system 4 (e.g., average 2-wall thickness, mass, volume, and/or material distribution) as the formed containers are rotated or otherwise transported through an inspection area 21 following blow molding.
  • the inspection area 21 may be positioned similar to the example inspection area 20 shown in Figure 1 and/or at any other suitable in-line location, for example, as described above.
  • containers such as the container 66 in Figure 3 are passed through the inspection area 21 of the measuring device 50 by any suitable mechanism such as, for example, a transfer assembly such as the transfer assembly 12, a conveyor, etc.
  • the measuring device 50 may comprise two vertical arms 52,
  • 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.
  • light energy from the emitter assembly 60 that is not absorbed by the container 66 may pass through the two opposite sidewalls of the container 66 and be sensed by the sensors 62.
  • 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 comprises a pair of light emitting diodes (LED's), laser diodes, etc., that emit light energy at different, discrete narrow wavelengths bands.
  • LED's light emitting diodes
  • the emitter assemblies 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 ( 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.
  • adjacent emitter-sensor pairs may be configured to operate at a small time offset relative to one another so as to minimize cross-talk.
  • 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 3, there may be an opening for each sensor 62. There may also be a corresponding opening for 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.
  • One example of a system such as that described above is set forth in U.S. Pat. No. 7,924,421 filed on Aug. 31, 2007.
  • Another type of measuring device utilizes a broadband light source, a chopper wheel, and a spectrometer to measure the wall thickness of the container as it passes through an inspection area 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.
  • a thermal source 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. Examples of similar systems are provided in U.S. Pat. No.
  • the senor(s) 62 may be on the same side of the passing containers as the emitter assembly(ies) 60 and sense light that is reflected by the front and back surfaces of the front sidewall of a passing container.
  • the container inspection system 103 may alternatively or additionally include various vision and other systems including, for example, a base vision system, a sidewall vision system, a finish vision system 112, and/or a base temperature sensor system 114.
  • the inspection system 103 may also include, alternatively or additionally, sensor systems for directly measuring crystallinity.
  • a birefringence sensor may measure crystallinity in cold mold-generated containers.
  • a near infrared (NIR) spectroscopy sensor may measure crystallinity in hot mold-generated containers.
  • NIR near infrared
  • Any or all of the various inspection systems may be configured to operate in-line and inspect the containers as they are formed, as fast as they are formed, without having to remove the containers from the processing line for inspection and without having to destroy the container for inspection.
  • the vision system or systems may be similar to the vision system used in the infrared absorption measurement devices available from AGR International, Inc. of Butler,
  • Figure 4 is a block diagram showing one embodiment of a base vision system 108.
  • the system 108 comprises a camera 202, optics 204, a light source 208 and an optional image processor 210. Images may be taken while the container 66 is in the inspection area 21, with the container 66 positioned vertically between the lower light source 208 and the upper/overhead camera 202. Resulting images may be useful, as described herein below, for determining the presence of haze or pearlescence in the container 66.
  • Images from the camera 202 may be provided to an image processor 210, which may perform various pre-processing and/or evaluate the images to determine properties of the container 66 such as, clarity status (e.g., haze or pearlescence status), (various container dimensions, etc.). Examples of systems for determining the clarity status of blow-molded containers are provided in U.S. Patent 9,539,756, issued January 10, 2017.
  • the image processor 210 is omitted and image processing is performed by the blow molder controller 102.
  • the camera 202 and optics 204 are positioned above the container 66.
  • the optics 204 may include various lenses or other optical components configured to give the camera 202 an appropriate field of view 206 to sense the base area 66 a of the container 66 through the finish 66 b. It will be appreciated that other configurations of the base vision system 108 are also possible. In some embodiments, the positions of the camera/optics 202/204 and light source 208 may be reversed. Also, in some embodiments, additional cameras (not shown) having additional fields of view may be utilized. [0036] Figure 5 is a block diagram showing one embodiment of a sidewall vision system 110. The illustrated example sidewall vision system 110 comprises two cameras 214, 214', two optics assemblies 216, 216' a light source 212 and the optional image processor 210'.
  • Images may be taken while the container 66 is in the inspection area 21, with the container positioned between the light source 212 and the cameras 214, 214'; that is, the light source 212 and cameras 214, 214’ are positioned on opposite sides of the container 66 as shown in Figure 5
  • the two cameras 214, 214' and optics 216, 216' are configured to generate respective fields of view 218, 218' that show sidewall regions 66c of the container 66.
  • the image processor 210' may perform various processing on images generated by the camera 214 including, for example, detecting container defects, detecting the clarity status (e.g., haze or pearlescence status) of the container, etc.
  • the image processor 210' performs pre-processing on images generated by the camera 214, with further processing performed directly by the blow molder controller 102. Also, in some
  • the image processor 210' may be omitted altogether. Also, in some embodiments, the image processor 210' may be omitted altogether. Also, in some embodiments, the image processor 210' may be omitted altogether. Also, in some embodiments, the image processor 210' may be omitted altogether. Also, in some embodiments, the image processor 210' may be omitted altogether. Also, in some embodiments, the image processor 210' may be omitted altogether. Also, in some other things.
  • one or more of the cameras 214, 214' may be omitted and/or additional cameras with additional fields of view (not shown) added.
  • FIG. 6 is a block diagram showing one embodiment of a finish vision system 112.
  • the illustrated example finish vision system 112 comprises a camera 220, optics 222, light sources 224, 226, and the optional image processor 210". Images may be taken while the container 66 is in the inspection area 21, with the container positioned between the light sources 224, 226 and the camera 220, such that the light source 226 and the camera 220 are positioned on opposite sides of the container 66 and such that the light source 224 is above the container 66.
  • the camera 220 and optics 222 are configured to generate a field of view 225 that includes the finish area 66 b of the container 66.
  • the finish vision system 112 comprises a backlight source 226 positioned in the field of view 225 to illuminate the finish 66 b. Also, in some embodiments, the finish vision system 112 comprises a round or bowl shaped light source 224 positioned above the finish 66 b.
  • An image processor 210" may perform various processing on images including, for example, deriving from the images various container characteristics (e.g., dimensions, clarity status, etc.). Some or all of the image processing, however, may be performed by the blow molder controller 102 and, in some embodiments, the image processor 210" may be omitted.
  • Figure 7 is a diagram showing example finish dimensions that may be measured utilizing the finish vision system 112. For example, the dimension H indicates a height of the finish. A dimension A indicates a total width of the finish 66 b. A dimension T indicates a width of the threads 66e of the container 66. A dimension E indicates a width of the seal 66/ of the finish.
  • the various vision systems 108, 110, 112 may be embodied by any suitable type of system capable of generating images of the desired portions of the containers 66.
  • the base and sidewall vision systems 108, 110 may be implemented utilizing the Pilot VisionTM system, available from AGR International, Inc. of Butler, Pennsylvania.
  • the finish vision system 112 may be implemented utilizing the Opti checkTM system, also available from AGR International, Inc. of Butler, Pennsylvania.
  • images from additional perspectives may be obtained by positioning cameras and light sources at different locations, for example, within the inspection area 20 or downstream of the blow molder system 4.
  • outputs of the various vision systems 108, 110, 112 are utilized to determine the presence of haze or pearlescence, generally referred to herein as a clarity status.
  • Processing to determine the clarity status of containers may be performed by the blow molder controller 102 and/or by any of the various image processors 210, 210', 210" described herein. Any suitable image processing algorithm may be utilized to determine haze or pearlescence status (e.g., the clarity status) of a container.
  • Figure 8 is diagram showing an image 240 of the container 66 illustrating various methods for determining clarity status.
  • the image 240 is comprised of a plurality of pixels, where each pixel has a value.
  • each pixel may have a value indicating the brightness of the image at the location of the pixel.
  • the value of each pixel may indicate color as well as brightness.
  • the emphasis area 242 is reproduced in larger form to illustrate image pixels 243.
  • Gray-scale values for various pixels are indicated by shading.
  • the blow molder controller 102 or other suitable processor, may identify instances of haze or pearlescence by examining the images for anomalous pixels.
  • Anomalous pixels may be pixels having a gray scale or other value that is different from the expected value, for example, indicating that the container 66 is darker than expected.
  • Anomalous pixels may be identified in any suitable manner. For example, anomalous pixels may be darker than a threshold value and/or greater than a threshold amount darker than the average of all pixels making up the bottle.
  • Pearlescence or haze may be detected, for example, by identifying a total number of anomalous pixels in the area representing the container 66 and/or a portion thereof (e.g., a base portion). Also, in some embodiments, a size and or number of contiguous groupings of anomalous pixels, such as grouping 244, may be utilized. Results of the algorithm may be expressed in a binary manner (e.g., pearlescence or haze is present; pearlescence or haze is not present) or in a quantitative manner, for example, based on the number of anomalous pixels or pixel groupings.
  • FIG. 9 is a diagram showing one embodiment of a base temperature sensor system 114.
  • the system 114 may comprise a temperature sensor 230 positioned with a field of view 232 that includes the base 66a of the container 66.
  • the temperature of the base 66a of the container 66 may be taken while the container 66 is in the inspection area 21, with the container 66 positioned in the field of view of the temperature sensor 230.
  • the temperature sensor 230 may comprise any suitable non-contact or infrared sensor including, for example, any suitable pyrometer, an infrared camera, etc. Signals from the sensor 230 may be provided to the blow molder controller 102 and/or another suitable processor for deriving a base temperature from the signals. It will be appreciated that various other temperature sensors may be utilized including, for example, a sidewall temperature sensor (not shown) with a field of view directed at the sidewall area 66c of the container 66.
  • FIG 10 is a diagram showing one embodiment of a birefringence sensor system 115 for measuring crystallinity and/or orientation.
  • Birefringence is an effect found in many materials, including PET.
  • a birefringence sensor system 115 may be utilized in conjunction with cold mold-generated containers to measure crystallinity (or orientation) expressed as bi-axial lattice structure. Birefringence occurs when linearly polarized light with two orthogonal components travel at different rates through a material. Because the orthogonal components travel at different rates through the container 66, there is a resulting phase difference between the two light components. The difference in the rates of travel of the light components, and therefore the observed phase difference, depends on the level of crystallinity exhibited by the container.
  • one component may be considered the fast beam and the other a slow beam.
  • the difference in rate, and therefore phase is measured as retardance, which is the integrated effect of birefringence acting along an optical path in a material.
  • Retardance is often measured according to a unit of (nm/cm thickness). Retardance can also be expressed as a phase angle when considering the wavelength of light used.
  • a birefringence sensor system 115 may transmit linearly polarized light through the container 66.
  • the system 115 may comprise an illumination source 250, a polarizer 252, and a sensor 254. Measurements of crystallinity may be taken while the container 66 is in the inspection area 21, which may be positioned between the illumination source 250 and the sensor 254.
  • the illumination source 250 and polarizer 252 may be positioned on one side of the container 66 and configured to illuminate the container 66.
  • the sensor 254 may be positioned on a side of the container 66 opposite the illumination source 250 and polarizer 252 and may be configured to receive the illumination provided by the illumination source 250.
  • the polarizer 252 may be oriented to cause illumination directed towards the container 66 to be linearly polarized with two orthogonal components.
  • the polarizer 252 may comprise two polarizer elements, 252 a,
  • the orientation of the linear polarizer 252 may be rotated about 45° relative to the axis of crystallization of the container 66.
  • a sensor 254 opposite the source may receive the light, including the two orthogonal components.
  • an optional electrically controlled liquid crystal variable polarization device 256 or equivalent that filters the light is placed between the container 66 and the sensor 254.
  • the variable polarization device 256 may be modified to allow the sensor 254 to alternately sense the two formerly orthogonal components of the incident beam and thereby measure the phase difference and/or difference in rate.
  • the angle difference between the positions of the variable polarization device 256 when measuring the two formerly orthogonal components may be proportional to the phase difference.
  • the amount of phase difference per unit thickness of the container walls is the retardance.
  • the end result may be a function of crystallinity and the thickness of the material.
  • the blow molder controller 102 may utilize container thickness (e.g., as measured by the material distribution system 103) to back-out a quantitative measurement of container crystallinity.
  • the system 115 is illustrated in a configuration that directs the illumination through the sidewall regions 66c of the container 66, the system 115 may be configured to measure birefringence through any suitable portion of the container 66.
  • the sensor system 115 also comprises a processor 258.
  • the processor 258 may, for example, process the output of the sensor 254 to generate a crystallinity reading for the container 66.
  • the processor 258 may also be in communication with the variable polarization device 256 to control its polarization value.
  • some or all of these functionalities may be executed by the blow molder controller 102.
  • the processor 258 may be omitted.
  • any suitable method or apparatus may be used for measuring birefringence or retardance.
  • FIG 11 is a diagram showing one embodiment of a near infrared (NIR)
  • the system 117 may be positioned in the inspection area 20 of the blow molder system 4 and/or downstream of the blow molder system 4.
  • a NIR spectroscopy sensor system 117 may be used in conjunction with hot mold-generated containers to measure crystallinity expressed as spherulitic structure.
  • the system 117 comprises an illumination source 260 positioned on one side of the container 66 and a spectrometer 262 positioned the other side of the container 66 opposite the illumination source 260.
  • the illumination source 260 and spectrometer 262 may be configured to measure absorption through the container 66 over all or a portion of the near infrared spectrum.
  • the illumination source 260 and spectrometer 262 may measure absorption across a wavelength range of 800 nm to 3000 nm. In some embodiments, the illumination source 260 and spectrometer 262 may measure absorption across a wavelength range of 2000 nm to 2400 nm.
  • the illumination source 260 and spectrometer 262 may be tuned to a particular wavelength or wavelength range in any suitable manner.
  • the illumination source 260 may be a broadband source generating illumination across the desired wavelength range.
  • the spectrometer 262 may be configured to measure the intensity of the illumination (e.g., after transmission through the container 66) at different wavelengths.
  • the spectrometer 262 may comprise a diffraction grating 266 or other suitable optical device for separating received illumination by wavelength across the desired range (e.g., spatially separating the received illumination by wavelength).
  • a controllable micromirror 268 or other similar device may direct a portion of the spatially separated illumination corresponding to a wavelength or wavelength range to a sensor 269, such as an InGaAs detector.
  • the sensor 269 may provide an output signal proportional to the intensity of the received illumination at the wavelength or wavelength range directed to the sensor 269 by the micromirror 268.
  • the micromirror 268 may be progressively tuned to direct different wavelengths or wavelength ranges to the sensor 269, providing a set of signals from the sensor 269 that indicate absorption of the illumination by the container 66 over the desired wavelength range. This may be referred to as an absorption spectrum or spectrum for the container 66.
  • the amount of illumination that is transmitted by the container 66 at any given wavelength may be the inverse of the absorption of the container 66 at that wavelength.
  • a processor 264 may be configured to control the micromirror 268 and/or receive and store signals from the sensor 269 to determine the absorption spectrum for the container 66.
  • the functionality of the processor 264 may be performed by the blow molder controller 102.
  • the processor 264 may be omitted.
  • the illumination is shown to intersect the container 66 at the sidewall region 66 c, the absorption spectrum may be taken at any suitable portion of the container 66.
  • Figure 11 shows just one example spectrometer 262. Any suitable type of spectrometer may be used.
  • the blow molder controller 102 receives (i) container characteristic data from the various sensors of the container inspection systems 103, (ii) outputs from sensors of the blow molder system 4, and/or (iii) output from sensors from the facility.
  • the container characteristic data describes containers 66 generated by the blow molder system 4.
  • the sensor outputs from the blow molder sensors describe internal operating conditions of the blow molder system 4 as described above, such as the oven temperature, the preform feed rate, time stamps for blowing of the containers such that time lapses since the last blowing can be determined, individual mold temperatures, perform temperatures, etc.
  • the data from the facility sensors can comprise the ambient facility temperature, pressure and moisture, for example.
  • the blow molder controller 102 Based on the container characteristic data, the blow molder controller 102 generates sets of blow molder input parameter changes that, if applied, would move containers generated by the blow molder system 4 towards the baseline container characteristics.
  • the PPMS 104 predicts preventative maintenance that should be performed on the blow molder system 4 based on, preferably, at least the sensed blow molder parameters (e.g., the conditions sensed by the sensors of the blow molders) and the attributes of the finished containers determined by the container inspection system 103.
  • the preventative maintenance suggestions provided by the PPMS 104 may comprise, for example: that certain molds need to be cleaned or replaced; that certain stretch rods or items pertaining to the stretching assemblies need to be re-aligned or replaced; that certain valves need to be cleaned, unclogged or replaced; that certain worn or deteriorating spindle parts need to be serviced or replaced; that certain preform oven heaters need to be cleaned or replaced; that certain seals or nozzles need to be replaced; that certain mold cooling or heating vents need to be cleaned; that certain cams, transfer bearings, or stretching slides need to be lubricated or replaced; that certain transfer grippers need to be repaired or replaced; and/or that certain air filters need to be replaced.
  • the PPMS 104 may also utilize data about the blow molder system itself, such as data about its components, data about its performance, and data about past maintenance and repairs. Such data may be stored in a database 109 and comprise, for example, how the quality of the containers changed
  • the PPMS 104 can report the predicted preventative maintenance to a user interface 107 that is in data communication with the PPMS 104.
  • the user interface 107 may be, for example, a display monitor that an operator(s) of the blow molder system 4 uses to monitor the operation of the blow molder system 4.
  • the PPMS could also send text or email messages to pre-specified addresses (text or email addresses) with the suggested preventative maintenance.
  • the PPMS 104 may use a predictive model 111 to translate the input data (e.g.,
  • the objectives of the predictive model may comprise in certain embodiments: (1) reduce or minimize downtime of the blow molder system while
  • the PPMS’s model 111 may utilize, for example, regression and/or machine learning techniques to learn the preventative maintenance predictions from the available data. Suitable regression techniques include linear regression models, logistic regressions, time series models, classification and regression trees (CART) and/or multivariate adaptive regression splines. Suitable machine learning techniques include, for example, neural networks (including deep neural networks), support vector machines and/or K-nearest neighbors (KNN). The model 111 may utilize one or a combination (e.g., an“ensemble”) of these regression and/or machine learning techniques to make its predictions. The model 111 may be implemented and trained in any suitable manner.
  • the model 111 can be run (or“executed”) by the PPMS 104 in an on-going manner during the operation of the blow molder system 4 to make preventative maintenance predictions during the operational life of the blow molder system 4.
  • the training of the model 111 can be, and preferably is, continual. That is, the model 111 can be refined through continual training as the blow molder system 4 continues to produce containers 66 and as maintenance on the blow molder system 4 is continued to be performed.
  • the present invention is directed to a system that comprises a container-forming apparatus, such as a blow molder, for forming containers, where the container-forming apparatus comprises multiple container-forming
  • the system also comprises a container inspection system for sensing a characteristic of the containers after formation by the container-forming apparatus.
  • the system further comprises a computer system that is in communication with the container inspection system and the sensor of the container-forming apparatus.
  • the computer system comprises a processor (or multiple processors) that is programmed to compute a preventative maintenance suggestion for the container-forming apparatus based on inputs from the sensor of the container-forming apparatus and inputs from the container inspection system.
  • the present invention is directed to a method that comprises the step of forming, by a container-forming apparatus, containers, where the container-forming apparatus comprises multiple container-forming components and a sensor.
  • the method also comprises sensing, by a container inspection system, a characteristic of the containers after formation of the containers by the container-forming apparatus.
  • the method comprises the step of computing, by a computer system that is in communication with the container inspection system and the sensor of the container-forming apparatus, a preventative maintenance suggestion for the container forming apparatus based on inputs from the sensor of the container-forming apparatus and inputs from the container inspection system.
  • the system further comprises a display that is in communication with the computer system, where the display is for displaying the preventative maintenance suggestion computed by the computer system.
  • the method may further comprise performing (by a technician, operator, machine, robot, person, for example) the preventative maintenance suggestion for the container-forming apparatus.
  • the processor is programmed to compute the preventative maintenance suggestion using a computer model that has a composite objective to reduce downtime of the container-forming apparatus and to form containers that satisfy an applicable quality standard for the containers when the container-forming apparatus is operating.
  • the model may comprise a regression mode, a machine learning model, and/or an ensemble of computer models.
  • the method may also comprise continuously training the model as the container-forming apparatus is operated.
  • the processor is programmed to additionally use data about the container-forming apparatus to compute the preventative maintenance suggestion.
  • the data may be stored in a data store of the computer system; and the data may comprise data regarding when the multiple container-forming components of the container-forming apparatus are to be cleaned, serviced and/or replaced.
  • the preventative maintenance suggestion comprises: a suggestion to replace a component of the container-forming apparatus; a suggestion to clean a component of the container-forming apparatus; a suggestion to re-align a component of the container-forming apparatus; and/or a suggestion to lubricate a component of the container-forming apparatus.
  • the preventative maintenance suggestion may comprise: a suggestion to replace a mold of the blow molder; a suggestion to clean a mold of the blow molder; a suggestion to re-align a stretch rod of the blow molder; a suggestion to replace a stretch rod of the blow molder; a suggestion to clean a valve of the blow molder; a suggestion to replace a spindle part of the blow molder; a suggestion to replace a seal of the blow molder; a suggestion to replace a nozzle of the blow molder; a suggestion to clean an oven heater of the blow molder; a suggestion to replace an oven heater of the blow molder; a suggestion to replace a vent of the blow molder; a suggestion to lubricate a component of the blow molder; a suggestion to replace an air filter of the blow molder; a suggestion to repair a transfer gripper of the blow molder; and/or a suggestion to replace a transfer gripper of the blow molder.
  • the processor is further programmed to control an operating parameter of the container-forming apparatus.
  • the processor can control an operating parameter(s) of the container-forming apparatus based on the inputs from the sensor of the container-forming apparatus and the inputs from the container inspection system.
  • the processor could also use the computed preventative maintenance recommendation to control an operating parameter(s) of the container-forming apparatus. For example, if the preventative maintenance recommendation is because a particular component of the container-forming apparatus is stressed, the processor could control an operating parameter of the container-forming apparatus to reduce stress on the component until the suggested maintenance is performed.
  • the processor could use the preventative maintenance recommendation in addition to— or in place of— the inputs from the sensor of the container-forming apparatus and/or the inputs from the container inspection system in making changes to the operating parameters of the container-forming apparatus.
  • the operating parameter may comprise: a temperature set point for the container-forming apparatus; a timing parameter for the container-forming apparatus; and/or a pressure parameter for the container-forming apparatus.
  • the characteristic sensed by the container inspection system comprises a wall thickness of the container.
  • the container inspection system may comprise an in-line container inspection system.
  • the container-forming apparatus is located in a facility and the system further comprises a facility sensor.
  • the processor can be further programmed to control the operating parameter of the container forming apparatus based on inputs from the facility sensor.
  • the facility sensor may be, for example: a temperature sensor that detects ambient temperature in the facility; an atmospheric pressure sensor that detects atmospheric pressure in the facility; a moisture sensor that detects moisture in air in the facility; and/or an electricity meter that senses an amount of electric energy consumed by the facility.
  • the sensor may comprise: an oven temperature sensor; a preform feed rate sensor; a timer that generates time stamps for when the containers are formed; a mold temperature sensor; a pressure sensor for a forming fluid (liquid or gas (e.g., air)) and hydraulic actuators; and/or a preform temperature sensor.
  • the blow molder controller 102 and the PPMS 104 may be implemented with software that is executed by a processor(s).
  • the software of the blow molder controller 102 and the PPMS 104 may be implemented by the same processor or across a common set of processors. In other embodiments, the software of the blow molder controller 102 and the PPMS 104 may be executed by different processors (or different sets of multiple processors).
  • each such computer device may comprise one or more, preferably multi-core, processors and one or more memory units.
  • the memory units may comprise software or instructions that are executed by the
  • the processor(s) may comprise a central processing unit(s) (CPU) and/or a graphical processing unit(s) (GPU).
  • the memory units that store the software/instructions that are executed by the processor(s) may be implemented with primary, secondary, tertiary and/or offline storage.
  • the primary storage may comprise memory, such as RAM or ROM, that is directly accessible by the processor of the blow molder controller 102 and/or the PPMS 104, as the case may be.
  • Secondary storage may comprise external memory that is not directly accessible by the CPU, such as HDDs, SSDs, etc.
  • the software for the blow molder controller 102 and the PPMS 104 may be implemented in computer software using any suitable computer programming language such as C#/.NET, C, C++, Python, Java, Javascript, Objective C, Ruby and using conventional, functional, or object-oriented techniques.
  • Programming languages for computer software and other computer-implemented instructions may be translated into machine language by a compiler or an assembler before execution and/or may be translated directly at run time by an interpreter.
  • Examples of assembly languages include ARM, MIPS, and x86; examples of high level languages include Ada, BASIC, C, C++, C#, COBOL, Fortran, Java, Lisp, Pascal, Object Pascal, Haskell, ML; and examples of scripting languages include Bourne script, JavaScript, Python, Ruby, Lua, PHP, and Perl.

Abstract

Un système d'entretien préventif prédictif informatisé utilise un modèle de données pour effectuer des prédictions d'entretien préventif pour un appareil de formation de récipient qui produit des récipients. Les prédictions d'entretien préventif peuvent être basées sur des conditions de fonctionnement détectées de l'appareil de formation de récipient, des attributs détectés de récipients finis produits par l'appareil de formation de récipient, et des données concernant l'appareil de formation de récipient lui-même. Les objectifs du modèle de données peuvent être de réduire le temps d'immobilisation d'entretien pour l'appareil de formation de récipient tout en maintenant la qualité des récipients produits.
PCT/US2020/030024 2019-05-07 2020-04-27 Entretien préventif prédictif pour processus de production de formation de récipient WO2020226921A1 (fr)

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