Apparatus and method for identifying manufactured components, such as cartridge identification - Patents.com

Abstract

A dispensing system for dispensing a fluid material onto a substrate includes a jet dispenser configured to receive the fluid material therein, the jet dispenser having a jet cartridge operatively connected thereto, the jet cartridge having a nozzle configured to receive the fluid material from the jet dispenser and through which the fluid material is ejected towards the substrate. The dispensing system further includes a camera configured to acquire a digital image of the jet dispenser, and a controller having a memory and a processor. The processor is configured to identify, on the digital image of the jet dispenser, an identification pattern of features present on the jet dispenser, compare the identification pattern to a memory pattern stored in the memory, calculate a similarity between the identification pattern and the memory pattern, and provide an identification value associated with the memory pattern.

Description

[Technical field]

[0001] The present disclosure relates generally to manufacturing systems and, more particularly, to identifying components in manufacturing systems. Further, the present disclosure relates generally to fluid dispensers and, more particularly, to identifying components within fluid dispensers. Even more particularly, the present disclosure relates generally to fluid dispensers and, more particularly, to identifying cartridges within fluid dispensers. [Background technology]

[0002] A manufacturing system is implemented to manufacture and / or modify products, such as substrates, with various equipment, components, and / or the like. For example, a manufacturing system may include a non-contact viscous material dispenser that is sometimes used to apply a viscous material to a substrate. The non-contact viscous material dispenser may include equipment, components, and / or the like, such as cartridges, that are implemented in a manufacturing line. Process control in a manufacturing line is very important. For example, being able to account for and adjust for slight variations in equipment of a non-contact viscous material dispenser, such as cartridges, allows for continued production of good parts under a variety of different conditions. In this regard, it is useful to track information about a particular piece of equipment being used at a given time. Tracking this information allows for the lifespan of a particular piece of equipment to be determined, and this information may be utilized to determine when the particular piece of equipment has exceeded its recommended usage. However, currently, there is no good way to identify and track individual pieces of equipment in a manufacturing system, such as a non-contact viscous material dispenser.

[0003] Therefore, a need exists for a mechanism and method for efficiently identifying individual pieces of equipment used within a manufacturing system, such as non-contact viscous material dispensers. Summary of the Invention

[0004] The aforementioned needs are met by various aspects of components, such as jet dispensers, and various aspects of manufacturing systems, such as dispensing systems, as disclosed. According to one aspect of the present disclosure, a jet dispenser identification system includes a camera configured to capture a digital image of a jet dispenser, and a controller having a memory and a processor. The processor is configured to: identify, on the digital image of the jet dispenser, an identification pattern of features present on the jet dispenser, compare the identification pattern to a memory pattern stored in the memory, calculate a similarity between the identification pattern and the memory pattern, and provide an identification value associated with the memory pattern.

[0005] Optionally, the identification value may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the jet cartridge.

[0006] Optionally, the processor may be configured to compare the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns, and may be further configured to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern.

[0007] Optionally, the processor may be configured to implement a neural network for comparing the identification pattern to a plurality of stored patterns and calculating a similarity between the identification pattern and each of the plurality of stored patterns, and may be further configured to implement the neural network to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern.

[0008] Optionally, the system may be in wired communication with a jet dispenser configured to receive a fluid material therein. Alternatively, the system may be in wireless communication with a jet dispenser configured to receive a fluid material therein.

[0009] According to another aspect, a dispensing system for dispensing a fluid material on a substrate may include a jet dispenser configured to receive a fluid material therein. The jet dispenser may have a jet cartridge operatively connected thereto. The jet cartridge may be configured to receive the fluid material and may have a nozzle configured to eject the fluid material. The dispensing system may further include a jet dispenser identification system including a camera configured to acquire a digital image of the jet dispenser and a controller having a memory and a processor. The processor is configured to identify, on the digital image of the jet dispenser, an identification pattern of features present on the jet dispenser, compare the identification pattern to a memory pattern stored in the memory, calculate a similarity between the identification pattern and the memory pattern, and provide an identification value associated with the memory pattern.

[0010] According to another aspect of the present disclosure, a method of training a neural network to identify a component from a list of stored components is disclosed, the neural network being stored in a memory of a controller and operable by a processor on the controller, the method comprising: introducing a first component to an input device in electronic communication with the controller, the first component having a first characteristic thereon, associating, via the processor, a first identifier associated with the first component with the first characteristic, and storing the association of the first characteristic with the first identifier in the memory; introducing a second component to the input device, the second component having a second characteristic, associating, via the processor, a second identifier associated with the second component with the second characteristic, and storing the association of the second characteristic with the second identifier in the memory.

[0011] Optionally, the input device may comprise a camera configured to capture digital images of the first and second components.

[0012] Optionally, the first and second identifiers may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the first and second components.

[0013] Optionally, the method may include introducing a third component to the input device, the third component having a third characteristic; comparing the third characteristic of the third component to the first characteristic of the first component and the second characteristic of the second component; and receiving from the processor a prediction as to which of the first component and the second component is more similar to the third component.

[0014] Optionally, the method may comprise indicating to the processor whether the prediction was correct or not.

[0015] According to another aspect, a method of identifying a jet cartridge in a dispensing system is disclosed, the dispensing system including the jet cartridge, a camera, and a controller having a processor and a memory, the method comprising activating the camera to capture an image of the jet cartridge, identifying a pattern of features on the jet cartridge visible on the captured image, comparing the identified pattern to a plurality of stored patterns in the memory, activating the processor to select one of the plurality of stored patterns, and displaying an identifier associated with the selected one of the plurality of stored patterns, the selected one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identified pattern.

[0016] Optionally, the method may comprise displaying a measure of similarity between the identified pattern and the selected pattern of the plurality of stored patterns.

[0017] Optionally, the identifier may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number.

[0018] Optionally, the pattern of features may include a bar code.

[0019] Optionally, the comparing and invoking steps may include implementing a neural network.

[0020] Optionally, the method may comprise displaying an accuracy value associated with the identifier.

[0021] According to another aspect of the present disclosure, a manufacturing system for dispensing a fluid material includes a dispenser configured to receive a fluid material therein, the dispenser may have a dispenser component operatively connected thereto, the dispenser component may be configured to receive the fluid material from the dispenser and may have a nozzle configured to eject the fluid material therethrough, the manufacturing system further includes a camera configured to acquire a digital image of the dispenser, and a controller having a memory and a processor, the processor configured to identify, on the digital image of the dispenser, a discriminatory pattern of features present on the dispenser, compare the discriminatory pattern to a memory pattern stored in the memory, calculate a similarity between the discriminatory pattern and the memory pattern, and provide a discriminatory value associated with the memory pattern.

[0022] Optionally, the identification value may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the dispenser component.

[0023] Optionally, the processor may be configured to compare the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns. The processor may be further configured to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern.

[0024] Optionally, the processor may be configured to implement a neural network for comparing the identification pattern to a plurality of stored patterns and calculating a similarity between the identification pattern and each of the plurality of stored patterns. The processor may be further configured to implement the neural network to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern.

[0025] According to yet another aspect of the present disclosure, a method of training a neural network to identify a component from a list of stored components is disclosed, the neural network being stored in a memory of a controller and operable by a processor on the controller, the method comprising: introducing a first component to an input device in electronic communication with the controller, the first component having a first characteristic thereon, associating, via the processor, a first identifier associated with the first component with the first characteristic, and storing the association of the first characteristic with the first identifier in the memory; introducing a second component to the input device, the second component having a second characteristic, associating, via the processor, a second identifier associated with the second component with the second characteristic, and storing the association of the second characteristic with the second identifier in the memory.

[0026] Optionally, the input device may comprise a camera configured to capture digital images of the first and second components.

[0027] Optionally, the first and second identifiers may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the first and second components.

[0028] Optionally, the method may include introducing a third component to the input device, the third component having a third characteristic; comparing the third characteristic of the third component to the first characteristic of the first component and the second characteristic of the second component; and receiving from the processor a prediction as to which of the first component and the second component is more similar to the third component.

[0029] Optionally, the method may comprise indicating to the processor whether the prediction was correct or not.

[0030] According to yet another aspect of the present disclosure, a method for identifying a dispenser component in a manufacturing system is disclosed, the manufacturing system including the dispenser component, a camera, and a controller having a processor and a memory, the method comprising activating the camera to capture an image of the dispenser component, identifying a pattern of features on the dispenser component visible on the captured image, comparing the identified pattern to a plurality of stored patterns in the memory, activating the processor to select one of the plurality of stored patterns, and displaying an identifier associated with the selected one of the plurality of stored patterns, the selected one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identified pattern.

[0031] Optionally, the method may comprise displaying a measure of similarity between the identified pattern and the selected pattern of the plurality of stored patterns.

[0032] Optionally, the identifier may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number.

[0033] Optionally, the pattern of features may include a bar code.

[0034] Optionally, the comparing and invoking steps may include implementing a neural network.

[0035] Optionally, the method may comprise displaying an accuracy value associated with the identifier.

[0036] The present application will be better understood when read in conjunction with the accompanying drawings. For the purpose of illustrating the subject matter, there are shown in the drawings exemplary aspects of the subject matter. However, the subject matter disclosed herein is not limited to the specific methods, apparatus and systems disclosed. [Brief description of the drawings]

[0037] [Figure 1] FIG. 1 is a side view of a dispensing system according to one embodiment of the present disclosure.

[0038] [Diagram 2] FIG. 2 is a perspective view of the jet dispenser of FIG.

[0039] [Diagram 3] FIG. 3 is a perspective view of a jet cartridge according to one embodiment of the present disclosure.

[0040] [Figure 4] FIG. 4 is a bottom view of the jet cartridge of FIG.

[0041] [Diagram 5] FIG. 5 is a perspective view of a portion of the jet cartridge of FIG.

[0042] [Figure 6] FIG. 6 is a cross-sectional view of a portion of a jet dispenser according to one embodiment of the present disclosure.

[0043] [Figure 7A]FIG. 7A is an image of a bottom view of a portion of a jet cartridge according to one embodiment of the present disclosure.

[0044] [Figure 7B] FIG. 7B is an image of a bottom view of a portion of another jet cartridge according to one embodiment of the present disclosure.

[0045] [Figure 7C] FIG. 7C is an image of a bottom view of a portion of yet another jet cartridge according to an embodiment of the present disclosure.

[0046] [Figure 8] FIG. 8 is a schematic diagram of a dispensing system according to one embodiment of the present disclosure.

[0047] [Figure 9] FIG. 9 is a schematic diagram of a learning module according to one embodiment of the present disclosure.

[0048] [Figure 10] FIG. 10 is a schematic diagram of an operational module according to one embodiment of the present disclosure.

[0049] [Figure 11] FIG. 11 is a flow chart of the learning module of FIG.

[0050] [Figure 12] FIG. 12 is a flow chart of the operational modules of FIG.

[0051] [Figure 13] FIG. 13 is a flow chart of a training process according to one embodiment of the present disclosure.

[0052] [Figure 14] FIG. 14 is a flow chart of an operational process according to one embodiment of the present disclosure.

[0053] Aspects of the present disclosure will now be described in detail with reference to the drawings, in which like reference numbers refer to like elements throughout unless otherwise specified. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0054] Components of a manufacturing system, such as cartridges, may have visible features on the component, such as the face of a nozzle. For example, visible features may include machine marks, other subtle imperfections, and / or the like. These may be used by a neural network to accurately recognize and identify the component, such as the cartridge, to which they belong. In particular, it has been found that using deep neural networks and image recognition, it is possible to determine whether a nozzle is clean or dirty. It has also been recognized that, using appropriate lighting, magnification, etc., subtle changes on the surface of each component, such as the nozzle of a cartridge, may become visible. For example, machine marks, changes in brightness, subtle imperfections, etc. These subtle changes may further be used to teach a deep neural network to identify the exact component, such as the cartridge, that is being used. They may be fingerprints of the component, facial recognition of the component, etc. In the case of a machine with a look-up camera installed, this may be an effective way to identify, track, and / or perform similar actions on the use of a component, such as the use of a cartridge. This may be of distinct advantage to the user. Additionally, the disclosed system can potentially be used to identify new physical defects within components, such as on the nozzle face, such as missing nozzles, damaged cartridge faces, and other physical defects.

[0055] Aspects of the present disclosure relate generally to manufacturing systems, and more specifically to identifying components in manufacturing systems. For example, the present disclosure relates generally to fluid dispensers. A non-contact viscous material dispenser can be used to apply a viscous material onto a substrate. For example, a non-contact viscous material dispenser can be used to apply a small amount of viscous material, i.e., a material having a viscosity of more than 50 centipoise, onto a substrate. As used herein, "non-contact" means that the jet dispenser does not contact the substrate during the dispensing process. For example, a non-contact jet dispenser can be used to apply various viscous materials onto an electronic substrate, such as a printed circuit board. The viscous material applied to the electronic substrate may include, by way of example and not limitation, general purpose adhesives, solder pastes, solder fluxes, solder masks, thermal greases, lid sealants, oils, encapsulants, potting compounds, epoxies, die attach fluids, silicones, room temperature vulcanizing (RTV) materials, cyanoacrylates, and / or other suitable materials.

[0056] In semiconductor package assembly, applications include underfill, solder ball reinforcement for ball grid arrays, dam-and-fill operations, chip encapsulation, underfill for chip scale packages, cavity fill dispensing, die attach dispensing, lid seal dispensing, no-flow underfill, flux jetting, thermal compound dispensing, etc. For surface mount technology (SMT) and printed circuit board (PCB) manufacturing, etc., surface mount adhesives, solder pastes, conductive adhesives, solder mask materials, and / or the like can be dispensed from the non-contact dispenser as well as selective flux jetting.

[0057] A jet dispenser may include a pneumatic or electric actuator for repeatedly moving a shaft, tappet, or the like, toward a seat while ejecting droplets of viscous material from the outlet orifice of the dispenser. An electrically actuated jet dispenser may more specifically use a piezoelectric actuator. Accurate ejection of fluid using a valve closing structure that contacts a valve seat may require the shaft to be brought into contact with the valve seat using a predetermined stroke (displacement) and velocity to effectively eject a dot of fluid material from the nozzle outlet. The displacement curve and velocity curve collectively form a motion profile.

[0058] Jet dispensers generally operate to dispense small amounts of fluid material onto a substrate by rapidly impacting a valve seat with a valve member to create a distinct high pressure pulse that ejects a small amount, or droplet, of fluid material from the nozzle of the dispenser, which travels through the air from the nozzle until it impacts the surface or substrate to which the fluid material is to be applied.

[0059] The valve member and nozzle may be housed in a jet cartridge designed for use with such a jet dispenser. The cartridge may be manufactured to specific ratios and tolerances. The cartridge may include different materials and may be manufactured using different tools and processes. Thus, there are various types of cartridges that may be used interchangeably with jet dispensers and may have various distinctions.

[0060] The present disclosure relates generally to manufacturing systems, and more particularly to identifying components in a manufacturing system, although for brevity the disclosure will be described in the context of a fluid dispenser, and more particularly, in the context of identifying cartridges within a fluid dispenser, however, aspects of the disclosure may be applicable to many other applications, implementations, and the like.

[0061] 1 and 2, a dispensing system 90 having a jet dispenser 10 according to one embodiment of the present disclosure is shown. The jet dispenser 10 may include an actuator 12, a jet cartridge 14 operably coupled to the actuator 12, and a fluid reservoir 15 adapted to supply a fluid material to the jet cartridge 14 via a fluid supply tube 16. The fluid material may include a variety of heat sensitive fluid materials such as epoxies, silicones, other adhesives having temperature dependent viscosities, and the like.

[0062] The jet dispenser 10 may be configured to eject fluid material toward a substrate (substrate) 11. The fluid material may be ejected in various manners and / or patterns. For example, the fluid material may be poured, dripped, forcibly pushed, or jetted, etc. In some aspects, the fluid material may be forcefully ejected (i.e., "jetted") from the jet dispenser 10, in which scenario, droplets of the fluid material leave the jet dispenser 10 before contacting the substrate 11. Thus, in a jet-type dispenser, the ejected droplets are "in flight" between the jet dispenser 10 and the substrate 11 and are not in contact with either the jet dispenser 10 or the substrate 11 for at least a portion of the distance between the jet dispenser 10 and the substrate 11. In other types of applications, the piecewise (discretely) jetted droplets of material may remain connected to the jet dispenser 10 (e.g., via thin strands of material) while the droplets are moved toward the substrate 11. In a further aspect, each subsequent drop may be connected to the preceding and / or subsequent drop. Such jetting dispenser embodiments may be used to dispense fluid materials including, but not limited to, underfill materials, encapsulating materials, surface mount adhesives, solder pastes, conductive adhesives, solder mask materials, fluxes, thermal compounds, and the like.

[0063] The jet dispenser 10 may further include a heating element 18 configured to provide heat to the fluid material while it is in the jet dispenser 10. The heating element 18 may include a heater, such as an electric heater, a radiator heater, a convection heater, or the like, and / or a heating coil. At least a portion of the heating element 18 may be disposed adjacent to the jet cartridge 14 such that at least a portion of the jet cartridge 14 contacts the heating element 18. The heating element 18 may be powered by a controllable power source 19 to maintain an optimal temperature and viscosity of the fluid material during operation.

[0064] In use, the actuator 12 is operable to actuate a valve member (not shown) in the jet cartridge 14 to permit the fluid material to be dispensed from the jet dispenser 10 towards the substrate 11. In some aspects, the actuator 12 may be configured to move the valve member to open a passage through the jet cartridge 14 through which the fluid material may flow out of the jet dispenser 10. The fluid material may flow due to gravity, fluid pressure, pneumatic pressure, mechanical pressure, etc. acting on the fluid material. In some aspects, the fluid material may be forced to be ejected, squirted, etc. from the jet cartridge 14 onto the substrate 11. In such aspects, the actuator 12 is configured to move the valve member towards and through the fluid material in the jet cartridge 14 to contact and force at least a portion of the fluid material in the jet cartridge 14 out of the jet cartridge 14 towards the substrate 11.

[0065] 3-6, an exemplary jet cartridge 14 is shown. It will be understood that other jet cartridges may be used with the jet dispenser 10. The jet cartridge 14 may be removably secured to the jet dispenser 10 and may be releasable and removable from the jet dispenser 10. In some aspects, the jet dispenser 10 may be configured to selectively receive and cooperate with different types of jet cartridges 14 and / or multiple jet cartridges 14 of the same type.

[0066] The jet cartridge 14 may include an outer cartridge body 20 and a flow insert (not shown) configured to be received in or on the outer cartridge body 20. The outer cartridge body 20 and the flow insert may be formed of any suitable heat resistant material, such as, for example, 303 stainless steel. The jet cartridge 14 may include a fluid inlet 24 through which a fluid material may be received into the jet cartridge 14. The jet cartridge 14 may further include a fluid outlet 26 through which a fluid material may be ejected from the jet cartridge 14, for example, toward the substrate 11. A fluid passage may be defined within the jet cartridge 14 between the fluid inlet 24 and the fluid outlet 26. It will be appreciated that the jet cartridge 14 may include multiple fluid passages. The fluid passages may include a variety of different shapes, and the disclosure is not limited to a particular fluid passage shape or direction. For example, the fluid passages may be linear or curvilinear. The fluid passageway may include a first portion 22 and a second portion 28 that is angularly offset from the first portion 22. The fluid passageway may extend circumferentially around the jet cartridge 14, for example, about a dispensing axis A (shown in FIG. 3 ). In some aspects, the fluid passageway may include a helical shape and may extend helically along the dispensing axis A.

[0067] A fluid chamber 31 may be defined within the jet cartridge 14 between the fluid inlet 24 and the fluid outlet 26. The fluid chamber 31 may be configured to receive a fluid material from the fluid inlet 24. The fluid chamber 31 may be in fluid communication with a fluid passage. In some aspects, the fluid passage may include the fluid chamber 31.

[0068] The jet cartridge 14 may include a nozzle 40 configured to pass through which the fluid material is ejected from the jet cartridge 14. The nozzle 40 may be disposed on, within, or adjacent to at least one of the outer cartridge body 20 and the flow insert. The nozzle may include a nozzle body 42 and a nozzle tip 44 extending from the nozzle body 42. In some aspects, at least a portion of the nozzle body 42 may be disposed within the jet cartridge 14 and at least a portion of the nozzle tip 44 may be disposed external to the jet cartridge 14.

[0069] In some aspects, the jet cartridge 14 may include a nozzle hub 34 configured to receive a nozzle 40 thereon or therein. The nozzle hub 34 may be secured to the jet cartridge 14, for example to the outer cartridge body 20. The nozzle body 42 may be disposed within the nozzle hub 34, and the nozzle tip 44 may extend exteriorly of the nozzle hub 34.

[0070] The fluid outlet 26 may be defined above or through the nozzle 40. In operation, the actuator 12 may actuate movement of the valve member 32 within the fluid chamber 31 and through the fluid chamber 31 towards the nozzle 40. During such movement, the valve member 32 may contact the fluid material within the fluid chamber 31 and may urge at least a portion of it towards the nozzle 40 and through the fluid outlet 26.

[0071] The outer cartridge body 20 may define a surface 50, at least a portion of which may be orthogonal to the dispensing axis A (see FIG. 3). The outer cartridge body 20 may define a distal face 52 at a distal end of the outer cartridge body 20. At least a portion of the distal face 52 may be orthogonal to the dispensing axis A. The nozzle hub 34 may include a plurality of surfaces 36 defined thereon, at least a portion of each of the plurality of surfaces 36 may be orthogonal to the dispensing axis A. With reference to FIG. 5, the nozzle 40 may define one or more surfaces 46, at least a portion of each of the one or more surfaces 46 may be orthogonal to the dispensing axis A. The one or more surfaces 46 may be disposed on the nozzle body 42, on the nozzle tip 44, or on both the nozzle body 42 and the nozzle tip 44.

[0072] A jet dispenser identification system 92 (see FIG. 8 ) may be utilized to observe the jet dispenser 10 (or another jet dispenser) for online or offline identification. One or more characteristics of the jet dispenser 10 may be detected and / or measured by the jet dispenser identification system 92. In some embodiments, the jet dispenser identification system 92 may be physically connected to the jet dispenser 10 or wirelessly connected to the jet dispenser 10. In some aspects, the jet dispenser identification system 92 may receive information from the jet dispenser 10 during operation of the jet dispenser 10. Alternatively, the jet dispenser identification system 92 may be configured to receive information before or after operation of the jet dispenser 10. The jet dispenser identification system 92 may be configured to receive information from multiple jet dispensers 10 or other suitable multiple jet dispensers.

[0073] A camera 30 may be used to observe the jet dispenser 10 (see FIG. 1). The camera 30 may be attached to the jet dispenser 10 or may be physically separate from the jet dispenser 10. The camera 30 may be oriented to optically capture images and / or video of at least a portion of the jet dispenser 10 and / or the substrate 11. The camera 30 may be oriented along a camera direction B (see FIG. 1). The camera direction B may be parallel to the dispensing axis A. It will be appreciated that the camera 30 may have any suitable viewing angle that defines a viewing area that the camera 30 can capture. The camera direction B may include any direction from the camera 30 within the viewing area. Additionally, the camera 30 may have a support structure configured to move the camera 30 into position along one or more axes, rotate the camera 30 about one or more axes, etc. The support structure may include various motors, controllers, gantries, carriages, etc. for arranging the camera 30 and operating positions, etc. A jet dispenser identification system 92 may include the camera 30.

[0074] The camera 30 may be configured to optically capture and / or record visual data before, during, and / or after operation of the jet dispenser 10. The camera 30 may include one or more separate cameras. The camera 30 may include a charge-coupled device (CCD), a complementary metal oxide semiconductor (CMOS) image sensor, a back-illuminated CMOS, etc. Images captured by the camera 30 may be converted and stored in a variety of formats, including a Joint Photographic Experts Group (JPEG) file format, a Tag Image File Format (TIFF) file format, a RAW feature format, etc. The camera 30 may include lenses, optics, lighting components, etc., as well as controllers for controlling them. The camera 30 may be oriented toward the jet dispenser 10 such that at least a portion of the camera 30 is parallel to the dispensing axis A. The camera 30 may be configured to visually view the jet cartridge 14. In some aspects, the camera 30 may be configured to view the outer cartridge body 20, the nozzle hub 34, the nozzle 40, or the like.

[0075] The camera 30 may be positioned to view the jet dispenser 10 such that the viewing angle of the camera 30 includes a portion that is substantially parallel to the dispensing axis A (i.e., along the camera direction B). That is, images and / or videos viewed and / or recorded by the camera 30 may be captured in a direction parallel to the dispensing axis A. The camera 30 may view one or more surfaces of the jet cartridge 14 described above, such as one or more surfaces 50 of the outer cartridge body 20, one or more distal faces 52 of the outer cartridge body 20, one or more surfaces 36 of the nozzle hub 34, one or more surfaces 46 of the nozzle 40, and / or other surfaces of the remainder of the jet cartridge 14 and / or jet dispenser 10.

[0076] The jet cartridge 14 may include features thereon that may be observed by the camera 30. The features may be located within or on the jet cartridge 14. The features may include markings caused by machining processes during manufacture of the jet cartridge 14, damage caused to the jet cartridge 14 during use, dirt or material buildup on the jet cartridge 14, and the like. In various embodiments, the features may be the shape, contour, outline, texture, variation, continuity, discontinuity, raised portions, recessed portions, flat portions, curved portions, and the like, of a surface, portion of a surface, structure, portion of structure, and the like of the jet cartridge 14. It will be appreciated that the features may include any other attribute that is visually identifiable by the camera 30. The jet cartridge 14 may have multiple features, which may include the same or different combinations of features as described above. FIGS. 7A-7C show multiple exemplary features 60 on the jet cartridge 14.

[0077] 7A-7C show images of a portion of an exemplary jet cartridge 14 captured by camera 30. The images in Figures 7A-7C are shown in a plane perpendicular to the dispensing axis A.

[0078] Each jet cartridge 14 may include a particular quantity, type, and / or arrangement of one or more features 60. Thus, each jet cartridge 14 may have a pattern 114 that is visually identifiable and / or recognizable by the camera 30. Each pattern 114 may include a particular arrangement of one or more features 60 on various surfaces of the jet cartridge 14. In some aspects, each jet cartridge 14 may have a unique, or substantially unique, pattern 114 of one or more features 60. Thus, each jet cartridge 14 may be distinguishable from other jet cartridges 14.

[0079] Some jet cartridges 14 may have patterns 114 that are closer to some jet cartridges 14 than to other jet cartridges 14. Some jet cartridges 14 produced by a first manufacturing process may have patterns 114 that are very similar to one another, but may have very different patterns 114 compared to other jet cartridges 14 produced by a second, different manufacturing process. The differences in patterns 114 may be due to differences in features 60 caused by manufacturing, such as differences in features 60 caused by the use of different materials, different combinations of materials, different manufacturing tools, different manufacturing constraints and tolerances, different manufacturing procedures, etc.

[0080] Thus, a jet cartridge 14 manufactured by a first manufacturing process can be distinguished from a jet cartridge 14 manufactured by a second manufacturing process. Such distinction can be determined by using the camera 30 to compare the patterns 114 of features 60 between different jet cartridges 14. This can be an effective way to identify and track the use of the jet cartridges. This can allow a user to monitor and track the duration of use of the cartridge to determine when the jet cartridge should be removed, replaced, cleaned, etc. Such identification can also be used to identify physical defects in the jet cartridge 14, particularly on the nozzle 40, that appear during use. In some aspects, this identification can be used to identify undesirable physical defects, such as chipped nozzles or damaged cartridge faces, prior to use, allowing a user to replace the defective jet cartridge 14.

[0081] The above-mentioned identification may be used to distinguish between different types of jet cartridges 14. Identification of the pattern 114 of features 60 may be used to distinguish between new jet cartridges 14 and previously used jet cartridges 14. Such identification may help a user determine when a jet cartridge 14 needs to be repaired, replaced, cleaned, etc. This identification may be used to distinguish between various jet cartridges 14 designed to be utilized with different types of jet dispensers 10, different dispensed fluid materials, and / or different substrates. Such identification may help a user determine whether the proper jet cartridge 14 is being utilized within a jet dispenser 10. The identification may be used to distinguish between jet cartridges 14 manufactured by different manufacturing processes as described above. Such identification may help a user determine whether the jet cartridge 14 being utilized is a proper part (or an acceptable substitute) from a genuine equipment manufacturer, or alternatively, whether the jet cartridge 14 is a less desirable or undesirable replica or counterfeit.

[0082] To identify a jet cartridge 14 that is being used, a user can view the jet cartridge 14, for example, along camera direction B and identify a pattern of features 60 present on the jet cartridge 14. The user can view the jet cartridge 14 with the naked eye or with the aid of an optical device. In some embodiments, the user can view the jet cartridge 14 through the camera 30.

[0083] The above identification may be performed by a controller 100 in operative communication with the camera 30. Referring to FIG. 8, an exemplary system 90 is illustrated. The system 90 may include a jet dispenser 10. The system 90 may include a camera 30 and a controller 100. The camera 30 may be configured to receive power from a power source 110 operatively connected to the camera 30. The power source 110 may include a battery, a fuel cell, a solar panel, a wall outlet, etc. The camera 30 may receive power directly from the power source 110 or via the controller 100. In some aspects, the jet dispenser identification system 92 may include the controller 100 and / or the power source 110. The system 90 may include a jet dispenser identification system 92 that is separate from the jet dispenser 10 (see FIG. 8). In some aspects, the jet dispenser identification system 92 may be operatively connected to and utilized in conjunction with the jet dispenser 10, a different jet dispenser, or a combination of the different jet dispenser 10 and other suitable jet dispensers.

[0084] The controller 100 may include or be located on or within a computing device, such as a conventional server computer, a workstation, a desktop computer, a laptop, a tablet, a network appliance, a personal digital assistant (PDA), a digital mobile phone, and / or other suitable computing device. The controller 100 may include a processor 102, a memory 104, a user interface 112, etc. The memory 104 may be a single memory device or multiple memory devices, including, but not limited to, read only memory (ROM), random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or any other device capable of storing digital information. The memory 104 may also include a mass storage device (not shown), such as a hard drive, an optical drive, a tape drive, a non-volatile solid state device, or any other device capable of storing digital information.

[0085] The processor 102 may operate under the control of an operating system resident in the memory 104. The processor 102 may include one or more devices selected from a microprocessor, a microcontroller, a digital signal processor, a microcomputer, a central processing unit, a field programmable gate array, a programmable logic device, a state machine, a logic circuit, an analog circuit, a digital circuit, and / or any other device that manipulates signals (analog or digital) based on operational instructions stored in the memory 104.

[0086] A user interface 112 may be communicatively connected to the controller 100 to allow a system operator to interact with the controller 100. The user interface 112 may include one or more input / output devices. The user interface 112 may include a video monitor, an alphanumeric display, a touch screen, a speaker, and any other suitable audible and / or visual indicators capable of providing information to a system operator. The user interface 112 may include one or more input devices capable of accepting commands or input from an operator, such as an alphanumeric keyboard, a pointing device, a keypad, push buttons, control knobs, a microphone, etc. In this manner, the user interface 112 may enable manual initiation of system functions, for example, during setup, calibration, inspection, and / or cleaning.

[0087] The processor 102 may be configured to control the operation of the camera 30. The processor 102 may include a learning module 106 configured to be used to "teach" or train the processor 102 how to identify the jet cartridge 14. The processor 102 may include an operating module 108 that may utilize the "learned" information from the learning module 106 to identify the jet cartridge 14 during operation of the jet dispenser 10.

[0088] In some aspects, the controller 100 and / or the processor 102 may not implement the learning module 106. In this aspect, the implementation of the teaching or training functionality may be realized in a separate computer system, and the processor 102 may utilize the functionality of the operating module 108. In some aspects, this separate computer system includes any one or more of the functionality of the system 90, which may be a training implementation of the system 90.

[0089] The learning module 106 may be used to train or teach the system 90 to identify images of various jet cartridges 14 and associate the identified images with the type of jet cartridge 14. With reference to FIG. 9, the learning module 106 may include an image identification module 120 and an image association module 122. The image identification module 120 may include instructions sent to the camera 30 to capture an image, images, videos, and / or videos, etc., of the jet cartridge 14 on the jet dispenser 10. The image identification module 120 may digitally identify one or more features 60 on the captured image, video, images, videos, etc., and may identify a pattern of features 60. The image association module 122 may then associate the identified feature pattern 60 with the particular jet cartridge 14 inspected. The association may be made with respect to the name, type, serial number, number, manufacturing lot number, and / or other product identifier of the jet cartridge 14. The product identifier may be entered into the controller 100 by a user via the user interface 112 or may be preprogrammed into software in the memory 104 of the controller 100. The associations made by the image association module 122 may be stored in the memory 104. In some aspects, multiple jet cartridges 14 of the same product identifier may be used to teach the learning module 106. Thus, the learning module 106 may generate multiple associations of different identification patterns for the features 60 for a single type of jet cartridge 14. Multiple patterns of features 60 for jet cartridges 14 of the same identification type may be stored together, averaged together, or otherwise combined to generate a single feature pattern 60 that resembles each of the feature patterns for each of the multiple jet cartridges 14 of the same type that have been observed. A similar learning process may be utilized to generate associations for different types of jet cartridges 14.

[0090] After the system 90 has been "trained" as described above, the system 90 may be used to identify a jet cartridge 14 based on the stored training data. Referring to FIG. 10, during use of the system 90, an operational module 108 of the processor may be utilized to identify the jet cartridge 14. The operational module 108 may include an image identification module 130, a comparison module 132, and a prediction module 136. The image identification module 130 may be configured to receive one or more images and / or videos from the camera 30 of the jet cartridge 14. Each image may include one or more features 60 arranged in a particular pattern that may be unique to one jet cartridge 14 or a set of jet cartridges 14. The comparison module 132 may compare the identified features 60 of each pattern to the stored features 60 and patterns in the memory 104 that were stored during the teaching phase by the learning module 106. The comparison module 132 may identify a best matching pattern of features 60 and a jet cartridge identifier associated with the best matching pattern. The prediction module 136 may then indicate to the user, for example via the user interface 112, that the observed jet cartridge 14 is likely to be the same (type) as the identified associated jet cartridge of the best matching pattern. The comparison module 132 and prediction module 136 may provide the user with a measure of the accuracy of the prediction. The measure of accuracy may be based on how similar the identified pattern is to the best matching pattern. The greater the similarity, the higher the indication of accuracy may be.

[0091] 11, an exemplary learning module 106 is illustrated. During a learning process, such as that described above, a first jet cartridge 14A may be observed by the camera 30. The camera 30 generates one or more images of the first jet cartridge 14A. Each image may include one or more features 60 arranged in a particular first pattern 114A as observed by the camera 30. The generated images may be electronically transmitted to the controller 100, where they may be stored in the memory 104. The first pattern 114A may then be associated with an identifier of the first jet cartridge 14A, such as a name, type, manufacturing lot number, etc. The association may be stored in the memory 104. The above process may be repeated any desired number of iterations. For example, a second jet cartridge 14B may be positioned to be observed by the camera 30. The camera 30 may generate an image of the second jet cartridge 14B, the image having one or more features 60 arranged in a particular second pattern 114B. The second pattern 114B may be associated with an identifier of the second jet cartridge 14B, and the association may be stored in the memory 104. The first jet cartridge 14A and the second jet cartridge 14B may be associated with the same identifier (i.e., may be the same type of jet cartridge 14). Alternatively, the first jet cartridge 14A may be different from the second jet cartridge 14B and may be associated with a different cartridge identifier than the second jet cartridge 14B. The system 90 may be trained to identify and associate any suitable number of different jet cartridges 14A, 14B, ..., 14n, and the training and teaching may utilize any suitable number of iterations of each of the different jet cartridges 14A, 14B, ..., 14n.

[0092] 12, an exemplary process for using the operation module 108 is illustrated. The camera 30 may be directed to observe the jet cartridge 14. The camera 30 may capture an image of the jet cartridge 14. The image may include one or more features 60 arranged in a particular pattern 114. The image with the pattern 114 may be transmitted to the controller 100 and stored in the memory 104. The processor 102 may compare the pattern 114 to one or more of the stored patterns in the memory 104 that were stored during the teaching process by the learning module 106. The comparison module 132 may compare parameters of the features 60 of the pattern 114 to the stored patterns, such as the first pattern 114A and / or the second pattern 114B. The comparable parameters may include the type, size, quantity, color, shape, orientation, and / or other characteristics of the one or more features 60. The comparable parameters may include the relative positions of the features 60. The comparable parameters may include the location of one or more features 60 on the jet cartridge 14, and in particular on the nozzle 40. The prediction module 136 may select the stored pattern 114 that is closest to the identified pattern 114. Because each of the stored patterns 114 is associated with a particular jet cartridge 14, the prediction module 136 may identify the associated jet cartridge of the selected best matching pattern 114.

[0093] In some aspects, the processor 102 may be configured to indicate to the user how similar the pattern 114 is to the closest matching stored pattern. The processor 102 may provide a numerical percentage of similarity between the observed jet cartridge 14 pattern 114 and the closest matching pattern 114. The more similar the two patterns 114 are, the higher the percentage. For example, if the system 90 has identified and stored data related to the first jet cartridge 14A, and during operation the system 90 again observes the first jet cartridge 14A, the system 90 may correctly identify the observed jet cartridge as the first jet cartridge 14A with a high percentage of confidence. In an ideal environment, a perfect match would result in a 100% match. However, it should be understood that manufacturing tolerances, lighting, camera features, other hardware, software, and other components in or around system 90 may interfere with the identification process such that the accuracy of the identification may not be precise.

[0094] The learning module 106 may include a machine learning component to allow the processor 102 to improve accuracy in identifying and matching the jet cartridge 14 based on the observed patterns 114. The teachings of the system 90 may include user-assisted guidance to better train the processor 102. With reference to FIG. 13, an exemplary training process 200 is illustrated. The training process 200 illustrated in FIG. 13 and described below may include any one or more other features, components, arrangements, and / or the like described herein. Aspects of the training process 200 may be performed in a different order consistent with aspects described herein. Additionally, the training process 200 may be modified to have more or fewer processes consistent with various aspects disclosed herein.

[0095] In step 202, a product may be introduced into the system 90 for identification. The product may include a jet cartridge 14. The camera 30 may be configured to generate one or more images of the jet cartridge 14 and features 60 thereon, as described throughout this application. The jet cartridge 14 may be a first jet cartridge 14A. It will be understood that a numerical identification of the jet cartridge is used throughout this application for relative description of the embodiments and processes, but is not intended to be limited to a particular jet cartridge. The processor 102 may store a first pattern 114A of features 60 of the first jet cartridge 14A. During step 204, the processor 102 may associate the identified pattern 114 of features 60 with an identifier for the first jet cartridge 14A. The identifier may be entered by a user or may be preprogrammed into the controller 100.

[0096] In step 206, a second product is introduced that is different from the first product. The second product may be a second jet cartridge 14B. In step 208, the system 90 may receive an image from the camera 30 of the second jet cartridge 14B and may associate the identified second pattern 114B of features 60 with an identifier for the second jet cartridge 14B. At this point in the process 200, the system 90 is trained to distinguish between at least the first jet cartridge 14A and the second jet cartridge 14B.

[0097] In step 210, a third product may be introduced into the system 90 such that the camera 30 is configured to identify and generate an image of the third product. The third product may be the first jet cartridge 14A, the second jet cartridge 14B, or another jet cartridge 14. The processor 102 identifies the features 60 and the patterns 114 of the features 60 of the third product.

[0098] In step 212, the processor 102 may attempt to identify the third product and match it to the best matching stored product using the operational module 108 as described above. The processor 102 may provide the user with a prediction that the product identifier associated with the best matching pattern 114 likely corresponds to the third product. The processor 102 may also provide an accuracy measure, as described above, that provides the user with an indication of how close the third product's pattern 114 is to the best matching pattern 114. The accuracy measure may be a percentage of similarity between the third product's pattern 114 and the best matching pattern 114.

[0099] In step 214, the user indicates to the system 90 whether the prediction is correct. If the prediction module 136 properly identifies the third product, the user so indicates (e.g., via the user interface 112) and the process 200 proceeds to step 216. In step 216, the processor 102 associates the pattern of the third product with the properly identified product (e.g., the first product or the second product) and stores the association in the memory 104. If the association is incorrect, the user so indicates and the process 200 proceeds to step 218. In step 218, the processor 102 may predict a different association than was made in step 212. The process 200 may return from step 218 to step 212 and try again to identify the appropriate association.

[0100] FIG. 14 illustrates an example process 300 for identifying a product using a trained system 90. The product to be identified may be, for example, a jet cartridge 14, as described throughout this application. Process 300 illustrated in FIG. 14 and described below may include any one or more other features, components, arrangements, and / or the like described herein. Aspects of process 300 may be performed in a different order consistent with aspects described herein. Additionally, process 300 may be modified to have more or fewer processes consistent with various aspects disclosed herein.

[0101] In step 302, the system 90 may be configured to observe the jet cartridge 14. The observation may be performed by the camera 30. The camera 30 may capture one or more images of the jet cartridge 14 and transmit the captured images to the controller 100.

[0102] In step 304, the system 90 may identify one or more features 60 on the acquired image or images. The system 90 may detect patterns 114 of the features 60.

[0103] In step 306, the system 90 may compare the identified pattern 114 to one or more patterns (e.g., patterns 114A, 114B, . . . , 114n) stored in the memory 104 during the teaching process 200 or a similar process.

[0104] In step 308, the system 90 may use the aforementioned comparison to identify a stored image having a pattern that most closely resembles the identified pattern 114. The system 90 may compare features of the patterns to identify the closest match. The comparison may examine features 60, specifically the type, size, quantity, color, shape, orientation, etc. of the features, as well as the relative positions of multiple features 60 and / or the position of one or more features 60 on the captured image.

[0105] In step 310, the system 90 may calculate the similarity between the pattern 114 and its features 60 and the best matching pattern from the memory 104. In step 312, the calculated similarity may be displayed to the user. The similarity may be expressed as a percentage. The percentage may indicate to the user how close the best matching pattern is to the retrieved pattern. The higher the similarity, the higher the accuracy percentage may be. For example, if the pattern 114 is exactly (completely) identical to the best matching pattern in the memory 104, the accuracy percentage may be 100% (or slightly less than 100% when considering manufacturing tolerances, optical distinctions, errors, etc.). The user may decide whether the indicated accuracy percentage is high enough to trust the identification of the system 90.

[0106] A user may rely on various acceptable threshold ranges for accuracy. For example, if the accuracy is between 90% and 100%, the user may be confident that the prediction is likely to be correct. On the other hand, if the accuracy is less than 30%, the user may be uncertain about the accuracy of the prediction. Accuracy measurements may also help the user determine wear of the jet cartridge 14. For example, if a particular jet cartridge 14 is identified as 90% consistent with stored data points when new, and after a set period of use, the same jet cartridge 14 is identified as 80% consistent with the same stored data points as before, the change in accuracy may indicate a change in the pattern 114 over time during use. For example, the jet cartridge 14 may receive more or different features 60 during use and / or existing features 60 may be altered during use. Such observations may facilitate the user's ability to determine how quickly a particular component wears out and when it is time to replace or clean that component.

[0107] It should be understood that the foregoing ranges are exemplary and are not intended to limit the applicability of any of the embodiments described herein.

[0108] As previously mentioned, benefits of utilizing the system 90 to identify components may include identification of counterfeit products. In some aspects, counterfeit products may include different features 60 and / or different patterns 114 of features 60 compared to products that are original equipment manufacturers (OEMs). To improve the ability to distinguish between OEM products (or other intended products) and counterfeit products, the OEM products may be manufactured to include one or more features 60 that indicate original (or otherwise approved) parts. For example, the OEM jet cartridge 14 may include a protective feature thereon that is included only on the OEM jet cartridge 14 and not on counterfeit jet cartridges. The protective feature may include any one of the features 60 described herein. The protective feature should be known to the manufacturer of the OEM parts and / or the user of the jet dispenser 10 and / or system 90. Thus, during the identification process described throughout this application, the system 90 may be configured to detect the protective feature during the process of detecting the features 60. If the protective feature is present, the processor 102 may indicate to the user that the product with the protective feature is an OEM product (or otherwise acceptable product). If the protective feature is not detected, the processor 102 may indicate to the user that the product may be a counterfeit.

[0109] The protection features may include any of the features 60 described above. In some aspects, the protection features may include a series of specific shapes, numbers, letters, symbols, etc. In some aspects, the protection features may include a barcode readable by a barcode reader (not shown). In some aspects, the barcode reader may be a separate component within the system 90. In other aspects, barcode reading functionality may be built into the camera 30 or into the controller 100 software.

[0110] The system 90 may include a learning module 106, an operating module 108, a training process 200, a process 300, etc., but in some aspects may be implemented as a neural network, which may include a network of neurons, a circuit of neurons, an artificial neural network, an artificial neuron, an artificial node, etc. The system 90 may include a plurality of neurons with connections that may be modeled using weights, which may reflect excitatory connections, inhibitory connections, etc. The system 90 may receive a plurality of inputs that may be modified by weights and summed, which may be linear combinations. The inputs may include one or more images, product identifiers, etc. The system 90 may generate outputs consistent with the training process 200, process 300, etc., as described above. In particular, the system 90 may generate outputs consistent with steps 310, 312, etc.

[0111] The system 90 may include a learning module 106, an action module 108, a training process 200, a process 300, etc., which may be trained via a data set as described herein, utilizing self-learning gained from experience as it relates to images as described herein. The system 90 may implement information processing paradigms for image recognition, image analysis, etc. The system 90 may implement artificial neurons such as artificial neural networks (ANNs), simulated neural networks (SNNs), etc., which may be substantially connected groups of artificial neurons, using mathematical models, computational models, etc., for information processing based on a connectionistic approach for computation for implementation in the learning module 106, the action module 108, etc. In particular, the system 90 may implement classification, including pattern recognition, pattern detection, etc., for visualization, etc., for implementation in the learning module 106, the action module 108, etc.

[0112] Below are a number of non-limiting examples of aspects of the present disclosure.

[0113] One embodiment provides a jet dispenser identification system comprising a camera configured to capture a digital image of a jet dispenser, the jet dispenser identification system further comprising a controller having a memory and a processor configured to identify, on the digital image of the jet dispenser, an identification pattern of features present on the jet dispenser, compare the identification pattern to a memory pattern stored in the memory, calculate a similarity between the identification pattern and the memory pattern, and provide an identification value associated with the memory pattern.

[0114] The above embodiment may further include any one or a combination of two or more of the following embodiments. In the system of the above embodiment, the identification value may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the jet dispenser cartridge. In the system of the above embodiment, the processor may be configured to compare the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns, and may be further configured to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern. In the system of the above embodiment, the processor may be configured to implement a neural network for comparing the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns, and may be further configured to implement the neural network to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern. In the system of any of the above embodiments, the system may be configured in wired communication with a jet dispenser configured to receive a fluid material therein. In the system of any of the above embodiments, the system may be configured in wireless communication with a jet dispenser configured to receive a fluid material therein.

[0115] In one embodiment, the method includes introducing a first component to an input device in electronic communication with the controller, the first component having a first characteristic thereon. The method further includes associating, via the processor, a first identifier associated with the first component with the first characteristic. The method further includes storing the association of the first characteristic with the first identifier in the memory. The method further includes introducing a second component to the input device, the second component having a second characteristic. The method further includes associating, via the processor, a second identifier associated with the second component with the second characteristic. The method further includes storing the association of the second characteristic with the second identifier in the memory.

[0116] The above embodiment may further include any one or combination of two or more of the following embodiments. In the method of the above embodiment, the input device may have a camera configured to capture digital images of the first and second components. In the method of the above embodiment, the first and second identifiers may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the first and second components. In the method of the above embodiment, the method may include introducing a third component to the input device, the third component having a third characteristic, comparing the third characteristic of the third component to the first characteristic of the first component and the second characteristic of the second component, and receiving from the processor a prediction as to which of the first component and the second component is more similar to the third component. In the method of the above embodiment, the method may include indicating to the processor whether the prediction is correct or not.

[0117] In one embodiment, the method includes activating the camera to capture an image of the jet cartridge. The method further includes identifying a pattern of features on the jet cartridge that are visible on the captured image. The method further includes comparing the identified pattern to a plurality of stored patterns in the memory. The method further includes activating the processor to select one of the plurality of stored patterns. The selected one of the plurality of stored patterns is the one of the plurality of stored patterns that is most similar to the identified pattern. The method further includes displaying an identifier associated with the selected one of the plurality of stored patterns.

[0118] The above embodiment may further include any one or combination of two or more of the following embodiments. In the method of the above embodiment, the method may include displaying a similarity measure between the identified pattern and the selected pattern of the plurality of stored patterns. In the method of the above embodiment, the identifier may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number. In the method of the above embodiment, the pattern of features may include a bar code. In the method of the above embodiment, the comparing and invoking may further include implementing a neural network. In the method of the above embodiment, the method may include displaying an accuracy value associated with the identifier.

[0119] In one embodiment, the manufacturing system comprises a dispenser configured to receive a fluid material therein, the dispenser having a dispenser component operatively connected thereto, the dispenser component having a nozzle configured to receive the fluid material from the dispenser and configured to dispense the fluid material. The manufacturing system further comprises a camera configured to capture a digital image of the dispenser. The manufacturing system further comprises a controller having a memory and a processor. The processor is configured to: identify, on the digital image of the dispenser, an identification pattern of features present on the dispenser; compare the identification pattern to a memory pattern stored in the memory; calculate a similarity between the identification pattern and the memory pattern; and provide an identification value associated with the memory pattern.

[0120] The above-mentioned embodiment may further include any one or a combination of two or more of the following embodiments. In the manufacturing system of the above-mentioned embodiment, the identification value may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the dispenser component. In the manufacturing system of the above-mentioned embodiment, the processor may be configured to compare the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns. The processor may be further configured to select one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern. In the manufacturing system of the above-mentioned embodiment, the processor may be configured to implement a neural network for comparing the identification pattern to a plurality of stored patterns and calculate a similarity between the identification pattern and each of the plurality of stored patterns. The processor may be further configured to implement the neural network for selecting one of the plurality of stored patterns, the one of the plurality of stored patterns being the one of the plurality of stored patterns that is most similar to the identification pattern.

[0121] In one embodiment, the method comprises introducing a first component to an input device in electronic communication with the controller. The method further comprises the first component having a first characteristic thereon. The method further comprises associating, via the processor, a first identifier associated with the first component with the first characteristic. The method further comprises storing the association of the first characteristic with the first identifier in the memory. The method further comprises introducing a second component to the input device, the second component having a second characteristic. The method further comprises associating, via the processor, a second identifier associated with the second component with the second characteristic. The method further comprises storing the association of the second characteristic with the second identifier in the memory.

[0122] The above embodiment may further include any one or combination of two or more of the following embodiments. In the method of the above embodiment, the input device may have a camera configured to capture digital images of the first and second components. In the method of the above embodiment, the first and second identifiers may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the first and second components. In the method of the above embodiment, the method may include introducing a third component to the input device, the third component having a third characteristic, comparing the third characteristic of the third component to the first characteristic of the first component and the second characteristic of the second component, and receiving from the processor a prediction as to which of the first component and the second component is more similar to the third component. In the method of the above embodiment, the method may include indicating to the processor whether the prediction is correct or not.

[0123] In one embodiment, the method comprises activating the camera to capture an image of the dispenser component. The method further comprises identifying a pattern of features on the dispenser component visible on the captured image. The method further comprises comparing the identified pattern to a plurality of stored patterns in the memory. The method further comprises activating the processor to select one of the plurality of stored patterns. The selected one of the plurality of stored patterns is the one of the plurality of stored patterns that is most similar to the identified pattern. The method further comprises displaying an identifier associated with the selected one of the plurality of stored patterns.

[0124] The above embodiment may further include any one or combination of two or more of the following embodiments. In the method of the above embodiment, the method may include displaying a similarity measure between the identified pattern and the selected pattern of the plurality of stored patterns. In the method of the above embodiment, the identifier may include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number. In the method of the above embodiment, the pattern of features may include a bar code. In the method of the above embodiment, the comparing and invoking may further include implementing a neural network. In the method of the above embodiment, the method may include displaying an accuracy value associated with the identifier.

[0125] While the systems and methods have been described with reference to various embodiments in various figures, those skilled in the art will understand that changes may be made therein without departing from the broad inventive concept thereof. It is understood, therefore, that the disclosure is not limited to the particular embodiments disclosed, but it is intended to cover modifications within the spirit and scope of the disclosure as defined by the appended claims.

[0126] The term "plurality," as used herein, means two or more. The singular terms "a," "an," and "the" also include (and are understood to include) plural references, and reference to a particular value includes (and is meant to include) at least that particular value unless the context clearly dictates otherwise. Thus, for example, reference to a "material" is (and is understood to be) a reference to at least one of such materials and equivalents thereof known to those skilled in the art.

[0127] When values ​​are expressed as approximations using the antecedent "about", it will be understood that the particular value forms another embodiment. In general, the use of the term "about" indicates an approximation that may vary depending on the desired properties sought to be obtained by the disclosed subject matter and should be interpreted in the particular context in which it is used based on its function. A person skilled in the art would be able to interpret it as such. In some cases, the number of significant figures used in a particular value may be one of the non-limiting ways of determining the scope of the term "about". In other cases, the scale used in a series of values ​​may be utilized to determine the intended range available to the term "about" for each value. When present, all ranges are inclusive and combinable. That is, reference to values ​​expressed in ranges includes each value within that range. The description of ranges of values ​​herein is merely intended to serve as a shorthand method of individually referring to each separate value within that range, unless otherwise stated herein, and each separate value is incorporated herein as if it were individually set forth herein. All methods described herein may be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context.

[0128] When lists are presented, unless otherwise stated, it is to be understood that each individual element of the list, and every combination of the list, is a separate embodiment. For example, a list of embodiments presented as "A, B or C" should be interpreted as including "A," "B," "C," "A or B," "A or C," "B or C," or "A, B or C."

[0129] As used herein, conditional language such as, inter alia, "can," "could," "might," "may," "for example," and the like, is generally intended to convey that certain embodiments include certain features, elements, and / or steps, while other embodiments do not, unless specifically stated otherwise or understood otherwise within the context in which it is used. Thus, such conditional language is generally not intended to suggest that the features, elements, and / or steps are in any way required by one or more embodiments, or that one or more embodiments necessarily include those features, elements, and / or steps. Terms such as "comprises," "including," "having," and the like, are used synonymously and inclusively without limiting and do not exclude additional elements, features, acts, operations, etc.

[0130] Although specific embodiments have been described, these embodiments are presented by way of example only and are not intended to limit the scope of the inventions disclosed herein. Thus, the foregoing description is not intended to imply that any particular feature, characteristic, step, module or block is necessary or essential. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms, and further, various omissions, substitutions and changes may be made in the form of the methods and systems described herein without departing from the spirit of the inventions disclosed herein. The appended claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the particular inventions disclosed herein.

Claims

1. 1. A jet dispenser identification system, comprising: a camera configured to capture a digital image of the jet dispenser; a controller having a memory and a processor; Equipped with The processor: Identifying, on the digital image of the jet dispenser, an identifying pattern of features present on the jet dispenser; comparing said identification pattern with a stored pattern stored in said memory; calculating a similarity between the identification pattern and the stored pattern; providing an identification value associated with said stored pattern A system configured as follows.

2. The system is configured in wired communication with a jet dispenser configured to receive a fluid material therein.

2. The system of claim 1.

3. The system is configured in wireless communication with a jet dispenser configured to receive a fluid material therein.

2. The system of claim 1.

4. 1. A dispensing system for dispensing a fluid material onto a substrate, comprising: a jet dispenser configured to receive a fluid material therein; The jet dispenser identification system of claim 1; Equipped with the jet dispenser having a jet cartridge operatively connected thereto; The jet cartridge is configured to receive the fluid material and has a nozzle configured to eject the fluid material. A dispensing system comprising:

5. 1. A manufacturing system for dispensing a fluid material, comprising: a dispenser configured to receive a fluid material therein; a camera configured to capture a digital image of the dispenser; a controller having a memory and a processor; Equipped with the dispenser having a dispenser component operatively connected thereto; the dispenser component is configured to receive the fluid material from the dispenser and has a nozzle configured to dispense the fluid material; The processor: Identifying, on the digital image of the dispenser, an identifying pattern of features present on the dispenser; comparing said identification pattern with a stored pattern stored in said memory; calculating a similarity between the identification pattern and the stored pattern; providing an identification value associated with said stored pattern A manufacturing system configured as follows.

6. The identification value includes at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the dispenser component.

10. The system according to claim 1 or 5.

7. The processor: comparing said identification pattern to a plurality of stored patterns; Calculating the similarity between the discrimination pattern and each of the plurality of stored patterns It is structured as follows: Selecting one of the plurality of stored patterns It is further structured as follows: The one of the plurality of stored patterns is the one of the plurality of stored patterns that is most similar to the identification pattern.

6. The system according to claim 1 or 5.

8. The processor: comparing said identification pattern to a plurality of stored patterns; Calculating the similarity between the discrimination pattern and each of the plurality of stored patterns configured to implement a neural network for Selecting one of the plurality of stored patterns and further configured to implement the neural network as follows: The one of the plurality of stored patterns is the one of the plurality of stored patterns that is most similar to the identification pattern. The manufacturing system according to claim 1 or 5.

9. 1. A method of training a neural network to identify a component from a stored list of components, comprising: the neural network is stored in a memory of a controller and is operable by a processor on the controller; The method comprises: introducing a first component into an input device in electronic communication with the controller, the first component having a first characteristic thereon; associating, via the processor, a first identifier associated with the first component with the first feature; storing an association of the first feature with the first identifier in the memory; introducing a second component into the input device, the second component having a second characteristic; associating, via the processor, a second identifier associated with the second component with the second characteristic; storing in the memory an association of the second feature with the second identifier; A method comprising:

10. The input device includes a camera configured to capture digital images of the first and second components.

10. The method of claim 9.

11. The first and second identifiers include at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number associated with the first and second components.

10. The method of claim 9.

12. introducing a third component into the input device, the third component having a third characteristic; comparing the third characteristic of the third component with the first characteristic of the first component and the second characteristic of the second component; receiving from the processor a prediction as to which of the first component and the second component is more similar to the third component; 10. The method of claim 9, further comprising:

13. indicating to the processor whether the prediction was correct.

13. The method of claim 12, further comprising:

14. 1. A method for identifying a dispenser component in a manufacturing system, comprising: the manufacturing system includes the dispenser component, a camera, and a controller having a processor and a memory; The method comprises: activating the camera to capture an image of the dispenser component; identifying a pattern of features on the dispenser component that are visible on the acquired image; comparing the identified pattern to a plurality of stored patterns in the memory; invoking the processor to select one of the plurality of stored patterns; displaying an identifier associated with the selected one of the plurality of stored patterns; Equipped with The selected one of the plurality of stored patterns is the one of the plurality of stored patterns that is most similar to the identified pattern. A method characterized by:

15. the dispenser component is a jet cartridge; The manufacturing system is a dispensing system.

15. The method of claim 14.

16. displaying a measure of similarity between the identified pattern and the selected one of the plurality of stored patterns.

15. The method of claim 14, further comprising:

17. The identifier includes at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number.

15. The method of claim 14.

18. The pattern of features includes a bar code.

15. The method of claim 14.

19. The steps of comparing and invoking further include implementing a neural network.

15. The method of claim 14.

20. displaying an accuracy value associated with said identifier.

15. The method of claim 14, further comprising:

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