Patent Publication Number: US-2022236683-A1

Title: Printing device parameter control using machine learning model, in order to maximize replaceable item lifespan

Description:
BACKGROUND 
     Printing devices can use a variety of different technologies to form images on media such as paper or to build three-dimensional (3D) objects. Such technologies include dry electrophotography (EP) and liquid EP (LEP) technologies, which may be considered as different types of laser and light-emitting diode (LED) printing technologies, as well as inkjet-printing technologies and three-dimensional (3D) printing technologies. Printing devices deposit print material, such as colorant like toner, ink (which can include other printing fluids or material as well), or 3D print material. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an example printing device in which a machine learning model is used to control parameters of a subsystem of the printing device in order to maximize replaceable item lifetime. 
         FIG. 2  is a diagram of an example non-transitory computer readable data storage medium storing program code to control parameters of a subsystem of a printing device using a machine learning model based on measured characteristics of the device, to maximize replaceable item lifespan. 
         FIG. 3  is a diagram depicting example initial training of a machine learning model that is used to control parameters of a subsystem of a printing device to maximize replaceable item lifetime, based on measured characteristics of the printing device. 
         FIG. 4  is a graph of an example function for converting replaceable item lifespan of a data set into a normalized lifespan cost when training a machine learning model to control parameters of a printing device subsystem based on measured characteristics of the printing device. 
         FIG. 5  is a diagram depicting example subsequent adaptive training of a machine learning model that is used to control parameters of a subsystem of a printing device based on measured characteristics of the printing device. 
         FIG. 6  is a flowchart of an example method. 
     
    
    
     DETAILED DESCRIPTION 
     As noted in the background, one type of printing device technology is liquid electrophotography (LEP). In a LEP printing device, a charging unit generates a uniform charge on a photoconductive (i.e., electrophotographic) surface like a drum, which can be referred to as a printing image plate (PIP), as the PIP rotates past the charging unit. A scanned laser beam or other electrostatic discharging mechanism, such as an array of light-emitting diodes (LEDs), selectively discharges the PIP in accordance with an image to form a latent image on the PIP, as the PIP rotates past the discharging mechanism 
     A binary ink developer (BID) of the LEP printing device selectively develops an inked image on the PIP in correspondence with the latent image as the BID engages the PIP. The PIP rotates past another drum on which a heated blanket is mounted, transferring the inked image onto the heated blanket, which melts particles within the ink into a smooth tacky film. The heated blanket transfers the image in film form onto a media sheet as the media advances between the heated blanket and an impression drum. 
     An LEP printing device can include an environmental subsystem. The environmental subsystem of an LEP printing device can capture volatile organic compound vapors dissipated during LEP printing and help ensure that the printing device satisfies regulatory requirements such as safety requirements. The environmental subsystem can also control temperature and humidity within the printing device. 
     An example environmental subsystem may include various components, including different types of heat exchangers such as an air-to-air heat exchanger and a water-to-air heat exchanger, a blower (i.e., fan), a humidifier, air ducts, and a damper, which regulate the return of cooled air to the LEP printing device. The heat exchangers, blowers, humidifier, and damper can be individually controlled via corresponding parameters. For example, the blower and the damper may be asynchronously turned on or off. In some types of LEP printing devices, the damper may control the return of cooled air close to the PIP, thus affecting the temperature of the of the PIP. 
     The PIP is an operator-replaceable item of the printing engine of an LEP printing device, with the other components of the printing engine including the charging unit, the discharging mechanism, the BID, and the heated blanket. The PIP may fail during printing, which can cause visible print defects, so in such instances has to be replaced to maintain print quality the PIP has to be replaced to so that image quality is maintained. Reducing PIP failure incidents—i.e., extending PIP lifespan—can further improve printing device productivity. 
     The temperature of the PIP can have a significant bearing on its lifespan, as well on its prominent role in ensuring print quality. In turn, the environmental subsystem can have a significant effect on PIP temperature, since the damper regulates the return of cooled air close to the PIP. It has been particularly observed that maintaining PIP temperature at a steady state increases its lifespan. 
     Techniques described herein provide for control of environmental subsystem parameters, such as whether environmental components should be on or off, from measured characteristics of an LEP printing device, using a machine learning model. The measured characteristics include physical characteristics, for instance, which may include various temperature and other environmental measurements taken by sensors within the printing device. The measured characteristics can also include other characteristics, such as printing characteristics like print times, print interruption durations, number and timing of print stops and starts, print job length, and so on. Controlling the environmental subsystem parameters using the machine learning model maximizes PIP lifespan. 
     After initial training, the machine learning model can be adapted over time to a particular LEP printing device operating in a particular environment and context. Therefore, the machine learning model becomes attuned to maximize PIP lifespan with respect to the temperature and humidity in which the printing device is operating (i.e., its environment), and with respect to the types of print jobs the printing device is printing and otherwise how the device is being used (i.e., its context). More generally, the techniques described herein control the controllable parameters of a subsystem of a printing device based on measured characteristics using a machine learning model, in order to maximize the lifespan of a replaceable item. 
       FIG. 1  shows an example printing device  100 . The printing device  100  may be a standalone printer or a multifunction peripheral (MFP), multifunction device (MFD), or all-in-one (AIO) printing device. The printing device  100  may print using any type of printing technology, such as a laser or light-emitting diode (LED) technology like a dry electrophotography (EP) or LEP technology, an inkjet-printing technology, or a three-dimensional (3D) printing technology. 
     The printing device  100  includes a printing engine  102 . The printing engine  102  includes the components of the device  100  by which print material, such as colorant like ink or toner, or another print material such as 3D print material, is selectively output from the device  100 . The printing engine  102  may selectively output the print material onto print media like paper to form two-dimensional (2D) images on the print media, or may output the print material to form 3D objects in a layer-by-layer manner. 
     The printing engine  102  includes a replaceable item  104 . The replaceable item  104  may be a replaceable item other than supplies of print material that the printing engine  102  selectively outputs. That is, the replaceable item  104  may be a replaceable item  104  other than the ink, toner, 3D print material or other print material that the printing engine  102  selectively outputs. 
     For example, for a LEP printing engine  102  that (indirectly) electrographically forms images on media using ink and that includes a charging unit, a discharging mechanism, a BID, and a heated blanket, the replaceable item  104  can be the PIP. As another example, for a dry EP printing engine  102  that electrographically forms images on media using toner, the replaceable item  104  may be a fuser that fuses toner onto the media or a transfer belt module that may include the photoconductive surfaces of the engine  102 . As a third example, for an inkjet printing engine  102  that forms images on media by jetting ink, the replaceable item  104  may be the printheads or dies that are separately installable from the supplies of ink. 
     The printing device  100  includes a subsystem  106  for the printing engine  102 . The subsystem  106  may not be part of the printing engine  102 . The subsystem  106  as one example may maintain environmental conditions, such as temperature and humidity, within the printing device  100 , and/or may maintain the surrounding environment of the device  100 , such as by controlling emissions, such as vapor, from the device  100 . For instance, the subsystem  106  may be an environmental subsystem, in the case of an LEP or other type of printing engine  102 . 
     The subsystem  106  may include separately controllable components. For example, as noted above, an environmental subsystem  106  can include different types of heat exchangers, blowers, a humidifier, and valves such as dampers, which may be separately controllable. A blower and a damper may be individually turned on and off and therefore controllable. The air flow through the heat exchangers and the humidifier may also be individually turned on and off, or more continuously controlled. For example, the humidifier may be set to a specific humidity level. 
     The subsystem  106  is controlled by having controllable parameter values  114  set. The subsystem  106  may receive the controllable parameter values  114  as input. Each value  114  may be for a separately controllable parameter of the subsystem  106 . For example, one value  114  may control whether an air-to-air heat exchanger of the subsystem  106  is turned on or off, another value  114  may control whether a water-to-air heat exchanger of the subsystem  106  is turned on or off, a third value  114  may control whether a damper of the subsystem  106  is open or closed, and so on. 
     The printing device  100  includes sensors  108 . The sensors  108  can include temperature sensors that measure temperatures at the locations at which they are disposed within the printing device  100 . The sensors  108  can include humidity sensors that measure humidity at the locations at which they are disposed within the printing device  100 . The sensors  108  can include air flow sensors that measure the rate of air flow at the locations at which they are disposed within the printing device  100 . The sensors  108  can include other types of sensors as well. The sensors  108  thus output physical characteristic measurements  112 , such as various temperatures, humidity, air flow rate, and so on. 
     The printing device  100  includes hardware logic  110 . The hardware logic  110  can include a processor and a non-transitory computer-readable data storage medium storing program code that the processor executes. For example, the hardware logic  110  may be or include an application-specific integrated circuit (ASIC), which is a type of special-purpose processor programmed in accordance with program code, such that the ASIC constitutes both the processor and the data storage medium. The hardware logic  110  may be or include a general-purpose processor that executes program code stored on a separate data storage medium like a semiconductor integrated circuit (IC) or a magnetic storage medium. 
     The hardware logic  110  uses a machine learning model  116  in order to maximize the lifespan of the replaceable item  104 . In the example of  FIG. 1 , the machine learning model  116  is stored on and is thus part of the hardware logic  110 . In another implementation, the machine learning model  116  may be stored on a computing device separate from the printing device  100 . For example, the computing device may be a host computing device to which the printing device  100  is communicatively connected and from which the printing device  100  receives print jobs. As another example, the computing device may be a server computing device to which the printing device  100  is communicatively connected over a network, and from which the device  100  may or may not receive print jobs. The network may be a local network or a remote network, the latter which can include a cloud-computing environment. 
       FIG. 2  shows an example non-transitory computer-readable data storage medium  200  storing program code  202  that the hardware logic  110  of the printing device  100  executes. The data storage medium  200  may constitute or otherwise be part of the hardware logic  110 . The hardware logic  110  executes the program code  202  to perform processing to control the subsystem  106  of the printing device  100  using the machine learning model  116 . 
     The hardware logic  110  receives as input physical characteristic measurements  112  from the sensors  108  ( 204 ). The logic  110  can also receive measurements of other characteristics, such as printing characteristics like print times, print interruption durations, number and timing of print stops and starts, print job length, and so on. The hardware logic  110  determines the controllable parameter values  114  by which the subsystem  106  is controlled ( 206 ). The hardware logic  110  sets the controllable parameters of the subsystem  106  to the determined values  114  ( 208 ). The hardware logic  110  thus provides as output the controllable parameter values  114  for the subsystem  106 . 
     The hardware logic  110  specifically determines the controllable parameter values  114  based on the received physical characteristic measurements  112 , and based on any other received characteristic measurements, using the machine learning model  116 . The hardware logic  110  provides the physical characteristic measurements  112  and the measurements of any other characteristics as input to the machine learning model  116 , and receives the controllable parameter values  114  as output from the machine learning model  116 . The hardware logic  110  thus controls the subsystem  106  by setting parameters of the subsystem  106  to the values  114  determined using the machine learning model  116  to maximize lifespan of the replaceable item  104 , based on the characteristic measurements. 
       FIG. 3  shows example initial training of a machine learning model  326  to control subsystem parameters based on measured characteristics, in order to maximize replaceable item lifespan. Training data  300  can be historical data regarding printing devices of the same kind, such as the same manufacturer and model. The printing devices may be operating in the same or different locations, and thus within the same or different environments. The printing devices may be operating in the same or different contexts, in that the devices may be printing the same or different print job types and/or have the same or different print job loads. The training data  300  may instead be simulated data as opposed to actual historical data that has been collected. 
     The training data  300  includes multiple data sets  302 . Each data set  302 , or data case, corresponds to the usage of a replaceable item of a printing device over the lifespan of the item, from first use after insertion into the device through last use after which the item is replaced within the device. Each data set  302  includes a number of pairs  303  of physical measurements  304  and controllable parameter values  306 . 
     There may be tens of thousands, or more, pairs  303  for each data set  302 . The number of pairs  303  can correspond to the number of times the values  306  of the subsystem&#39;s parameters were set and/or the number of times measurements  304  the printing device&#39;s characteristics were taken. For example, each pair  303  can correspond to a different time at which the parameters  106  of the subsystem  106  were set to values  114  as determined based on measurements like the measurements  112  provided by the sensors  108  of  FIG. 1 . The measurements  304  and the parameter values  306  may be preprocessed using a sliding data window technique or in another manner. 
     Each data set  302  includes the lifespan  308  of a corresponding replaceable item. The lifespan  308  is the actual lifespan of the replaceable item, from first insertion of the item into a printing device through final removal from the device. The lifespan  308  may be measured in number of page impressions, such as the number of pages that were printed while the replaceable item was used in the printing device. 
     Each data set  302  includes a number, or count, of warning events  310  that occurred within the printing device over the lifespan  308  of the corresponding replaceable item. Warning events are non-critical events that did not result in failure (and thus replacement) of the item, but nevertheless may have shortened the lifespan  308  of the item or at least affected print quality. An example of a warning event includes the temperature of a printing engine component or the replaceable item exceeding a recommended operating temperature. Another example is user-requested calibration of the printing device not having been performed. 
     Each data set  302  includes a reason for replacement  312  of its corresponding replaceable item within the printing device. The reason for replacement  312  may also be considered as reason of failure of the replaceable item. In the context of an LEP printing device, example reasons for replacement  312  can include fused ink; electrical failure; cracks; other damage; scratches; and so on. There may be a set list of potential reasons for replacement from which the reason for replacement  312  of each data set  302  is selected. 
     In the example of  FIG. 3 , the lifespans  308  of the replaceable items of the data sets  302  are converted to corresponding lifespan costs  314 . Each lifespan cost  314  normalizes the lifespan  308  of the replaceable item of a corresponding data set  302 . Each lifespan cost  314  may be a value from zero to one, where zero represents that the corresponding replaceable item had a lifespan  308  below a specified minimum lifespan and one represents that its lifespan  308  exceeded a specified maximum lifespan. 
     In the example of  FIG. 3 , the numbers of warning events  310  of the data sets  302  are converted to corresponding event occurrence frequencies  316 . Each event occurrence frequency  316  is a normalized occurrence frequency of the warning events that occurred during the lifespan  308  of the replaceable item of a corresponding data set  302 . Each event occurrence frequency  316  may be a value from zero to one, and may be computed as the number of warning events  310  of a corresponding data set  302  divided by the lifespan  308  of the replaceable item for that data set  302 . 
     In the example of  FIG. 3 , cost factors  318  for the data sets  302  are computed from the lifespan costs  314  and the event occurrence frequencies  316 . For instance, the cost factor  318  of a data set  302  can be the sum of the data set  302 &#39;s lifespan cost  314  and event occurrence frequency  316 . For each data set  302 , the cost factor  318  represents the lifespan cost  314  for the replaceable item as penalized by the frequency  316  of warning events that occurred over the item&#39;s lifespan  308 . 
     In the example of  FIG. 3 , the reasons for replacement  312  of the data sets  302  are mapped to field-adjustable weights  320 . Each reason for replacement  312  is mapped to a corresponding weight  320 . For instance, if there is a set list of potential reasons for replacement from which the reason for replacement  312  of each data set  302  is selected, each reason of the list has a corresponding weight  320 . 
     The weight  320  of a data set  302  indicates the severity of failure or cause of replacement of the replaceable item. A fused ink failure, for instance, may be considered more severe than an electrical failure of the replaceable item. An electrical failure, in turn, may be considered more severe than a crack in the replaceable item. 
     The weights  320  are field-adjustable in that the weights  320  to which the potential reasons for replacement are mapped may be user-adjustable for a particular printing device or for a particular environment in which printing devices are operating. For example, the user at one location may deem certain reasons for replacement  312  as more severe than other reasons for replacement  312  as compared to the user at another location. Therefore, users can customize the weights  320  to which the reasons for replacement  312  are mapped to fit their particular scenarios. 
     In the example of  FIG. 3 , cost function values  322  for the data sets  302  are computed from the cost factors  318  and field-adjustable weights  320 . The cost function values  322  are values of a cost function that the machine learning model  326  minimizes. That is, the machine learning model  326  determining subsystem parameter values from printing device characteristic measurements by minimizing the cost function. Setting the parameters of the subsystem to the values determined by the machine learning model  326  maximizes replaceable item lifespan. 
     The cost function is associated with the lifespan of the replaceable item. In the example of  FIG. 3 , for each data set  302 , the value  322  of the cost function is computed from the data set  302 &#39;s cost factor  318  and field-adjustable weight  320 . For example, the cost function may multiply the cost factor  318  by the field-adjustable weight  320 . 
     Machine learning model training  324  is performed to train the machine learning model  326  from the training data  300 . Specifically, training  324  occurs on the basis of the pairs  303  of characteristic measurements  304  and controllable parameter values  306  of the data sets  302 , as well as the cost function values  322  for the data sets  302  computed from their lifespans  308 , numbers of warning events  310 , and reasons for replacement  312 . The machine learning model  326  may be a neural network (NN) or other type of machine learning model. 
       FIG. 4  shows a graph  400  of an example function  402  for converting the replaceable item lifespan of a data set into a normalized lifespan cost. The function  402  can be used to convert the lifespan  308  of each data set  302  to a lifespan cost  314  in  FIG. 3 . The x-axis  404  of the graph  400  corresponds to lifespan, such as in number of impressions, and the y-axis  406  corresponds to lifespan cost. 
     Below a minimum lifespan  408 , the lifespan cost is at a maximum value, such as one. This means that when a replaceable item has a lifespan that does not even reach the expected minimum lifespan, the associated lifespan cost is at its greatest. The minimum lifespan  408  may be 15,000 page impressions, as one example. 
     Above a maximum lifespan  410 , the lifespan cost is at a minimum value, such as zero. This means when a replaceable item has a lifespan that exceeds the expected maximum lifespan, the associated lifespan cost is at its smallest. The maximum lifespan  410  may be 80,000 page impressions, as one example. 
     Between the minimum and maximum lifespans  408  and  410 , the lifespan cost decreases linearly from one to zero in the example of  FIG. 4 . The range between the minimum and maximum lifespans  408  and  410  corresponds to the expected lifespan range of a replaceable item. The lifespan cost may decrease non-linearly, instead of linearly as in  FIG. 4 . 
       FIG. 5  shows example adaptive training of the machine learning model  326  after the model  326  has been initially trained. During printing device usage  502 , a subsystem of the printing device has its parameters controlled via values  510  that the machine learning model  326  determines from measurements  508  of the device&#39;s characteristics. Each time the machine learning model  326  determines parameter values  510  from characteristic measurements  508 , the measurements  508  and values  510  are collected as a pair  512 . Over the lifespan of the replaceable item, running data  500  is collected as a number of such pairs  512 . 
     The running data  500  also includes the number of warning events  518  that occurred during the lifespan of the replaceable item. During printing device usage  502 , each time a warning event  514  occurs, the number of warning events  518  is incremented  516 . The warning events  514  can occur asynchronously to control of the subsystem using the machine learning model  326 . The pairs  512  of measurements  508  and parameter values  510  are thus collected asynchronously to incrementing  516  of the number of events  518 . 
     At some point during printing device usage  502 , the replaceable item is replaced  504  within the printing device. At time of replacement  504 , the remainder of the running data  500  is specified, including the lifespan  520  of the replaceable item that has been replaced, and the reason for replacement  522  of the item. A user may manually specify the reason for replacement  522  from a set list of potential reasons for replacement after inspection of the item. 
     An event occurrence frequency  524  is computed from the number of warning events  518  of the running data  500 , as is the lifespan cost  526  from the lifespan  520  of the replaceable item that has been replaced, so that a cost factor  528  can be computed, as in  FIG. 3 . Similarly, the reason for replacement  522  is mapped to a field-adjustable weight  529  as in  FIG. 3 , and a cost function value  530  computed from the cost factor  528  and the weight  529 . Adaptive training  506  of the machine learning model  326  can then occur from the cost function value  530  and the pairs  512  of measurements  508  and parameter values  510 . 
     The running data  500  on which basis adaptive machine learning model training  506  occurs can include as few as one data set: the data set that is generated when a replaceable item is replaced within the printing device. In another implementation, adaptive training  506  may not be performed until the replaceable item has been replaced within the printing device multiple times. The machine learning model  326  may be an adaptive NN or other type of adaptive machine learning model, and may be adaptively trained in a reinforced learning or other manner. 
     The machine learning model  326  may be adaptively trained on a per-printing device basis, or on a per-location basis for multiple printing devices residing at the same location. For example, the machine learning model  326  may initially be generically trained pursuant to  FIG. 3 , and an instance of the model  326  subsequently adaptively trained for each printing device pursuant to  FIG. 5 . Such adaptive training of the machine learning model  326  for each printing device renders it more specific to the environment and context in which each device is being used. 
       FIG. 6  shows an example method  600 . The method  600  includes training a machine learning model that provides values for controllable parameters of a subsystem for a printing engine of a printing device from characteristic measurements of the printing device ( 602 ). Setting the parameters to the values provided by the machine learning model maximizes a lifespan of a replaceable item. The machine learning model may be initially trained as a generic machine learning model not specific to the environment and context in which the printing device is used. The method  600  includes using the machine learning model to set the controllable parameters of the subsystem ( 604 ). As the replaceable item is replaced within the printing device, the method  600  may adaptively train the machine learning model to render it more specific to the environment and context in which the device is used. 
     Techniques have been described herein for controlling parameters of a subsystem for a printing engine of a printing device, such as the environmental subsystem of an LEP printing device. The parameters are controlled based on measured characteristics of the printing device, using a machine learning model, to maximize lifespan of a replaceable item of the device, such as the PIP of an LEP printing device. The machine learning model can minimize a cost function associated with the replaceable item lifespan.