Patent Publication Number: US-11642823-B2

Title: Systems and approaches for autotuning an injection molding machine

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This non-provisional application is a continuation of U.S. application Ser. No. 16/432,403, entitled “Systems and Approaches for Autotuning an Injection Molding Machine”, filed Jun. 5, 2019, which claims the benefit of the filing date of U.S. Provisional Application No. 62/692,265, entitled “Systems and Approaches for Autotuning an Injection Molding Machine”, filed Jun. 29, 2018, the entirety of each of which is hereby incorporated by reference. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates generally to injection molding and, more particularly, to approaches for autotuning control parameters injection molding machines in response to varying operational parameters. 
     BACKGROUND 
     Injection molding is a technology commonly used for high-volume manufacturing of parts constructed of thermoplastic materials. During repetitive injection molding processes, a thermoplastic resin, typically in the form of small pellets or beads, is introduced into an injection molding machine which melts the pellets under heat and pressure. In an injection cycle, the molten material is forcefully injected into a mold cavity having a particular desired cavity shape. The injected plastic is held under pressure in the mold cavity and is subsequently cooled and removed as a solidified part having a shape closely resembling the cavity shape of the mold. A single mold may have any number of individual cavities which can be connected to a flow channel by a gate that directs the flow of the molten resin into the cavity. A typical injection molding process generally includes four basic operations: (1) heating the plastic in the injection molding machine to allow the plastic to flow under pressure; (2) injecting the melted plastic into a mold cavity or cavities defined between two mold halves that have been closed; (3) allowing the plastic to cool and harden in the cavity or cavities while under pressure; and (4) opening the mold halves and ejecting the part from the mold. 
     In these systems, a control system controls the injection molding process according to an injection pattern that defines a series of setpoint values for control parameters of the various components of the injection molding machine. For example, the injection cycle can be driven by a fixed and/or a variable melt pressure profile whereby the controller uses sensed pressures at a nozzle as the input for determining a driving force applied to the material. 
     Changes in molding conditions can significantly affect properties of the molten plastic material. As an example, material specification differences between resin batches and changes in environmental conditions (such as changes in temperature or humidity) can raise or lower the viscosity of the molten plastic material. When viscosity of the molten plastic material changes, quality of the molded part may be impacted. For example, if the viscosity of the molten plastic material increases, the molded part may be “under-packed” or less dense due to a higher required pressure, after filling, to achieve optimal part quality. Conversely, if the viscosity of the molten plastic material decreases, the molded part may experience flashing as the thinner molten plastic material is pressed into the seam of the mold cavity. Furthermore, recycled plastic material that is mixed with virgin material may impact the melt flow index (MFI) of the combined plastic material. Inconsistent mixing of the two materials may also create MFI variation between cycles. 
     Some conventional injection molding machines do not adjust the injection cycle to account for these changes in material properties. As a result, these injection molding machines may produce lower quality parts, which must be removed during quality-control inspections, thereby leading to operational inefficiencies. Moreover, as an injection molding run may include hundreds, if not thousands, of injection cycles, the environmental conditions of the injection molding machine may not be constant across each injection cycle of the run. Thus, even if the injection cycle is adapted to account for the environmental factors at the onset of the run, the changing environmental conditions may still result in the production of lower quality parts during injection cycles executed later in the run. 
     Additionally, a reliance on an injection cycle based on a fixed melt pressure pattern, the injection cycle may not be capable of properly injecting materials having varying characteristics (e.g., regrind, biodegradable, and/or renewable materials). Additionally, while some systems may use an adjustable melt pressure pattern, these systems are oftentimes incapable of maintaining material tolerances when material specifications (e.g., viscosity and part density) do change. As a result, these systems may produce inconsistently-dimensioned parts, thus further increasing operational inefficiencies. 
     SUMMARY 
     Embodiments within the scope of the present invention are directed to the control of injection molding machines to produce repeatably consistent parts by automatically retuning the control parameters of an injection molding machine based on the operation of the injection molding machine. Systems and approaches for controlling the injection molding machine include first obtaining a model of the injection molding machine, a mold, and/or a material to determine an initial set of control parameters for the injection molding machine. For example, the control parameters may include a melt pressure profile and/or gain values for a proportional-integral-derivative (PID) controller. Operation of the injection molding machine is measured during the injection cycle. When operation is outside of an expected range of operation, the control parameters are automatically tuned (e.g., adjusted based upon current operation of the injection molding machine). 
     As compared to conventional, fixed control of an injection molding process across the various injection cycles of a run of injection cycles, automatically tuning the control parameters can reduce the number of oscillations that occur and/or reduce the magnitude of the oscillations that do occur. Reducing the oscillations improves how closely the performance of the injection molding machines matches the setpoints defined by the injection cycle. Automatically tuning the control parameters also causes the injection molding machine to achieve steady state values that more closely match those defined by the injection cycle. As a result, the consistency at which the injection molding machines produces molded parts is improved. 
     In various embodiments, a controller of the injection molding machine may be operatively connected to one or more sensors that monitor the operating conditions of the injection molding machine. For example, one sensor may monitor a screw position; another sensor may monitor a velocity at which the screw rotates; still another sensor may monitor a mold cavity pressure; and yet another sensor may monitor a temperature of a thermoplastic material or of a heated barrel. The controller can obtain the sensor data generated by the one or more sensors to determine whether or not the operation of the injection molding machine is within the expected range of operation. 
     In some embodiments, the controller compares a single parameter to a threshold value. For example, an overshoot pressure may exceed a threshold amount, an error in steady-state pressure may exceed a threshold amount, or a humidity in the injection molding machine&#39;s ambient environment may have shifted beyond a threshold amount. In additional or alternative embodiments, the controller may combine the sensor data to generate a composite metric or score that is compared to a threshold value. For example, the sensor data may be combined to determine a metric indicative of the viscosity of the molten material. In some embodiments, one or more of the characteristics of the injection molding machine, mold, and/or the molten material indicated by their respective models are also used to determine the composite metric. 
     In some embodiments, one or more machine learning techniques are applied to determine the composite metric and/or the threshold value to which the composite value is compared. For example, in some implementations, performance of a plurality of injection cycles is monitored for a plurality of different injection molding machines, molds, and molten materials. Accordingly, this historical data can be used as an input to train the machine learning algorithm to correlate the characteristics of the injection molding machine, mold, and/or molten material compiled in their respective models and their impact on the measured response to being controlled in accordance with the injection cycle. 
     Therefore, the controller may determine the need to adjust the control parameters of the injection molding process with more accuracy than conventionally possible. Moreover, when compared to conventional injection molding systems that rely on manual monitoring of the injection molding machine, the present techniques enable the determination of the need to adjust the control parameters based on relationships beyond those capable of manual observation. 
     Further, different injection molding machines, molds, and/or thermoplastic materials may exhibit different performance characteristics when following the same injection pattern. For example, some injection molding machines may be used more frequently than other injection molding machines. Accordingly, moving parts in the injection molding machine may exhibit higher or lower resistivity depending on the particular effects caused by wear and tear. As another example, different injection molding machines may be manufactured by different companies using different processes. These differences may be quantified and represented by the model of the injection molding machine. 
     In some embodiments, the mold may also be modeled. The model of the mold may include data associated with historic injection cycles executed by injection molding machines. For example, the data may include an identifier of the injection molding machine that executed the mold cycle, a plurality of injection pressure or injection velocity values sensed over the course of the mold cycle, or other characteristics of injection molding machine when executing the mold cycle. 
     In some further embodiments, the molten material may also be modeled. The model of the molten material may include a MFI associated with the material and/or a correlation between molten material MFI and the ratio of regrind to unused molten material. 
     In various embodiments, the controller is also operatively connected to a model database that stores the models representative of injection molding machines, molds, and/or molten materials. The controller can obtain the models corresponding to the relevant injection molding machine, mold, and/or molten material. In addition to the sensor data obtained from the one or more sensors, the controller can analyze the model of the injection molding machine when automatically determining the tuning adjustments to one or more control parameters. 
     Analyzing the models of the injection molding machine, mold, and/or molten material to determine a composite score and/or to adjust the control parameters further reduces the error between the setpoint pattern and the exhibited response by tailoring control to the specific operating equipment. Consequently, the consistency of the molded product is increased, thereby enabling the production of molded products that can achieve tighter tolerances than achievable using conventional techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       While the specification concludes with claims particularly pointing out and distinctly claiming the subject matter that is regarded as the present invention, it is believed that the invention will be more fully understood from the following description taken in conjunction with the accompanying drawings. Some of the figures may have been simplified by the omission of selected elements for the purpose of more clearly showing other elements. Such omissions of elements in some figures are not necessarily indicative of the presence or absence of particular elements in any of the exemplary embodiments, except as may be explicitly delineated in the corresponding written description. None of the drawings are necessarily to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. 
         FIG.  1 A  illustrates a schematic view of an example injection molding machine having a controller coupled thereto in accordance with various embodiments of the present disclosure; 
         FIG.  1 B  illustrates a schematic view of an example injection molding machine having a controller and a PID controller coupled thereto in accordance with various embodiments of the present disclosure; 
         FIG.  2 A  illustrates a comparison plot between setpoint pressure values and sensed pressure values for an injection cycle executed by the injection molding machine constructed according to the disclosure; 
         FIG.  2 B  illustrates particular aspects of the comparison plot of  FIG.  2 A ; 
         FIG.  3    illustrates a comparison plot of a operation parameter metric and an injection cycle index of a run of injection cycles; and 
         FIG.  4    illustrates an exemplary method for autotuning control parameters of an injection molding process. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to the figures in detail,  FIG.  1 A  illustrates an exemplary injection molding machine  10  for producing thermoplastic parts in high volumes (e.g., a class  101  or  30  injection mold, or an “ultra-high productivity mold”), especially, but not necessarily, thinwalled parts having an L/T ratio of 100 or greater. The injection molding machine  10  generally includes an injection system  12  and a clamping system  14 . A thermoplastic material may be introduced to the injection system  12  in the form of thermoplastic pellets  16 . The thermoplastic pellets  16  may be placed into a hopper  18 , which feeds the thermoplastic pellets  16  into a heated barrel  20  of the injection system  12 . The thermoplastic pellets  16 , after being fed into the heated barrel  20 , may be driven to the end of the heated barrel  20  by a ram, such as a reciprocating screw  22 . The heating of the heated barrel  20  and the compression of the thermoplastic pellets  16  by the reciprocating screw  22  causes the thermoplastic pellets  16  to melt, forming a molten thermoplastic material  24 . The molten thermoplastic material is typically processed at a temperature of about 130° C. to about 410° C. 
     The reciprocating screw  22  forces the molten thermoplastic material  24  toward a nozzle  26  to form a shot of thermoplastic material, which will be injected into a mold cavity  32  of a mold  28  via one or more gates. The molten thermoplastic material  24  may be injected through a gate  30 , which directs the flow of the molten thermoplastic material  24  to the mold cavity  32 . In other embodiments the nozzle  26  may be separated from one or more gates  30  by a feed system (not shown). The mold cavity  32  is formed between first and second mold sides  25 ,  27  of the mold  28  and the first and second mold sides  25 ,  27  are held together under pressure by a press or clamping unit  34 . The press or clamping unit  34  applies a clamping force during the molding process that is greater than the force exerted by the injection pressure acting to separate the two mold halves  25 ,  27 , thereby holding the first and second mold sides  25 ,  27  together while the molten thermoplastic material  24  is injected into the mold cavity  32 . In a typical high variable pressure injection molding machine, the press typically exerts 30,000 psi or more because the clamping force is directly related to injection pressure. To support these clamping forces, the clamping system  14  may include a mold frame and a mold base. 
     Once the shot of molten thermoplastic material  24  is injected into the mold cavity  32 , the reciprocating screw  22  stops traveling forward. The molten thermoplastic material  24  takes the form of the mold cavity  32  and the molten thermoplastic material  24  cools inside the mold  28  until the thermoplastic material  24  solidifies. Once the thermoplastic material  24  has solidified, the press  34  releases the first and second mold sides  25 ,  27 , the first and second mold sides  25 ,  27  are separated from one another, and the finished part may be ejected from the mold  28 . The mold  28  may include a plurality of mold cavities  32  to increase overall production rates. The shapes of the cavities of the plurality of mold cavities may be identical, similar or different from each other. (The latter may be considered a family of mold cavities). 
     A controller  50  is communicatively connected to the injection molding machine  10  and is configured to execute a set of computer-readable instructions stored in a non-transitory memory to cause the injection molding machine  10  to execute injection cycles (i.e., the above-described injection molding process). To execute an injection cycle, the controller  50  may implement an injection pattern that includes one or more setpoint values for the control parameters that form an injection pattern. In some embodiments, the injection pattern defines a substantially constant pressure profile. Of course, the injection pattern may define other pressure profiles (e.g., a conventional, high pressure injection molding process). 
     The controller  50  is also communicatively coupled to one or more sensors  52 , such as the illustrated nozzle sensor, to measure operation of the injection molding machine  10 . Although  FIG.  1 A  only depicts a nozzle sensor and a screw position sensor, it should be appreciated that the controller  50  may monitor the data generated by any number of sensors  52 . In various embodiments, the sensors  52  may include any number of temperature sensors, pressure sensors, velocity sensors, and/or position sensors configured to monitor operation of the injection molding machine  10 . Additionally, the sensors  52  may include sensors that monitor the environment surrounding the injection molding machine  10 . For example, the sensors  52  may include a humidity sensor, a temperature sensor, an altitude sensor, a barometer, and/or a seismometer. 
     According to disclosed embodiments, the controller  50  is also operatively connected to a model database  66  that stores models indicative of characteristics of the injection molding machine  10 , the mold  28 , and/or the molten thermoplastic material  24  (or, in some embodiments, the thermoplastic pellets  16  in the hopper  18 ). For example, the model of the injection molding machine  10  may indicate a resistivity of one or more components of the injection molding machine  10 , a number of injection cycles executed using the injection molding machine  10 , a known error for one or more process variables introduced by the injection molding machine  10 , a purge pot pressure of the injection molding machine  10 , and/or a dead head pressure of the injection molding machine  10 . As another example, the model of the mold  28  may indicate a resistivity of the mold walls of the mold  28 , a number of injection cycles executed using the mold  28 , and/or a material from which the mold  28  is made. As still another example, the model of the molten thermoplastic material  24  may indicate a MFI and/or factor indicative of how MFI changes based on the amount of regrind introduced into the hopper  18 . Although  FIG.  1 A  depicts the model database  66  as a single entity, in some embodiments, the model database  66  may be divided into or made redundant using any number of database entities. The data populating the model database  66  may be stored on a non-transitory computer readable data storage medium, such as a read/write data storage medium that is associated with one or more components of the injection molding machine  10 , the mold  28 , and/or a storage container or bag containing thermoplastic pellets  16  of the thermoplastic material  24 . 
     Prior to executing a run of injection cycles, the controller  50  may obtain and analyze the model for the injection molding machine  10 , the mold  28 , and/or the molten thermoplastic material  24  to set an initial value for one or more control parameters of the injection molding machine. For example, the control parameters may be associated with component setpoint patterns that define a series of setpoint values for a particular control parameter over the course an injection cycle (such as melt pressure, injection velocity, hold pressure exerted by the clamping unit  34 , and/or position of the screw  22 ). The control parameters may also include parameters that are substantially constant throughout the injection cycle (such as temperature of the heated barrel  20 ). Additionally or alternatively, the controller  50  may analyze any environmental sensors  52  to set the initial values for the one or more control parameters. 
     In some embodiments, the controller  50  determines the initial values by inputting the model data and/or the sensor data into a machine learning model. In these embodiments, the machine learning model may be trained on historical data of prior injection cycles executed using the same or other injection molding machines, molds, and/or material. Based on the trained relationships between the model data and/or the sensor data, the machine learning model may generate a set of initial values that minimizes the error between the expected operation of the injection molding machine  10  and the injection pattern indicated by the injection cycle and/or produces more consistent molded parts. 
     In the embodiment illustrated in  FIG.  1 B , the controller  50  is also operatively connected to a proportional-integral-derivative (PID) controller  60 . In these embodiments, the PID controller  60  is configured to control a particular control parameter of the injection molding process. In operation, the PID controller  60  compares one or more setpoint values  58  (such as the target setpoint values included in an injection pattern) for the control parameter to the measured value of the control parameter via an adder or comparator  55 . For example, the PID controller  60  may be configured to control injection pressure or an injection velocity. Accordingly, one of the sensors  52  may be configured to monitor the injection pressure or injection velocity. In some embodiments, the sensor data is communicated directly to the comparator  55 . In other embodiments, the sensor data is communicated to the controller  50  and/or the PID controller  60  which routes the sensor data to the comparator  55 . 
     After the controller  50  determines the initial values of the control parameters for the injection molding process, the controller  50  executes a run of injection cycles (i.e., a series of sequentially executed injection cycles using the injection molding machine  10 ). As described herein, over the course of the run, operation of the injection molding machine  10  shifts. For example, the viscosity of the molten material  24  may shift, the temperature of the environment may shift, or trace amounts of the molten material  24  may be deposited on the mold  28 . As a result, the initial values may no longer be optimal for operating the injection molding machine  10  via the initial injection pattern. Accordingly, after each injection cycle of the run, the controller  50  may be configured to analyze the operational parameters of the prior injection cycle to automatically determine whether or not the control parameters for the injection molding process should be adjusted (e.g., “auto-tuned”). 
     With reference to  FIG.  2 A , illustrated is a comparison plot between setpoint melt pressure values  102  (e.g., melt pressure control parameters) and the sensed melt pressure values  104  (e.g., measured operational parameters) for an injection cycle executed by the injection molding machine  10 . It should be appreciated that while the illustrated plot is based on a substantially constant pressure profile, the disclosed techniques may be applied to any suitable pressure profile. In embodiments that implement the substantially constant pressure profile, the sensed melt pressure values  104  may be generated by a nozzle sensor of the sensors  52  and communicated to the controller  50  during the execution of the injection cycle. During an initial phase of the injection cycle, pressure rapidly increases to a setpoint value (setpoint P fill ). In the fill phase, the pressure is held at the steady-state pressure value as the mold cavity  32  is filled. When molten material  24  nears the end of the mold cavity  32 , pressure is reduced to second, lower, setpoint value (setpoint P Hold ). In the pack and hold phase, the pressure is held at the steady-state pressure value as the molten material  24  in the mold cavity  32  cools. After the material  24  is cooled, the mold  28  is opened in the molded part is ejected from the mold cavity  32 . 
     However, as illustrated, the sensed melt pressure values  104  do not match the setpoint pressure values  102 . Accordingly, in some embodiments, the controller  50  is configured to analyze these differences to determine the need to adjust the control parameters. For example, the controller  50  may determine a metric indicative of the difference between the setpoint P fill  and the measured P Fill  or the difference between the setpoint P Hold  and the measured P Hold . As another example, the controller  50  may determine a metric indicative of the total amount of error  103  between the setpoint pressure values  102  and the sensed pressure values  104 . 
     According to aspects of this disclosure, when the injection molding machine  10  exhibits a step response (such as the one indicated by the setpoint values  102 ), the sensed pressure values  104  do not immediately reach the steady-state value  102 . Instead, as illustrated in  FIG.  2 B , the response overshoots the steady-state value  102  and exhibits decreasing oscillatory error until achieving the steady-state value. Accordingly, while  FIG.  2 A  illustrates the sensed pressure curve  104  without the overshoot, the sensed pressure curve may actually exhibit the oscillatory error indicated by the pressure curve  105  as shown in  FIG.  2 B . The difference between the overshoot pressure associated with the step response of the pressure curve  105  and the setpoint  102  is referred to as the “P Overshoot .” Similarly, when the controller  50  compensates for the overshoot pressure, the pressure curve  105  exceeds the setpoint value  102  again. The difference between the amount the pressure curve  105  exceeds the setpoint  102  is referred to as the “P Undershoot .” Accordingly, in some embodiments, the controller may be configured to determine a metric based on the P Overshoot  or P Undershoot  values to determine the need to adjust the control parameters. 
     It should be appreciated that  FIGS.  2 A and  2 B  only illustrate some example operational parameters that may be analyzed by the controller  50  to determine the need to adjust the control parameters. In various embodiments, the controller  50  may analyze other operational parameters (such as injection velocity, screw position, clamping pressure, etc.) to determine the need to adjust the control parameters. 
     Regardless of the particular operational parameter, the controller  50  may compare the value for the operational parameter to a threshold to determine the need to adjust the control parameters. Referring to  FIG.  3   , illustrated is a comparison plot of an operational parameter metric and an injection cycle index of a run of injection cycles. The operational parameter metric values  114  (illustrated as “X”s) represent the value of the operational parameter during each injection cycle of the run. The controller  50  has defined an expected range of operation that includes an upper bound threshold  112   a  and a lower bound threshold  112   b . Accordingly, the controller  50  may detect when the value of the metric exceeds the threshold  112   a  (as illustrated by the value  114   b ). In response, the controller  50  autotunes the control parameters. As a result, as illustrated, the next value  114  is within the thresholds  112   a  and  112   b.    
     It should be appreciated that term “exceeds a threshold” does not necessarily refer to the operational parameter exceeding an upper bound of expected operation, such as the threshold  112   a . In other scenarios, the controller  50  may determine the need to adjust the control parameters based on the metric exceeding the lower bound threshold  112   b.    
       FIG.  4    illustrates an exemplary method  200  for autotuning control parameters of an injection molding process. The method  200  may be performed by a controller  50  operatively connected to the injection molding machine  10  of  FIG.  1 A or  1 B . 
     The example method  200  begins by the controller  50  analyzing a model of at least one of the injection molding  10 , the mold  28 , and a molten material  24  to determine initial values for one or more control parameters of the injection molding machine  10  (block  202 ). As described above, the controller  50  may obtain the models from the model database  66 . In addition to any data included in the models, the controller  50  may analyze data generated by the sensors  52 , including sensors configured to sense environmental conditions associated with the injection molding machine  10 . In some embodiments, the controller  50  utilizes the model data (and any sensor data) as an input into a machine learning algorithm that generates the initial values for the one or more control parameters. 
     At block  204 , the controller  50  executes a run of injection cycles at the injection molding machine  10 . During each injection cycle of the run, the injection molding machine  10  injects the molten material  24  into a cavity  32  of the mold  28  according to an injection pattern. The injection pattern may define one or more setpoint patterns for one or more control parameters. For example, the injection pattern may define a setpoint pattern for melt pressure, screw position, screw velocity, hold or clamp pressure, and so on. 
     At block  206 , the controller  50  measures operation of the injection molding machine  10  during a particular injection cycle of the run of injection cycles. In some embodiments, the controller  50  measures operation of the injection molding machine  50  after the controller  50  finishes controlling the injection molding machine  10  to execute the particular injection cycle. To measure the operation of the injection molding machine  10 , the controller  50  may obtain data sensed by the sensors  52  configured to monitor various conditions of the injection molding process. 
     At block  208 , the controller  50  determines that one or more operational parameters exceeds a threshold. The operational parameters may include a steady-state error, an overshoot pressure, an undershoot pressure, an environmental parameter, and so on. Accordingly, the controller  50  may compare a value for a particular operational parameter to the threshold. In some embodiments, the threshold may be indicative of a viscosity of the molten material  24  and/or a molded part weight (which can be used as an indication of part-to-part consistency) being outside of an expected range of operation. 
     Additionally or alternatively, the controller  50  may combine two or more of the operational parameters to generate a composite metric. In some embodiments, the controller  50  assigns the individual operational parameters a weight or weighting function to combine the operational parameters into the composite metric. For example, the weights or weighting functions may be indicative of the amount the particular operational parameter impacts the viscosity of the molten material  24  and/or the molded part weight. Accordingly, in these embodiments, the controller  50  compares the composite metric to the threshold. 
     In some embodiments, the controller  50  applies a machine learning algorithm to determine the composite metric. More particularly, the controller  50  may apply machine learning techniques to determine the weights and/or weighting functions for the operational parameters combined into the composite metric. In some embodiments, the machine learning model that determines the weights used to develop the composite metric may be a different machine learning model than the model used to determine the initial control values. In these embodiments, while both machine learning models may be trained based on data collected during prior injection cycles executed using the same or different injection molding machines, molds, and/or molten materials, the machine learning model that determines the weights associated with the operational parameters may be configured to determine a need to autotune the control parameters, but not necessarily the particular values to which the control parameters are tuned. In other embodiments, the same machine learning model determines both the weights or weighting function to combine the operational parameters to generate the composite metric, as well as the values to which the control parameters are tuned. 
     At block  210 , upon determining that the one or more operational parameters exceeds the threshold, the controller  50  adjusts the control parameters for subsequent injection cycles of the run of injection cycles. In some embodiments, the controller  50  adjusts one or more setpoint patterns for the control parameters that form the injection pattern. In embodiments that include the PID controller  60  being operatively connected to the injection molding machine  10  as illustrated in  FIG.  1 B , the controller  50  may adjust one or more of the gains of the PID controller  60 . To this end, the PID controller  60  may include one or more interfaces to receive commands to configure the proportional, integral, and/or derivative gains. The interfaces may include application-layer interfaces, such as an application programming interface (API), and a communication interface, such as a wired or wireless communication link. The controller  50  may generate a command to adjust the proportional, integral, and/or derivative gains in a format defined by the API of the PID controller  60  and transmit the command to the PID controller  60  via the wired or wireless communication link. 
     In some embodiments, the controller  50  applies a machine learning algorithm to determine an adjustment to the control parameters. For example, the controller  50  may utilize the machine learning algorithm used to generate the initial values for the control parameters to determine the adjustment. As described above, the environment and/or the operation of the injection molding machine  10  changes throughout the course of a run. Accordingly, when the controller  50  utilizes the updated set of operational data as an input, the machine learning algorithm may produce a different set of control parameter values. The controller  50  may analyze this output set of control parameters values to determine the adjustment to the one or more control parameters. As a result, when the controller  50  controls the injection molding machine  10  to execute subsequent injection cycles, the consistency in molded parts is improved. 
     It should be appreciated that a run may include a sufficient number of injection cycles that the operational parameters may continue to shift, thereby causing the operation of the injection molding machine  10  to be outside of the expected range of operation. Accordingly, the controller  50  may be configured to execute the actions associated with blocks  206 - 210  after each subsequent injection cycle of the run. 
     It should be understood that the term “control parameter” generally refers to an input into the injection molding process controlled by a controller and the term “operational parameter” generally refers to measured characteristic of the injection molding process during operation. In some embodiments, the same characteristic of the injection molding process may be both a control parameter and an operational parameter. For example, a melt pressure may be associated with a control parameter (e.g., a setpoint value or injection pattern) and an operational parameter (e.g., a sensed pressure value via a physical or virtual sensor). 
     Those skilled in the art will recognize that a wide variety of modifications, alterations, and combinations can be made with respect to the above described embodiments without departing from the scope of the invention, and that such modifications, alterations, and combinations are to be viewed as being within the ambit of the inventive concept. 
     The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s). The systems and methods described herein are directed to an improvement to computer functionality, and improve the functioning of conventional computers.