Method of operating a molding system, molding system and controller for controlling the operation of a

Abstract

An injection molding system and a method for controlling an injection molding system are disclosed. The injection molding system has a plurality of operating components and one or more controllers. The controller is configured to receive an indication of an operating parameter to be optimized, the operating parameter being selectable from a plurality of operating parameters of the plurality of operating components, and to select a target optimization model from a plurality of optimization models based on the operating parameter to be optimized. The controller is also configured to iteratively optimize the target operational parameters by using the target optimization model until a predetermined result is achieved.

Description

Technical Field

[0001] This technology relates to molding systems and methods for operating molding systems. More specifically, this technology relates to a molding system having a controller for operating the molding system while optimizing its performance. Background Technology

[0002] Molding is a process by which molded articles are formed from molding materials using a molding system. A wide variety of molded articles can be formed using molding processes such as injection molding. An example of a molded article that can be formed from materials such as polyethylene terephthalate (PET) is a preform that can then be blow-molded into beverage containers, such as bottles. However, a variety of other molded articles can be produced, such as thin-walled containers, medical devices (such as blood vials and syringes), automotive parts, and more.

[0003] Broadly speaking, the cost of producing molded products consists of the capital cost of the molding system itself, resin costs, and other administrative expenses (such as electricity, water supply, and labor costs). To maximize profits from the system, it should be operated at full capacity as much as possible. Simultaneously, it is important to ensure the quality of the output (i.e., the molded product) and to ensure that the output meets the specifications and other production preferences of the customer (i.e., the molding system operator).

[0004] WO 20212 / 37339 discloses a molding system having a supervisory controller operable to track the current operating conditions of multiple processing stations, define paths from processing stations preparing to release output units and processing stations preparing to receive input units, and provide instructions to a transport system to move input and output units along the paths. Summary of the Invention

[0005] The purpose of this invention is to improve upon at least some of the inconveniences present in the prior art.

[0006] Broadly speaking, embodiments of this technology aim to provide a method for optimizing the performance of a molding system. Developers of this technology have understood the need to develop a molding system and its associated controller to allow the molding system to optimize itself for different customer needs. For example, it may be necessary to optimize the molding system for an alternative optimization objective selectable by the operator, rather than optimizing for maximum output.

[0007] Broadly speaking, a molding system controller can be configured to sense any combination of one or more operating parameters and dynamically adjust one or more (same or other) operating parameters to optimize the operation of the molding system. A given molding system is a complex system with multiple interdependent processes and may require optimization of multiple operating parameters of the molding system to achieve optimal performance. Therefore, embodiments of this technology are configured to sense any operating parameters of the molding system (and its upstream or downstream systems) and dynamically adjust one or more operating parameters of the molding system (and other upstream or downstream systems).

[0008] Without being bound by any particular theory, one area where dynamic sensing and adaptation could be beneficial is the use of recycled molding materials in molding systems. Broadly speaking, recycled molding materials can be prepared through mechanical or chemical processes. Mechanically recycled PET can be considered essentially a new polymer with different and unique properties compared to virgin PET, posing unique challenges to processing. Due to the different properties of the raw materials, the steady-state setting of the system becomes more dynamic, where the structure and / or process are adjusted synchronously based on the detected characteristics of the RPET. In other words, mechanically recycled polymer raw materials can be dynamically treated to address changes, even cumulative changes, in polymer properties until it is ultimately unsuitable for its intended purpose, and then sent for chemical recycling, thus making it new again.

[0009] The controller may be embodied as one or more processors in a computer system, configured to optimize the molding system, particularly based on the needs of different clients. In some embodiments, the functions of the controller may be performed via a single hardware processor. In other embodiments, the functions of the controller may be performed via multiple hardware processors. Furthermore, without departing from the scope of this technology, one or more functions of the controller disclosed herein may be performed by a distributed processing system.

[0010] In some embodiments, the controller may be configured to continuously monitor the operating parameters of the molding system using data collected by one or more sensors. Additionally, the controller may be configured to process the captured data using one or more computer-implemented models, and, in particular, adjust one or more parameters of the molding process based on user input. For example, the controller may receive user input, collect real-time data, analyze this data, and trigger one or more actions or commands to dynamically optimize one or more operating parameters, such as temperature, pressure, and cycle time, according to optimization goals set via user input.

[0011] In some non-limiting embodiments, an operator of a given molding system may wish to optimize the molding system for an optimization objective other than maximum output. As a broad example, the optimization objective can be a given requirement for a given molding system that is important to a given operator of that molding system. In this broad example, embodiments of the present technology provide a controller configured to optimize the operating parameters of one or more components of a given molding system in accordance with a requirement.

[0012] In some non-limiting embodiments, the controller may be configured to use a "feedback mechanism" to optimize the operating parameters of one or more components toward an optimization goal for the given molding system during iterative adjustments to the operation of the system.

[0013] In some non-limiting embodiments, an operator of a given molding system may wish to optimize the molding system for the objective of minimizing energy costs, i.e., optimizing for the minimum energy cost per molded article.

[0014] In some non-limiting embodiments, an operator of a given molding system may wish to optimize the molding system for the purpose of minimizing energy consumption, i.e., optimizing for the minimum energy consumed per molded article.

[0015] In some non-limiting embodiments, an operator of a given molding system may want to optimize the molding system for a productivity optimization objective, i.e., reduce the production rate to meet production requirements.

[0016] In some non-limiting embodiments, an operator of a given molding system may want to optimize the molding system for the purpose of optimizing machine reliability, i.e., saving machine life by optimizing production rate.

[0017] In some non-limiting embodiments, an operator of a given molding system may wish to optimize the molding system for the purpose of optimizing peak load, i.e., minimizing peak electrical load.

[0018] In some non-limiting embodiments, an operator of a given molding system may want to optimize the molding system for time-related optimization objectives, i.e., scheduling constraints or production plans.

[0019] In some non-limiting embodiments, the operator of a given molding system may wish to optimize the molding system for human optimization goals, i.e., when skilled team members are accessible. In alternative non-limiting embodiments, it may be desirable to automatically optimize the performance of a given molding system during the startup process.

[0020] In a first broad aspect of this technology, a method for operating an injection molding system is provided. The injection molding system has multiple operating components and a controller. The controller is configured to control the multiple operating components. The method is executable by the controller. The method includes receiving an indication of operating parameters to be optimized, the operating parameters being selectable from multiple operating parameters of the multiple operating components. The method includes selecting a target optimization model from multiple optimization models based on the operating parameters to be optimized. The method includes iteratively optimizing the target operating parameters using the target optimization model until a predetermined result is achieved.

[0021] In some embodiments of this technology, the target optimization model further includes an indication of a predetermined result.

[0022] In some embodiments of this technology, multiple optimization models are physical models of the operation of the injection molding system.

[0023] In some embodiments of this technology, multiple optimization models are organized in a hierarchical structure such that, based on economic criteria, the first optimization model in the hierarchical structure has a higher rank than the second optimization model.

[0024] In some embodiments of this technology, the target optimization model is a target subgroup of optimization models among multiple optimization models associated with a subgroup of operational parameters among multiple operational parameters. Using the target subgroup of optimization models involves iteratively and simultaneously optimizing a subgroup of target operational parameters until a predetermined result is achieved.

[0025] In some embodiments of this technology, multiple operating components include a blow molding machine and a molding machine. The blow molding machine is located downstream of the molding machine. Instructions for operating parameters are received from the blow molding machine.

[0026] In some embodiments of this technology, multiple operating components include a dryer and a molding machine. The dryer is located upstream of the molding machine. Instructions for operating parameters are received from the dryer.

[0027] In a second, broader aspect of this technology, a method for operating an injection molding system is provided. The injection molding system has a plurality of operating components and a controller. The controller is configured to control the plurality of operating components. The method is executable by the controller. The method includes receiving an indication of an operating objective associated with the injection molding system. The method includes accessing a database storing a plurality of models, each of the plurality of models being associated with at least one of the plurality of operating components. The method includes selecting a target model from the plurality of models based on the operating objective, the target model being used to optimize target operating parameters of at least one target operating component. The method includes iteratively optimizing the target operating parameters using the target model; wherein iterative optimization includes checking the target operating parameters of at least one target operating component against predetermined results. The method includes controlling the injection molding system using the target operating parameters in response to satisfying predetermined results.

[0028] In some embodiments of this technology, a given model among a plurality of models is used to indicate the operating parameters of a respective operating component associated with a given one of a plurality of operating envelopes.

[0029] In some embodiments of this technology, a given model is used to indicate the interdependence between the operating parameters of the first operating component and the operating parameters of the second operating component.

[0030] In some embodiments of this technology, a given model is used to enable the controller to evaluate the impact of the operating parameters of the first operating component and the operating parameters of the second operating component during optimization.

[0031] In some embodiments of this technology, interdependence is based on mathematical models.

[0032] In some embodiments of this technology, a given model among multiple models further indicates the sensitivity of the operating parameters to changes in order to achieve the operational objective.

[0033] In some embodiments of this technology, the method further includes, in response to the optimization objective failing to meet a predetermined result during iterative optimization of the target optimization parameters, selecting from a plurality of models another target model for optimizing other target operating parameters of at least one other target operating component.

[0034] In some embodiments of this technology, the method further includes receiving operator input for a predetermined result before using target operating parameters to control the injection molding system.

[0035] In some embodiments of this technology, at least one other target operating component of another target model is different from at least one target operating component of the target model.

[0036] In some embodiments of this technology, the operating parameters include at least one of pump speed, battery charging, filling rate, holding time, cooling time, transfer speed, injection start, mold clamping tonnage, and tonnage locking.

[0037] In some embodiments of this technology, each of the multiple models includes indications of operating parameters for an upward direction and for a downward direction for optimization.

[0038] In some embodiments of this technology, the operational objective is to achieve the lowest possible energy cost per molded article.

[0039] In some embodiments of this technology, the operational objective is to minimize the energy consumed per molded article.

[0040] In some embodiments of this technology, the operational objective is the minimum production rate required for a predetermined production target.

[0041] In a third, broad aspect of this technology, an injection molding system is provided, having a plurality of operating components and one or more controllers. The one or more controllers are configured to receive indications of operating parameters to be optimized, the operating parameters being selectable from a plurality of operating parameters of the plurality of operating components. The one or more controllers are configured to select a target optimization model from a plurality of optimization models based on the operating parameters to be optimized. The one or more controllers are configured to iteratively optimize the target operating parameters using the target optimization model until a predetermined result is achieved.

[0042] In some embodiments of the injection molding system, the target optimization model further includes an indication of a predetermined result.

[0043] In some embodiments of the injection molding system, multiple optimization models are physical models of the operation of the injection molding system.

[0044] In some embodiments of the injection molding system, multiple optimization models are organized in a hierarchical structure such that, based on economic criteria, the first optimization model in the hierarchical structure has a higher rank than the second optimization model.

[0045] In some embodiments of the injection molding system, the target optimization model is a target subgroup of optimization models among multiple optimization models associated with a subgroup of operating parameters among multiple operating parameters. By using the target subgroup of optimization models, the injection molding system iteratively and simultaneously optimizes the subgroup of target operating parameters until a predetermined result is achieved.

[0046] In some embodiments of the injection molding system, multiple operating components include a blow molding machine and a molding machine. The blow molding machine is located downstream of the molding machine. Instructions for operating parameters are received from the blow molding machine.

[0047] In some embodiments of the injection molding system, multiple operating components include a dryer and a molding machine. The dryer is located upstream of the molding machine. Instructions for operating parameters are received from the dryer.

[0048] In a fourth broad aspect of this technology, an injection molding system is provided, having a plurality of operating parts and one or more controllers. The one or more controllers are configured to receive instructions on an operating objective associated with the injection molding system. The one or more controllers are configured to access a database storing a plurality of models, each of which is associated with at least one of the plurality of operating parts. The one or more controllers are configured to select a target model from the plurality of models based on the operating objective, the target model being used to optimize target operating parameters of at least one target operating part. The one or more controllers are configured to iteratively optimize the target operating parameters using the target model; wherein the iterative optimization includes checking the target operating parameters of at least one target operating part against predetermined results. The one or more controllers are configured to control the injection molding system using the target operating parameters in response to satisfying predetermined results.

[0049] In some embodiments of the injection molding system, a given model among a plurality of models is used to indicate the operating parameters of a respective operating component associated with a given one of a plurality of operating envelopes.

[0050] In some embodiments of the injection molding system, a given model is used to indicate the interdependence between the operating parameters of a first operating component and the operating parameters of a second operating component.

[0051] In some embodiments of the injection molding system, a given model is used to enable the controller to evaluate the impact of the operating parameters of the first operating component and the operating parameters of the second operating component during optimization.

[0052] In some embodiments of the injection molding system, interdependence is based on mathematical models.

[0053] In some embodiments of the injection molding system, a given model among multiple models further indicates the sensitivity of the operating parameters to changes in order to achieve the operational objectives.

[0054] In some embodiments of the injection molding system, one or more controllers are further configured to select from a plurality of models, in response to the optimization objective failing to meet a predetermined result during iterative optimization of the target optimization parameters, an alternative target model for optimizing alternative target operating parameters of at least one other target operating component.

[0055] In some embodiments of the injection molding system, one or more controllers are further configured to receive operator input for a predetermined result before using target operating parameters to control the injection molding system.

[0056] In some embodiments of the injection molding system, at least one other target operating component of the other target model is different from at least one target operating component of the target model.

[0057] In some embodiments of the injection molding system, the operating parameters include at least one of pump speed, battery charging, filling rate, holding time, cooling time, transfer speed, injection start, clamping tonnage, and tonnage locking.

[0058] In some embodiments of the injection molding system, each of the multiple models includes indications of operating parameters for optimization in the upward direction and for optimization in the downward direction.

[0059] In some embodiments of injection molding systems, the operational objective is to minimize the energy cost per molded article.

[0060] In some embodiments of injection molding systems, the operational objective is to minimize the energy consumed per molded article.

[0061] In some embodiments of injection molding systems, the operational objective is the minimum production rate required to meet predetermined production needs.

[0062] In the context of this specification, the words “first,” “second,” “third,” etc., are used as adjectives merely to allow for distinction between the nouns they modify, and not to describe any specific relationship between these nouns. Furthermore, as discussed in other contexts herein, references to the “first” and “second” elements do not preclude the two elements from being the same actual real-world element.

[0063] These and other aspects and features of the non-limiting embodiments of the present technology will become apparent to those skilled in the art after reading the following description of specific non-limiting embodiments in conjunction with the accompanying drawings.

[0064] The embodiments of this technology each have at least one of the above-described objectives and / or aspects, but not necessarily all of them. It should be understood that some aspects of this technology arising from attempts to achieve the above objectives may not satisfy those objectives and / or may satisfy other objectives not specifically described herein.

[0065] Additional and / or alternative features, aspects, and advantages of embodiments of the present technology will become apparent from the following description, the accompanying drawings, and the appended claims. Attached Figure Description

[0066] A better understanding of embodiments of the present technology (including alternatives and / or variations thereof) can be obtained by referring to the detailed description of non-limiting embodiments in conjunction with the following drawings, wherein:

[0067] Figure 1 A schematic diagram of an injection system including a controller implemented according to a non-limiting embodiment of the present technology is depicted;

[0068] Figure 2 It includes Figure 1 A schematic diagram of the components of the control system of the molding system controller;

[0069] Figure 3 It is shown Figure 2 A block diagram of the components of the supervisory control layer of the control system;

[0070] Figure 4 It is by Figure 3 A schematic representation of a table showing the list of tools for the supervisory control layer and the tools for maintaining the action database;

[0071] Figure 5 Depicting control Figure 1 A schematic diagram of the flowchart of the injection molding system method; and

[0072] Figure 6 Depicting in Figure 3 A schematic representation of the envelope information maintained in the envelope database of the supervisory control layer. Detailed Implementation

[0073] Reference will now be made in detail to various non-limiting embodiments of the molding system and related methods of operation. It should be understood that, in view of the non-limiting embodiments disclosed herein, other non-limiting embodiments, modifications, and equivalents will be apparent to those skilled in the art, and these variations should be considered within the scope of the appended claims.

[0074] Furthermore, those skilled in the art will recognize that certain structural and operational details of the non-limiting embodiments discussed below may be modified or omitted entirely (i.e., not essential). In other instances, well-known methods, processes, and components are not described in detail.

[0075] Injection molding system

[0076] refer to Figure 1 This paper depicts a non-limiting embodiment of an injection molding machine 100 that may be suitable for implementing the present technology. For illustrative purposes only, it is assumed that the injection molding machine 100, as part of the molding system 200, is configured to manufacture preforms (not depicted) for subsequent blow molding into containers of the final shape. It should be noted that the molding machine 100 can be implemented as any suitable type of molding machine, such as, but not limited to, hydraulic presses, all-electric motors, and hybrid motors thereof.

[0077] It should be understood that, in alternative, non-limiting embodiments, molding system 200 may include other types of molding systems, such as, but not limited to, compression molding systems, compression injection molding systems, transfer molding systems, etc. Furthermore, molding system 200 may include additional machinery, such as a blow molding machine (not depicted) located downstream of molding machine 100 and / or a dryer located upstream of molding machine 100. Alternatively, the blow molding machine may be located separately from molding system 200, such as in a different facility or controlled by a different entity.

[0078] exist Figure 1 In a non-limiting embodiment, the molding machine 100 includes a fixed pressure plate 102 and a movable pressure plate 104. The fixed pressure plate 102 is also referred to as a stationary pressure plate 102. In some embodiments of the present technology, the molding machine 100 may include a third immovable pressure plate (not depicted). Alternatively or additionally, the molding machine 100 may include a turret block, a rotating cube, a turntable, etc. (all not depicted, but known to those skilled in the art).

[0079] The injection molding machine 100 further includes an injection unit 106 for plasticizing and injecting molding material. The injection unit 106 may be implemented as a single-stage injection unit or a two-stage injection unit. In some alternative, non-limiting embodiments of the art, the injection unit 106 may be implemented as one or more injection units (not depicted). However, it should be noted that the operating principle of the molding machine 100 can be implemented using any known operating principle.

[0080] In operation, the movable pressure plate 104 moves toward and away from the fixed pressure plate 102 by means of a stroke cylinder (not shown) or any other suitable device. For example, clamping force (also known as closing or mold closing tonnage) can be generated within the molding machine 100 using connecting rods 108, 110 (typically four connecting rods 108, 110 are present in the molding machine 100) and a connecting rod clamping mechanism 112, and an associated hydraulic system (not depicted) generally associated with the connecting rod clamping mechanism 112. It will be understood that alternative devices, such as column-based clamping mechanisms, toggle-type locking arrangements (not depicted), etc., can be used to generate clamping tonnage.

[0081] The molding machine 100 has a mold 113, which has a first mold half 114 and a second mold half 116. The first mold half 114 can be associated with a fixed pressure plate 102 and the second mold half 116 can be associated with a movable pressure plate 104. Figure 2In a non-limiting embodiment, the first mold half 114 defines a mold cavity 118. The mold cavities 118 are typically formed in an array, such that a given number of columns and a given number of rows exist. In some embodiments, there may be eight columns and twelve rows of mold cavities 118, forming a total of 96 cavities; however, it should be noted that embodiments of this technology are not limited to any particular number of cavities. As those skilled in the art will understand, the mold cavities 118 can be formed by using suitable mold inserts (such as cavity inserts, gate inserts, etc.) or any other suitable means. Therefore, the first mold half 114 can generally be considered a "mold cavity half".

[0082] The second mold half 116 includes a mold core 120 complementary to the mold cavity 118, such that the mold core 120 also generally forms an array similar to the array formed by the mold cavity 118, wherein the array formed by the mold core 120 has the same number of columns and rows as the array formed by the mold cavity 118. As those skilled in the art will understand, the mold core 120 can be formed by using suitable mold inserts or any other suitable means. Therefore, the second mold half 116 can generally be considered as a "mold core half". Although Figure 1 Not depicted, but the first mold half 114 may be further associated with a melt distribution network (commonly referred to as a hot runner) for distributing molding material from the injection unit 106 to each of the mold cavities 118. Additionally, the second mold half 116 is provided with a neck ring (not depicted) to produce a preform with a neck portion (not depicted) of the preform.

[0083] The first mold half 114 can be connected to the fixed pressure plate 102 by any suitable means, such as suitable fasteners (not depicted). The second mold half 116 can be connected to the movable pressure plate 104 by any suitable means, such as suitable fasteners (not depicted). It should be understood that in alternative, non-limiting embodiments of the present technology, the positions of the first mold half 114 and the second mold half 116 can be reversed, and therefore, the first mold half 114 can be associated with the movable pressure plate 104 and the second mold half 116 can be associated with the fixed pressure plate 102. In alternative, non-limiting embodiments of the present technology, the fixed pressure plate 102 does not need to be fixed and can be movable relative to other parts of the molding machine 100.

[0084] Figure 1A first mold half 114 and a second mold half 116 are depicted in the so-called "mold open position," wherein the movable pressure plate 104 is generally positioned away from the fixed pressure plate 102, and thus the first mold half 114 is generally positioned away from the second mold half 116. For example, in the mold open position, a molded article (not depicted) can be removed from the first mold half 114 and / or the second mold half 116. In the so-called "mold closed position" (not depicted), the first mold half 114 and the second mold half 116 are pushed together (by means of movement of the movable pressure plate 104 toward the fixed pressure plate 102) and cooperate to define (at least partially) a molding cavity into which molten plastic (or other suitable molding material) can be injected, as is known to those skilled in the art. Due to the arrangement of the mold cavity 118 and the mold core 120, the molding cavity generally forms a mold array, also referred to as a molding cavity array. The molding cavity array has the same number of columns and rows as the array formed by the mold cavity 118 and the mold core 120.

[0085] The injection molding machine 100 may further include a robot 122 (sometimes referred to as a retrieval and / or removal device) operably coupled to a fixed platen 102. Those skilled in the art will readily understand how the robot 122 can be operably coupled to the fixed platen 102, and therefore will not be described in detail herein. The robot 122 includes a mounting structure 124, an actuated arm 126 coupled to the mounting structure 124, and a transfer plate 128 coupled to the actuated arm 126. The transfer plate 128 includes a plurality of molded article receivers 130.

[0086] Generally, the purpose of multiple molded article receivers 130 is to remove molded articles (such as preforms in this example) from one or more mold cores 120 (or one or more mold cavities 118) and / or to perform post-molding cooling of the molded articles. In the non-limiting example illustrated herein, the multiple molded article receivers 130 include multiple cooling tubes for receiving multiple molded preforms. However, it should be clearly understood that the multiple molded article receivers 130 may have other configurations. The exact number of multiple molded article receivers 130 is not particularly limited. In some cases, the number of molded article receivers 130 corresponds to the number of mold cavities 118.

[0087] like Figure 1 The robot 122 is schematically depicted as a side-entry type. However, it should be understood that in alternative, non-limiting embodiments of the present technology, the robot 122 may be a top-entry type or other types. It should also be clearly understood that the term "robot" is intended to cover structures that perform a single operation as well as structures that perform multiple operations. In at least some embodiments, it is also contemplated that the robot 122 may be omitted and / or replaced by devices for moving the molded article 50 in different implementations.

[0088] It should be noted that the robot 122 is configured to manipulate the molded article 50 while maintaining the molded cavity array (i.e., the robot 122 does not disturb the positioning of the molded article).

[0089] The molding machine 100 further includes a post-molding processing device 132 operatively coupled to a movable pressure plate 104. Those skilled in the art will readily understand how the post-molding processing device 132 can be operatively coupled to the movable pressure plate 104, and therefore will not be described in any detail herein. The post-molding processing device 132 includes a mounting structure 134 for coupling the post-molding processing device 132 to the movable pressure plate 104. The post-molding processing device 132 further includes an inflation chamber 129 coupled to the mounting structure 134. A plurality of processing pins 133 are coupled to the inflation chamber 129. The number of processing pins within the plurality of processing pins 133 generally corresponds to the number of receivers within the plurality of molded article receivers 130. In at least some embodiments, it is also contemplated that the post-molding processing device 132 may be omitted and / or replaced by devices for processing molded articles in different implementations. It should be noted that even in the depicted embodiment, the post-molding processing device 132 is coupled to the movable pressure plate 104, but in alternative, non-limiting embodiments of the art, the post-molding processing device 132 may be implemented differently, for example, it may not need to be coupled to the movable pressure plate 104 to move with it.

[0090] The molding machine 100 further includes a computing device 140, also referred to herein as a processor 140 or "controller," which is configured to control one or more operations of the molding machine 100. As will be described below, the processor 140 is further configured to control one or more operations of the molding system 200, of which the molding machine 100 is a part, and these operations will be described in more detail below. As those skilled in the art will understand, the computing device 140 may include a plurality of processors or computer-implemented devices operatively connected together.

[0091] Processor 140 includes a human-machine interface (not separately numbered), or HMI for short. The HMI of processor 140 can be implemented using any suitable interface. As an example, the HMI of processor 140 can be implemented as a multifunction touchscreen. Examples of HMIs that can be used to implement non-limiting embodiments of the present technology are disclosed in commonly owned U.S. Patent 6,684,264, the contents of which are incorporated herein by reference in their entirety.

[0092] Those skilled in the art will understand that processor 140 can be implemented using pre-programmed hardware or firmware elements (e.g., application-specific integrated circuits (ASICs), electrically erasable programmable read-only memory (EEPROM), etc.) or other related components. In other embodiments, the functionality of processor 140 can be implemented using a processor with access rights to a code memory (not shown) that stores computer-readable program code for the operation of a computing device. In this case, the computer-readable program code can be stored on a fixed, tangible medium that is directly readable by various network entities (e.g., removable disks, CD-ROMs, ROMs, hard disks, USB drives), or the computer-readable program code can be stored remotely but can be transmitted to processor 140 by a transmission medium via a modem or other interface device (e.g., a communication adapter) connected to a network (including but not limited to the Internet). This transmission medium can be a non-wireless medium (e.g., optical or analog communication lines) or a wireless medium (e.g., microwave, infrared, or other transmission schemes) or a combination thereof.

[0093] In alternative, non-limiting embodiments of this technology, the HMI need not be physically attached to the processor 140. In fact, the HMI of the processor 140 can be implemented as a separate device. In some embodiments, the HMI can be implemented as a wireless communication device (such as a smartphone) that is “paired” with or otherwise communicatively connected to the processor 140.

[0094] Processor 140 can perform several functions, including but not limited to receiving control commands from the operator, controlling the molding machine 100 based on operator control commands or preset control sequences stored within processor 140 or elsewhere within the molding machine 100, and acquiring one or more operating parameters associated with the molding system (such as one or more of the following: pump speed, battery charging, filling rate, holding time, cooling time, transfer speed, injection start, clamping tonnage, and tonnage locking). According to a non-limiting embodiment of the present technology, processor 140 is further configured to process the acquired one or more operating parameters associated with the molding system 200 and output the information to the operator using an HMI or similar means. It is conceivable that processor 140 can be implemented in a distributed manner on multiple physical machines without departing from the scope of the present technology.

[0095] The molding machine 100 further includes a plurality of monitoring devices (not depicted) configured to acquire various operating parameters associated with the performance of the molding machine 100. These monitoring devices are generally known in the art and therefore will not be described further herein.

[0096] By way of example only, the injection molding machine 100 may include a counter to count the opening and closing of the mold, thereby determining the number of cycles over a period of time and / or the cycle time of each cycle. The injection molding machine 100 may also include multiple pressure gauges to measure pressures (such as hydraulic fluid pressure or molding material pressure) within various components of the injection molding machine 100.

[0097] According to a non-limiting embodiment of the present technology, processor 140 is configured to acquire a plurality of operating parameters associated with molding machine 100. The nature of the plurality of operating parameters thus acquired can vary. How processor 140 acquires the plurality of operating parameters depends, of course, on the nature of the plurality of operating parameters acquired.

[0098] By monitoring the operation of the molding machine 100, the processor 140 can acquire operating parameters such as one or more of the following: pump speed, battery charging, filling rate, holding time, cooling time, transfer speed, injection start, clamping tonnage, and tonnage lock. As an example only, the processor 140 can acquire cycle time by monitoring the performance of the molding machine 100. Naturally, the processor 140 can acquire machine variables either by the operator inputting them using an HMI or by reading memory tags (not depicted) associated with the molds used in the molding machine 100 (i.e., the first mold half 114 and the second mold half 116 described above). Various embodiments of the memory tags (not depicted) are known in the art. Generally, the memory tags (not depicted) can store information about the mold, the molded article to be produced, predefined control sequences, setting sequences, etc.

[0099] In some non-limiting embodiments of this technology, the processor 140 may acquire the operating parameters by receiving instructions from an operator. However, in some embodiments of the molding machine 100, the processor 140 may acquire some (or even all) of the operating parameters by monitoring the performance of the molding machine 100. In some non-limiting embodiments of this technology, this may be carried out by inspecting the molded articles and assessing their conformity to specifications, and then accepting or rejecting articles based on that assessment. This may be done by suitable equipment (such as optical inspection equipment) or by an operator. Naturally, other ways in which the processor 140 acquires some or all of these or other operating parameters are also possible, some of which will be described below.

[0100] In alternative, non-limiting embodiments of this technology, the processor 140 may obtain operating parameters from other components of the molding system 200, such as, but not limited to, the aforementioned blow molding machine (not depicted) located downstream of the molding machine 100 and / or a dryer located upstream of the molding machine 100. Additionally or alternatively, the operating parameters may be associated with quality parameters of the blow-molded or filled container made from the molded article (this is particularly applicable to those embodiments where the molded article is a preform for subsequent blow molding into a container of the final shape).

[0101] Additionally or alternatively, operating parameters can be obtained from inspection (which can be performed by an inspection system (not depicted) or an operator of molding system 200). An example of such operating parameters that can be obtained through inspection includes, but is not limited to, parameters of the molded article, such as physical properties, dimensions, weight, etc. In other words, the inspection system can generate an indication of the current value of the operating parameter; the current value is used to trigger the process to iteratively optimize the target operating parameter.

[0102] Additionally or alternatively, inspection can identify impurities or molding-related defects present in the molded article (such as, but not limited to, short shots).

[0103] Another example of a source of operating parameters could be equipment used to supply material to the molding machine 100, such as a post-consumer plastic recycling system (which may be implemented as a liquid polycondensation system, etc.), which can provide operating parameters including, but not limited to, resin parameters such as viscosity, flake quality, humidity level, residence time, etc. More broadly, operating parameters obtained by the controller 140 from such equipment could include viscosity, melt temperature distribution, the state of the filter element, the quality of the incoming flakes (i.e., ground old bottles or other types of recycled material), the humidity of the incoming flakes, residence time in the hopper, etc.

[0104] In some embodiments of this technology, the aforementioned inspection of the molded article can provide operating parameters for defining statistical ranges (of critical dimensions). These statistical ranges can be used by the controller 140 to determine, at least in part, which operating parameters are within acceptable ranges for optimal performance of the molding machine 100 based on statistical variations.

[0105] The developers of this technology understand that some operating parameters of the molding system 200 have very tight variations (i.e., small standard deviations). The controller 140 can be configured to determine that the molding system 200 operates with optimized performance in response to current operating parameters being within the tight range. Conversely, if the controller 140 determines that the variations are outside the tight range, the controller 140 can determine that further optimization is needed.

[0106] In some non-limiting embodiments of this technology, controller 140 may generate a database (not depicted) relating the performance of molding system 200 to optimized and non-optimized configurations. Controller 140 may use the database to execute various optimization routines described herein.

[0107] control system

[0108] refer to Figure 2 It depicts a block diagram of a non-limiting embodiment of a control system 200 for operating an injection molding system 200. The control system 200 may, for example, be located in a computing device 140 (…). Figure 1 The system is implemented in [the following context]. As shown in the figure, the control system 200 is configured in layers. These layers include the enterprise platform layer 202, the supervisory control layer 204, and the control layer 206.

[0109] The control layer 206 includes multiple control modules, each of which typically controls the operation of its respective subsystem of the injection molding system 200. For example, the depicted control modules 207-1, 207-2, ... 207-10 are responsible for controlling the injection unit 106, the movable pressure plate 104, the robot 122, and other components of the injection molding system 200. Each control module may include one or more programmable logic controllers (PLCs) coupled to individual actuators and sensors within the subsystem.

[0110] The supervisory control layer 204 includes a supervisory controller 205. The supervisory controller 205 interfaces with the control module of the control layer 206 to guide and coordinate molding operations, manage the configuration of subsystems, and guide the production of manufactured articles. The supervisory control layer 204 is interconnected with the enterprise platform layer 202 via a network. The network can be a local area network (LAN) or a wide area network such as the Internet.

[0111] The supervisory controller 205 is operable to receive and interpret operational information, such as status messages, associated with the molding system 200 from each of the control modules 207 in the control layer 206. In this example, message sending may be initiated by a control module. For instance, messages may be sent in response to the initiation or completion of a processing step, or periodically.

[0112] The supervisory controller 205 also implements the aforementioned human-machine interface (HMI) or operator interface functions, which may include a graphical user interface presented to the operator on one or more display panels, which may be touch-sensitive. The HMI may also be equipped with hardware buttons or other manual controls for specific functions.

[0113] The enterprise platform layer 202 includes one or more servers 203 and can be used as a data repository for operational data required for producing molded articles using the injection molding system 200.

[0114] According to a non-limiting embodiment of this technology, the master data structure list can be maintained as part of the enterprise platform layer 202, and at any given time, only a subset of the data can be copied to the supervisory control layer 204 and the control layer 206. For example, the data can be stored in a master database at the enterprise layer 202, and a subset of the data can be written to memory at the supervisory control layer 204 or the control module at the control layer 206. The copied data may, for example, be simply data related to the possible configuration of physically available tools for installation.

[0115] The enterprise platform layer 202 and / or the monitoring layer 204 can be configured to monitor the production / output of the injection molding system 200. Specifically, orders defining production requirements can be entered or received at the enterprise platform layer 202 and / or the monitoring layer 204.

[0116] Production requirements may include, for example, the type and quantity of molded articles required, and the time required for the articles to be produced. Based on the production requirements, the enterprise platform layer 202 and / or the supervisory layer 204 may schedule production of a specific type and quantity, and send instructions to the supervisory control layer 204 to carry out production according to the schedule. In some embodiments, the enterprise platform layer 202 communicates with the supervisory control layer 204 of multiple molding systems and may schedule and guide the production of each molding system.

[0117] In some embodiments, the enterprise platform layer 202 is interconnected with a supervisory control layer 204 of multiple different molding systems (not depicted). In such embodiments, the enterprise platform layer 202 can request configuration information from each molding system to determine which systems are capable of producing the required type and quantity of articles. Production scheduling may involve allocating production instructions based on capacity and the current production being scheduled.

[0118] In some embodiments, the enterprise platform layer 202 and / or the supervisory control layer 204 provide interfaces for external users, such as operators, to input instructions and monitor production. For example, operators can access the enterprise platform layer 202 and / or the supervisory control layer 204 through the interface to issue production orders or retrieve data about production progress. This interface can be provided via a wide area network (WAN) such as the Internet. For example, users can interact with the enterprise platform layer 202 and / or the supervisory control layer 204 through a website or mobile application or by calling one or more APIs.

[0119] refer to Figure 3 The supervisory control layer 204 may store or otherwise access the envelope database 302, the tool and action database 304, and multiple machine learning algorithms 306, such as neural network-based machine learning algorithms (MLA) or decision tree-based MLA.

[0120] Broadly speaking, the envelope database 302 stores information about observed operating parameters of the injection molding machine 100. These operating parameters may include the number of preforms manufactured, the number of cycles, energy consumption, hydraulic pressure, pump speed, battery charging, filling rate, holding time, cooling time, transfer speed, injection start, injection end, holding start, holding end, clamping tonnage, tonnage locking engagement and disengagement time, etc. The envelope database 302 may also include indications of historical performance parameters of the injection molding machine 100, such as cycle time, part weight, mold breathing, etc.

[0121] It should be noted that the architecture of the envelope database 302 can depend particularly on various aspects of the operation of the molding system. In some embodiments, the envelope database 302 may include a data acquisition module to capture and record real-time operational information from the respective operating components, such as real-time temperature, pressure, material usage, cycle time, and other relevant process variables. The collected data is then processed, systematically organized, and / or stored in a manner that ensures accurate and reliable storage.

[0122] In some embodiments, to maintain data integrity and security, the envelope database 302 incorporates encryption and access control mechanisms, thereby allowing only authorized entities to access specific information while protecting sensitive data from unauthorized entities. The envelope database 302 can be configured to undergo regular backups and redundancy measures to prevent potential data loss and system downtime.

[0123] Furthermore, the database facilitates seamless integration with external systems and third-party applications, enabling data exchange and interoperability across various platforms. This interoperability promotes enhanced communication between the molding system and other production units, thereby facilitating a more comprehensive understanding of the entire manufacturing process.

[0124] In at least some embodiments of this technology, the envelope database 302 may store data accessible to one or more data analysis tools and / or machine learning algorithms to derive meaningful insights from the accumulated data. It is conceivable that the processor 140 may be configured to access the envelope database 302 to retrieve target data and use the target data during an optimized process toward the operational goals of the molding system 100.

[0125] Tools and Actions Database 400

[0126] refer to Figure 4The text describes a non-limiting example of the contents of a tool and motion database 400. More broadly, the tool and motion database 400 stores information representing multiple models 402 (in the depicted embodiment, four models are depicted: power consumption, cycle time, part weight, and mold breathing). Each of the multiple models 402 is associated with at least one of the operating parts of the injection molding system 200, as referenced above. Figure 1 The operating component is described. Four models are depicted in the illustrated diagrams. However, it should be understood that a particular implementation of the tool and motion database 400 may have fewer or more models.

[0127] According to a non-limiting embodiment of the present technology, multiple models 402 are used to optimize the operating parameters of the injection molding machine 100 (based on the operator selecting an optimization objective from multiple possible optimization objectives) until a predetermined result is achieved. Therefore, the multiple models 402 may also be referred to herein from time to time as "optimization models" or "multiple optimization models".

[0128] In some non-limiting embodiments of this technology, a given model among a plurality of models 402 is used to indicate operating parameters of respective operating components associated with the injection molding machine 100, which can be varied to achieve an associated optimization objective. In a sense, the operating parameters can be considered as “tools” for the optimization task, while the given model can be considered as a “toolbox” that can be used to achieve the selected optimization objective.

[0129] In some non-limiting embodiments of this technology, each of the plurality of models 402 further includes the sensitivity of the operating parameters to changes in order to achieve the operational objective. For example, in some embodiments of this technology, given one of the plurality of models 402, it can indicate that by changing a given operating parameter by an amount X, the change in the performance of the associated operating component will be Y.

[0130] For the same reason, a given one of the multiple models 402 can indicate that by changing a given operating parameter by an amount X, the effect of another operating parameter of the given one of the multiple models 402 will be Z. In some non-limiting embodiments of the present technology, sensitivity parameters can be used in the optimization process of the operating parameters. For example, and as will be described in more detail below, during the iterative optimization process, a larger change can be performed at the beginning of the optimization process based on sensitivity, while the change can be reduced again based on the sensitivity indication as the optimization begins to get closer to the predetermined result.

[0131] In some non-limiting embodiments, the given model is further configured to indicate the interdependence between the operating parameters of the first operating component and the operating parameters of the second operating component. In some non-limiting embodiments of this technology, the interdependence is based on a mathematical model.

[0132] Broadly speaking, in some non-limiting embodiments of this technology, a given model among a plurality of models 402 is used to enable the computing device 140 to evaluate the impact of the operating parameters of the first operating unit and the operating parameters of the second operating unit during optimization.

[0133] The developers of this technology have further recognized that not all operating parameters of the injection molding machine 100 have an equal impact on the output of the injection molding machine 100 (such as the cost per molded article produced, the quality of the molded articles produced, etc.). For example, the resin consumption of the injection molding machine 100 can have a relatively higher impact on the total production cost than other operating parameters. Therefore, in some non-limiting embodiments of this technology, multiple models 402 can be organized in a hierarchical structure, wherein some models among the multiple models 402 can have a higher optimization priority based on economic criteria. For example, in some non-limiting embodiments, it is conceivable that those models among the multiple models 402 associated with operating parameters having a higher economic impact can obtain a relatively higher priority in such a model hierarchy. In some embodiments of this technology, those models among the multiple models 402 associated with operating parameters having a higher economic impact can obtain a relatively higher execution priority in such a model hierarchy.

[0134] In other non-limiting embodiments of this technology, by collecting parameters associated with the resin used in real time, simultaneous optimization of several operating parameters (such as optimization for the type of resin used and optimization for energy consumption) based on two or more of the multiple models 402 can be implemented. As an example, in some of these non-limiting embodiments of this technology, some of the multiple models 402 can be organized into subgroups, where optimization to maintain optimal performance (within the subgroup) of two or more operating parameters can be performed substantially simultaneously based on the real-time collection of associated performance data. As a non-limiting example, the developers of this technology have understood that the greatest source of variability can be attributed to the resin processed by the injection molding machine 100. It is further understood that the quality of the resin may be difficult to determine in advance. Furthermore, variations in the resin used by the injection molding machine 100 (such as, but not limited to, PCR, PCR / virgin blends, virgin material differences, various types of additives, such as, but not limited to, AA blockers or other additives) can have an impact on overall molding efficiency and / or good molded article yield.

[0135] There are no particular limitations on how the multiple models 402 are generated and / or filled. In some non-limiting embodiments of the present technology, the multiple models 402 are physical models associated with operating components of the injection molding machine 100. In some of these embodiments, at least some of the multiple models 402 are mathematical models. In some of these embodiments, at least some of the multiple models 402 are derived from first principles.

[0136] Multiple models 402 can be generated by the manufacturer of injection molding machine 100 using observations of multiple injection molding machines during testing or operation at a customer site. In other non-limiting embodiments of the technology, multiple models 402 can be generated using computer modeling or machine learning algorithms (MLA).

[0137] In a specific illustration, multiple models 402 include:

[0138] • First model 404 related to power consumption;

[0139] • Second model 406 associated with cycle time;

[0140] • The third model 408, which is associated with the weight of the part; and

[0141] • The fourth model 410 is associated with mold breathing.

[0142] It should be clearly understood that multiple models 402 can have fewer or more models.

[0143] In a broader sense, multiple models 402 map optimization objectives (such as power consumption (G1a / G1b), cycle time (G2a / G2b), part weight (G3a / G3b), mold breathing (G4a / G4b) etc.) to available tools (i.e., variable operating parameters such as pump speed (T1), battery charging (T2), filling rate (T3), holding time (T4), cooling time (T5), transfer speed (T6), injection start (T7), mold clamping tonnage (T8), tonnage locking (T9) etc.).

[0144] exist Figure 4 It can be observed that each of the multiple models 402 has at least some tools (i.e., variable operating parameters) that are different from those of another model in the multiple models 402. For example, only the first model 404 has a pump speed (T1) as a usable tool (i.e., a variable operating parameter). In other words, it can be said that at least one usable tool in terms of the variable operating parameters of the operating parts of one model is different from at least one usable tool in terms of the variable operating parameters of the operating parts of another model.

[0145] Using the examples of the first model 404 and the second model 406, we will now describe how to store information within multiple models 402 and how to use it for the optimization of operator-selectable parameters (also referred to as “optimization objectives” in this document).

[0146] The first model 404 is associated with power consumption as an optimization objective. It includes two optimization objectives: one for increasing power consumption (G1a) and the other for decreasing power consumption (G1b). In other words, each of the multiple models 402 includes indications of operating parameters for an upward direction of optimization and a downward direction of optimization.

[0147] Model 404 first illustrates the interdependencies of six tools used for optimizing power consumption. More specifically, the available tools for increasing power consumption (G1a) are:

[0148] • Increase pump speed (T1a)

[0149] • Increase battery charging (T2a)

[0150] • Increase the fill rate (T3a)

[0151] • Increase transmission speed (T6a)

[0152] • Increase clamping capacity (T8a)

[0153] • Neutral tonnage lock (T9b)

[0154] The available tools for reducing power consumption (G1b) are:

[0155] • Reduce pump speed (T1b)

[0156] • Reduce battery charging (T2b)

[0157] • Reduce the fill rate (T3b)

[0158] • Reduce transmission speed (T6b)

[0159] • Reduce clamping tonnage (T8b)

[0160] • Enable tonnage locking (T9a)

[0161] Similarly, model 406 illustrates the interdependence of four tools for optimizing the cycle time objective. More specifically, the available tools for increasing the cycle time (G2a) are:

[0162] • Gap filling rate (T3b)

[0163] • Increase holding time (T4a)

[0164] • Increased cooldown time (T5a)

[0165] • Delayed injection start (T7b)

[0166] The available tools for reducing cycle time (G2b) are:

[0167] • Increase the fill rate (T3a)

[0168] • Reduce holding time (T4b)

[0169] • Reduced cooling time (T5b)

[0170] • Allow injections to begin earlier (T7a)

[0171] Multiple models 402 also store information about the interdependencies of various models within the multiple models 402. Figure 4 Interdependence 412 is schematically illustrated. In the illustrated embodiment, interdependence 412 is an illustration of the interdependence between changes in one available tool for one optimization objective relative to another optimization objective. For example, a decrease in battery charging will adversely affect cycle time. Interference can be generated based on information stored in envelope database 302.

[0172] Machine learning algorithms

[0173] In some embodiments, the processor 110 may be configured to employ machine learning algorithms to control the operating parameters of the molding system. Broadly, the machine learning algorithms can be trained on data stored in the envelope database 302 and / or the tool and motion database 304 to generate signals for autonomous control of the operating components of the molding system.

[0174] Training data can include combinations of one or more operating parameters of one or more operating components, such as temperature, pressure, material properties, cycle time, and other relevant process variables. These machine learning algorithms are designed to analyze and identify complex patterns, trends, and relationships within historical data to generate models of the molding process dynamics.

[0175] In some embodiments, a given machine learning model can be trained to analyze and identify complex patterns, trends, and relationships among one of the following models: a first model 404 associated with power consumption, a second model 406 associated with cycle time, a third model 408 associated with part weight, and a fourth model 410 associated with mold breathing.

[0176] During real-time operation, processor 140 can autonomously interpret data acquired from the operating components of the molding system using one or more trained machine learning algorithms. Processor 140 may also employ a feedback control loop to continuously compare real-time data with pre-trained patterns and adjust the system's control parameters accordingly. In doing so, the algorithms optimize the molding process in response to changing conditions, optimization objectives, and / or constraints, enabling the molding system to adapt to dynamic production environments while maintaining product quality and consistency.

[0177] Optimize process

[0178] refer to Figure 5 A flowchart of a method 500 for operating an injection molding system 200 (including an injection molding machine 100) will now be described. The following description will use an example of controlling the injection molding machine 100, but the same method can be applied in substantially the same way to other components of the injection molding system 200 (such as those upstream or downstream of the injection molding machine 100, and components located both upstream and downstream of the injection molding machine 100).

[0179] Method 500 has a user side 502 and a machine side 504. Machine side 504 illustrates the steps performed by computing device 140. User side 502 illustrates the interaction between computing device 140 and the operator of injection molding machine 100. It should be noted that in some non-limiting embodiments of this technology, at least some of the steps in user side 502 may be optional. In some other non-limiting embodiments of this technology, at least some of the steps in user side 502 may be implemented by computing device 140 or by another computing device, such as a server implementing machine learning algorithms (MLA).

[0180] Method 500 begins with computing device 140 receiving from the operator of injection molding machine 100 an instruction of an operational objective associated with the injection molding machine 100 to be optimized. This is depicted as step 510, where the operator inputs the optimization objective using the aforementioned HMI. For illustrative purposes, the optimization objective is to reduce power consumption. More broadly, step 510 involves computing device 140 receiving from the operator of injection molding machine 100 an instruction of operational parameters to be optimized, selectable from multiple operational parameters of multiple operational components. In alternative, non-limiting embodiments of the art, the instruction of operational parameters to be optimized may be received from a supervised plant model attempting to optimize the entire plant. In some of these non-limiting embodiments, the use of multiple models 402 can be used to optimize performance at the plant level, not just at the level of injection molding machine 100.

[0181] In some non-limiting embodiments of this technology, a single optimization objective can be selected from multiple possible optimization objectives at any given time. However, in alternative non-limiting embodiments of this technology, it is foreseeable that two or more optimization objectives can be selected for their joint optimization.

[0182] At step 511, computing device 140 determines which of the plurality of models 402 is applicable to the optimization objective selected at step 510. More broadly, computing device 140 selects the target optimization model from the plurality of models 402 based on the operating parameters to be optimized.

[0183] In a non-limiting embodiment of this technology, step 511 includes one or more of steps 512-520:

[0184] At step 512, computing device 140 examines references from a plurality of models 402. Computing device 140 accesses the tool and action database 400 and determines which of the plurality of models 402 is associated with the selected optimization objective. In this example, it is the first model 404 associated with power consumption. Based on the indicated interdependencies 412, computing device 140 further identifies a second model 406 associated with cycle time as an interdependent model. In some embodiments, during step 512, computing device 140 is configured to evaluate whether the selected optimization objective is an achievable optimization objective.

[0185] At step 514, computing device 140 retrieves envelope information from envelope database 302. Based on the selected optimization objective, and based on the identified (or target) models among the plurality of models 402 and the information stored in envelope database 302, computing device 140 identifies historical performance information. In some embodiments, during step 514, computing device 140 may be configured to determine a set of historical optimization parameters when the molding system has operated according to the optimization objective. In some non-limiting embodiments of the art, computing device 140 retrieves information about a “baseline machine” (i.e., another injection molding machine that has been operating at or near the optimization parameters).

[0186] In the illustrated embodiment, computing device 140 determines:

[0187] High power during fill function

[0188] • Fill rate is at a high level

[0189] • Fill time is below average

[0190] At step 516, computing device 140 accesses tool and action database 304 to locate the toolbox. Based on the parameters determined at step 514, computing device 140 selects an appropriate tool in first model 404, which in this example is reducing battery charging (T2b) to achieve lower power consumption (G1b). In some embodiments, the selection of an appropriate tool in first model 404 may be based on a set of historical optimization parameters determined at step 514.

[0191] At step 518, computing device 140 accesses the effects of various tools available within tool and action database 304. The tool and action database 400 includes indications of interdependencies 412, and computing device 140 determines the following effects in this example:

[0192] • Reducing battery charging time will decrease recharge time, thus affecting cycle time.

[0193] • And will additionally lead to insufficient clamping tonnage.

[0194] At step 520, computing device 140 selects a target tool from a plurality of tools available for a given model. In the illustrated embodiment, computing device 140 selects reduced battery charge (T2b) as an applicable tool for optimization.

[0195] At step 522, the computing device 140 iteratively optimizes the operating parameters using a target optimization model until a predetermined result (also referred to as an "envelope") is achieved. In some non-limiting embodiments of the present technology, optimization can be performed by modeling the behavior of the injection molding machine 100. In other non-limiting embodiments of the present technology, optimization can be performed by running the injection molding machine 100 with newly set parameters and observing the feedback (i.e., the new performance parameters of the injection molding machine 100).

[0196] At step 524, the computing device 140 checks whether the envelope has been reached. In some non-limiting embodiments of the art, the computing device 140 checks whether the performance of the injection molding machine 100 has been pushed beyond an acceptable envelope on another operating parameter by optimizing a given operating parameter. In the illustrated embodiment, and based on interdependence 412, the computing device 140 can verify whether the power consumption optimization at the current iteration has an adverse effect on the cycle time.

[0197] If “No”, the computing device 140 returns to step 522.

[0198] If “yes”, the computing device 140 proceeds to step 526, in which the computing device 140 generates a report and presents the report to the operator using the aforementioned HMI.

[0199] At step 528, the operator monitors the optimization progress and intervenes via the aforementioned HMI if necessary. For example, the operator can adjust the optimization objective, select a different optimization objective, abort the optimization, or determine that the optimization is complete.

[0200] At step 530, the operator determines whether the optimization objective has been achieved and whether the optimization is acceptable. In some non-limiting embodiments of this technology, this step involves checking whether a predetermined result has been achieved. If "yes," method 500 terminates at 550.

[0201] If "No", method 500 proceeds to step 532, where computing device 140 determines whether the envelope can be further pushed. To do so, computing device 140 may return to step 512, where computing device 140 attempts to optimize different models among multiple models 402 of the operating parameters of the injection molding machine based on an initial optimization objective (or another optimization objective).

[0202] In other words, in response to the optimization objective failing to meet the predetermined result during the iterative optimization of the target optimization parameters, the computing device 140 can select another target model from a plurality of models 402 for optimizing other target operation parameters of at least one other target operation component.

[0203] It should be noted that even though step 530 has been described as being performed on the user side 502, these steps can also be automated and implemented as part of the machine side 504, where the computing device 140 checks the current performance in the optimization cycle against predetermined results. For example, in this example of optimizing energy consumption, the computing device 140 can check that energy usage has been minimized without adversely affecting the cycle time (based on interdependence 412).

[0204] According to a non-limiting embodiment of the present technology, once the optimization objective has been achieved, the computing device 140 can use such optimized operating parameters to control the injection molding system 200.

[0205] In some non-limiting embodiments of this technology, optimized operating parameters can be used to operate the injection molding machine 100. In some embodiments, the processor 140 can adjust the operating parameters of the injection molding machine to match the optimized operating parameters.

[0206] In some non-limiting embodiments of this technology, the adjustment of operating parameters to match optimized operating parameters can be performed by using active mold components (or other components of the injection molding machine 100) (i.e., adaptable and / or programmable components). Examples of such active components are disclosed in commonly owned patent applications with publication numbers WO 2005 / 102654 (published November 3, 2005), WO2005 / 102661 (published November 3, 2005), WO2005 / 102650 (published November 3, 2005), WO2005 / 102649 (published November 3, 2005), WO2010 / 121349 (published October 28, 2010) and WO2011 / 054080 (published May 12, 2011), the entire contents of which are incorporated herein by reference. It should be noted that at least some of the steps in method 500 may overlap or be performed substantially simultaneously.

[0207] It should be noted that, in alternative, non-limiting embodiments of this technology, active components that can be used to adjust operating parameters may include, but are not limited to: molding components having adaptable molding surfaces; adjustable heat dissipation components and / or processes; dynamic gate holes, etc.

[0208] Optimized additional non-restrictive examples

[0209] According to non-limiting embodiments of the present technology, the following operating parameters can be optimized.

[0210] • Power bank energy consumption optimized for application

[0211] • Injection fill rate customized according to part geometry and features

[0212] • Injection start timing to minimize mold breathing.

[0213] • Holding and cooling times categorized by part weight and outlet temperature

[0214] • Recovery rate and transfer speed reduced to a minimum

[0215] • The barrel heating temperature should be adjusted according to the resin conditions.

[0216] • Operations during startup process

[0217] • Processing temperature of molding machine 100 and / or upstream and downstream equipment

[0218] • Resin consumption

[0219] In some embodiments of this technology, during the start-up process of the molding machine 100, it may be necessary to optimize the performance of the molding machine 100 (e.g., reduce its speed). This can be a tedious process typically performed by the operator of the molding system 200. According to some non-limiting embodiments of this technology, the processor 140 can be configured to automatically adjust the operation of the molding system 200 to a reduced rate, eliminating the need for currently manually performed venting start and stop. This can directly or indirectly result in optimized energy consumption, optimized operator time usage, and elimination of manual steps susceptible to operator error and inefficiency.

[0220] In some embodiments of this technology, controller 140 may be configured to implement bidirectional communication from upstream / downstream devices (not depicted). Therefore, in some embodiments of this technology, controller 140 may be configured to optimize the performance of molding machine 100 during unplanned machine downtime, which may include transmitting commands to an upstream dryer to control the dryer to different temperature setpoints to avoid “cooking” the material.

[0221] In some embodiments of this technology, optimization of resin consumption may be based on optimization of the weight of the molded article. In some of these embodiments, the inspection (manual or automatic) may include weighing the molded article. When optimizing resin consumption, the controller 140 may be configured to optimize the weight distribution of the parts to allow for a reduction in part weight, i.e., to make the molded articles more consistent in weight distribution or among themselves.

[0222] In some embodiments of this technology, controller 140 can optimize the maintenance cycle of molding system 200. This can have the following technical effects: improved efficiency, at least lower downtime, and improved timing when scheduled maintenance occurs (i.e., scheduling maintenance for multiple service points at the same time (in parallel), rather than maintaining them one by one). In other words, controller 140 can optimize the performance of molding system 200 so that certain parts will be synchronized with other parts in terms of lifespan (service life).

[0223] For example,

[0224] • It may happen that at a given time in the molding system 200, in response to determining that there is 3 months of service for the valve and 1 month of service for the motor, the controller 140 may execute an optimized routine that slows down the motor to compensate for the gap and allows these service points to be synchronized (at least partially).

[0225] • If the molding system 200 has a peak season (production commitment) and will be in a low season for 2 months, then the controller 140 can optimize performance to “extend” the life of the equipment to two more reliable months compared to discovering that you will replace something within a month.

[0226] In some embodiments of this technology, the controller 140 can optimize the overall performance of the molding system 200, where a given molding system 200 may operate at lower performance due to infrastructure constraints. Examples of such infrastructure constraints include, but are not limited to, a lack of mold cooling or tower water cooling. The former will prolong cooling cycle time to maintain part quality, while the latter will result in slower cycles to limit pump motor power to match tower water cooling capacity.

[0227] In a typical molding system 200, both services have sensor (thermocouple) feedback, and these signals can serve as supervisory (i.e., "not exceeding") limits to control overall system performance. This signal feedback can be used to optimize the performance of the molding system 200 and determine the "cycle time balance".

[0228] In some non-limiting embodiments of this technology, controller 140 may be further configured to perform polymer optimization and / or chemical chain optimization. In other words, the molding process is optimized to minimize degradation of the molding material (e.g., reduction in molecular weight, intrinsic viscosity, formation of harmful end groups). In these embodiments of this technology, controller 140 may receive indications of parameters of the resin used (e.g., one or more of the following: indication of the content of recycled polymer; intrinsic viscosity of the resin; indication of molding byproducts (such as harmful end groups)). This information may be obtained, for example, from one or more of infrared spectroscopy, gas chromatography-flame ionization (GC-FID), gas chromatography-mass spectrometry (GC-MS), or reversed-phase high-performance liquid chromatography (RP-HPLC). The indication may include detailed identification and quantification of one or more parameters of the resin (e.g., impurities), or it may provide a more general indication, such as resin degradation exceeding predefined limits. Based on this information, controller 140 may be configured to optimize one or more operating parameters of molding system 200 or other downstream or upstream equipment. For example, controller 140 may then adjust resin plasticization based on indications of resin parameters. For example, controller 140 can optimize the rotational speed of the plasticizing screw. Additionally or alternatively, controller 140 can optimize the temperature to which the polymer is heated during plasticizing. Additionally or alternatively, controller 140 can optimize the amount of AA scavenger added.

[0229] Non-limiting examples of envelope information

[0230] refer to Figure 6 This describes an example of envelope information 602 stored in envelope database 302.

[0231] In the illustrated embodiment, envelope information 602 represents the fill rate. As implied above, envelope information 602 can be generated by the manufacturer of injection molding machine 100 by observing one or more injection molding machines during testing or operation at a customer site.

[0232] Envelope information 602 is depicted in the "Inner Envelope" section 604 and the "Outer Envelope" section 606. The "Inner Envelope" section 604 represents the acceptable range of operating parameters (in this case, the fill rate). The "Outer Envelope" section 606 represents the unacceptable range of operating parameters (in this case, the fill rate).

[0233] In other words, the aforementioned reference to “checking the envelope” is intended to indicate whether the current value of the associated operation parameter falls within either the “inside the envelope” section 604 or the “outside the envelope” section 606.

[0234] Modifications and improvements to the above embodiments of this technology will be apparent to those skilled in the art. The foregoing description is exemplary and not restrictive. Therefore, the scope of this technology is intended to be limited only by the scope of the appended claims.

[0235] The description of embodiments of this technology provides only examples of the technology, and these examples do not limit the scope of the technology. It should be clearly understood that the scope of the technology is limited only by the claims. The above concepts can be applied to specific conditions and / or functions, and can be further extended to various other applications within the scope of this technology. Having thus described embodiments of the technology, it will be apparent that modifications and enhancements are possible without departing from the described concepts.

Claims

1. A method of operating a molding system, the molding system having a plurality of operating components and a controller configured to control at least some of the plurality of operating components, the method executable by the controller and comprising: receiving an indication of an operating parameter to be optimized, the operating parameter selectable from a plurality of operating parameters of the plurality of operating components; based on the operating parameter to be optimized, selecting a target optimization model from a plurality of optimization models; using the target optimization model, iteratively optimizing the target operating parameter until a predetermined result is achieved.

2. The method of claim 1, wherein the target optimization model further comprises an indication of the predetermined result.

3. The method of claim 1, wherein the plurality of optimization models are physical models of operation of the molding system.

4. The method of claim 1, wherein the plurality of optimization models are organized in a hierarchy such that a first optimization model of the plurality of optimization models in the hierarchy has a higher rank than a second optimization model of the plurality of optimization models based on an economic criteria.

5. The method of claim 1, wherein the target optimization model is a target subset of optimization models of the plurality of optimization models associated with a subset of operating parameters of the plurality of operating parameters, and wherein the using the target subset of optimization models comprises: iteratively and simultaneously optimizing the subset of target operating parameters until the predetermined result is achieved.

6. The method of claim 1, wherein the plurality of operating components comprises a blow molding machine and a molding machine, the blow molding machine disposed downstream of the molding machine, the indication of the operating parameter received from the blow molding machine.

7. The method of claim 1, wherein the plurality of operating components comprises a dryer and a molding machine, the dryer disposed upstream of the molding machine, the indication of the operating parameter received from the dryer.

8. A method of operating a molding system, the molding system having a plurality of operating components and a controller configured to control the plurality of operating components, the method executable by the controller and comprising: receiving an indication of an operating target associated with the molding system; accessing a database storing a plurality of models, each model of the plurality of models associated with at least one of the plurality of operating components; based on the operating target, selecting a target model from the plurality of models, the target model for optimizing a target operating parameter of at least one target operating component of the plurality of operating components; using the target model, iteratively optimizing the target operating parameter; wherein iteratively optimizing comprises checking the target operating parameter of the at least one target operating component against a predetermined result; and in response to satisfying the predetermined result, using the target operating parameter to control the molding system.

9. The method of claim 8, wherein: a given model of the plurality of models is for indicating: (i) operating parameters of respective operating components associated with a given one of the plurality of operating envelopes.

10. The method of claim 9, wherein the given model is used to indicate a mutual dependency of an operating parameter of a first operating component and an operating parameter of a second operating component.

11. The method of claim 10, wherein the given model is used to enable the controller to evaluate an impact of the operating parameter of the first operating component and the operating parameter of the second operating component during optimization.

12. The method of claim 10, wherein the mutual dependency is based on a mathematical model.

13. The method of claim 9, wherein the given model of the plurality of models further indicates: (ii) a sensitivity of an operating parameter to change to achieve the operating objective.

14. The method of claim 8, wherein: in response to the optimization objective not satisfying the predetermined result during the iterative optimization of the target optimization parameter, selecting from the plurality of models a further objective model for optimizing a further objective operating parameter of at least one further objective operating component.

15. The method of claim 8, wherein the method further comprises receiving an operator input of the predetermined result prior to using the target operating parameter to control the molding system.

16. The method of claim 8, wherein the at least one further objective operating component of the further objective model is different than the at least one target operating component of the target model.

17. The method of claim 8, wherein the operating parameter comprises at least one of pump speed, battery charge, fill rate, hold time, cool time, transfer speed, injection start, tonnage lock, and tonnage lock.

18. The method of claim 8, wherein each model of the plurality of models comprises an indication of an upward direction of operating parameters for optimization and a downward direction of operating parameters for optimization.

19. The method of claim 8, wherein the operating objective is a lowest energy cost per molded article.

20. The method of claim 8, wherein the operating objective is a lowest energy consumed per molded article.

21. The method of claim 8, wherein the operating objective is a lowest production rate for a predetermined production requirement.

22. The method of claim 8, wherein the operating objective is an optimized maintenance schedule.

23. The method of claim 8, wherein the operating objective is an optimized part weight.

24. The method of claim 8, wherein the operating objective is an optimized resin consumption.

25. The method of claim 8, wherein the operating objective is an optimized resin plasticization.

26. The method of claim 25, wherein prior to the iterative optimization of the target operating parameter, the method further comprises receiving an indication of a current operating parameter of the target operating parameter.

27. The method of claim 26, wherein the receiving the indication of the current operating parameter of the target operating parameter comprises receiving an indication of a polymer used in the molding system.

28. The method of claim 27, wherein the indication of the polymer used in the molding system includes an indication of a content of the polymer.

29. The method of claim 28, wherein the indication of the content of the polymer includes an indication of harmful end groups.

30. The method of claim 27, wherein the iteratively optimizing the target operating parameter includes varying at least one of (i) a process parameter and (ii) a structure associated with the molding system, the varying being based on the indication of the polymer.

31. A molding system having a plurality of operating components and one or more controllers, the controllers configured to: receive an indication of an operating parameter to be optimized, the operating parameter being selectable from a plurality of operating parameters of the plurality of operating components; based on the operating parameter to be optimized, select a target optimization model from a plurality of optimization models; iteratively optimize the target operating parameter using the target optimization model until a predetermined result is achieved.

32. The molding system of claim 31, wherein the target optimization model further includes an indication of the predetermined result.

33. The molding system of claim 31, wherein the plurality of optimization models are physical models of operation of the molding system.

34. The molding system of claim 31, wherein the plurality of optimization models are organized in a hierarchy such that a first optimization model of the plurality of optimization models in the hierarchy has a higher rank than a second optimization model of the plurality of optimization models based on an economic criterion.

35. The molding system of claim 31, wherein the target optimization model is a target subset of optimization models of the plurality of optimization models associated with a subset of operating parameters of the plurality of operating parameters, and wherein using the target subset of optimization models, the molding system is configured to: iteratively and simultaneously optimize the subset of target operating parameters until the predetermined result is achieved.

36. The molding system of claim 31, wherein the plurality of operating components includes a blow molding machine and a molding machine, the blow molding machine being disposed downstream of the molding machine, the indication of the operating parameter being received from the blow molding machine.

37. The molding system of claim 31, wherein the plurality of operating components includes a dryer and a molding machine, the dryer being disposed upstream of the molding machine, the indication of the operating parameter being received from the dryer.

38. A molding system having a plurality of operating components and one or more controllers, the controllers configured to: receive an indication of an operating target associated with the molding system; access a database storing a plurality of models, each model of the plurality of models being associated with at least one of the plurality of operating components; based on the operating target, select a target model from the plurality of models, the target model being used to optimize a target operating parameter of at least one target operating component of the plurality of operating components; iteratively optimize the target operating parameter using the target model. wherein iteratively optimizing includes checking the target operating parameters of the at least one target operating component against a predetermined outcome; and controlling the molding system using the target operating parameters in response to the predetermined outcome being satisfied.

39. The molding system of claim 38, wherein: a given model of the plurality of models is used to indicate: (i) operating parameters of respective operating components associated with a given one of the plurality of operating envelopes.

40. The molding system of claim 38, wherein the given model is used to indicate a mutual dependency of an operating parameter of a first operating component and an operating parameter of a second operating component.

41. The molding system of claim 40, wherein the given model is used to enable the controller to evaluate an impact of the operating parameter of the first operating component and the operating parameter of the second operating component during optimization.

42. The molding system of claim 40, wherein the mutual dependency is based on a mathematical model.

43. The molding system of claim 39, wherein the given model of the plurality of models further indicates: (ii) a sensitivity of an operating parameter to change in order to achieve the operating objective.

44. The molding system of claim 38, wherein the one or more controllers are further configured to: select, in response to the optimization objective not satisfying the predetermined outcome during the iterative optimization of the target optimization parameters, another target model from the plurality of models for optimizing other target operating parameters of at least one other target operating component.

45. The molding system of claim 38, wherein the one or more controllers are further configured to receive an operator input of the predetermined outcome prior to controlling the molding system using the target operating parameters.

46. The molding system of claim 38, wherein the at least one other target operating component of the other target model is different from the at least one target operating component of the target model.

47. The molding system of claim 38, wherein the operating parameters include at least one of pump speed, battery charge, fill rate, hold time, cool time, transfer speed, injection start, clamp tonnage, and tonnage lock.

48. The molding system of claim 38, wherein each model of the plurality of models includes an indication of operating parameters for an upward direction of optimization and a downward direction of optimization.

49. The molding system of claim 38, wherein the operating objective is a lowest energy cost per molded article.

50. The molding system of claim 38, wherein the operating objective is a lowest energy consumed per molded article.

51. The molding system of claim 38, wherein the operating objective is a lowest production rate for a predetermined production requirement.

52. The molding system of claim 38, wherein the operating objective is an optimized maintenance schedule.

53. The molding system of claim 38, wherein the operating objective is an optimized part weight.

54. The molding system of claim 38, wherein the operational target is optimized resin consumption.

55. The molding system of claim 38, wherein the molding system further comprises an inspection system, and wherein the one or more controllers are further configured to receive an indication of a current value of the operational parameter from the inspection system; the current value being used to trigger a process to iteratively optimize the target operational parameter.

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