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

Abstract

Injection molding systems and methods for controlling injection molding systems are disclosed. The injection molding system having a plurality of operational components and one or more controllers. The controllers are configured to receive an indication of an operational parameter to be optimized, the operational parameter selectable from a plurality of operational parameters of the plurality of operational components, and based on the operational parameter to be optimized, select a target optimization model from a plurality of optimization models. The controllers are also configured to, by using the target optimization model, iteratively optimize the target operational parameter until a pre-determined result is achieved.

Description

[0001] METHOD OF OPERATING A MOLDING SYSTEM, A MOLDING SYSTEM AND A CONTROLLER FOR CONTROLLING OPERATION OF MOLDING SYSTEM

[0002] FIELD OF THE TECHNOLOGY

[0003] The present technology relates to molding systems and methods for operating the molding systems. More specifically the present technology relates to a molding system for having a controller for operating the molding system while optimizing its performance.

[0004] BACKGROUND

[0005] Molding is a process by virtue of which a molded article can be formed from molding material by using a molding system. Various molded articles can be formed by using the molding process, such as an injection molding process. One example of a molded article that can be formed, for example, from polyethylene terephthalate (PET) material is a preform that is capable of being subsequently blown into a beverage container, such as a bottle and the like. However, various other molded articles can be produced, such as thin-walled containers, medical appliances (such as blood vials and syringes), components for automotive industry, and the like.

[0006] Broadly speaking, the cost of producing a molded article is made up of the capital cost of the molding system itself, the cost of resin, and other overheads (electricity, water supply, labour costs, etc.). To gamer the most profitability from the system, the molding system should be running as much as possible, and at full capacity. At the same time, it is important to ensure the quality of the output (i.e., molded articles), while ensuring that the output meets customer’s (i.e., molding system operator’s) specification and other production preferences.

[0007] WO 20212 / 37339 discloses a molding system having a supervisory controller operable to track a current operating condition of a plurality of process stations, define paths from ones of the process stations ready to release an output unit, and ones of the process stations ready to receive an input unit, and provide instructions for the transportation system to move the input and output units along the paths.

[0008] SUMMARY

[0009] It is an object of the present invention to ameliorate at least some of the inconveniences present in the prior art.

[0010] Broadly speaking, embodiments of the present technology are directed to a method of optimizing performance of the molding system. Developers of the present technology have appreciated that it is desirable to develop a molding system and a controller associated therewith for allowing the molding system to optimize itself for different customer needs. For example, instead of optimizing for highest output, it may be desirable to optimize the molding system for another operator- selectable optimization goal.

[0011] Broadly speaking, the controller of the molding system can be configured to sense any combination of one or more operating parameters and to dynamically adapt one or more (same or other) operational parameters to optimize the operations of the molding system. A given molding system is a complex system with multiple interdependent processes and a number of operational parameters of the molding system may need to be optimized to achieve the most optimal performance. As such, embodiments of the present technology are configured to sense any operational parameter of the molding system (as well as system upstream or downstream thereof), as well as dynamically adapt one or more operational parameters of the molding system (other system upstream or downstream thereof).

[0012] Without wishing to be bound by any specific theory, one area where dynamic sensing and adapting may be beneficial is the use of recycled molding material in the molding system. Broadly speaking, recycled molding material may have been prepared by a mechanical or a chemical process. It can be said that mechanically recycled PET is essentially a new polymer that has varied and distinct properties to virgin PET that pose unique processing challenges. Due to the varying nature of the feedstock, the steady state setup of the system is rather more dynamic, wherein the structure and / or process is adapted in step with the detected properties of the RPET. Put another way, the mechanically recycled polymer feedstock can be dynamically processed to address the varying, and somewhat cumulative, changes in polymer properties until it is eventually unfit for its intended purpose and then sent on for chemical recycling, thus rendering it new again.

[0013] The controller may be embodied as one or more processors of a computer system configured to optimize the molding system based on inter alia different customer needs. In some embodiments, functionalities of the controller may be executed via a single hardware processor. In other embodiments, functionalities of the controller may be executed via multiple hardware processors. Furthermore, one or more functionalities of the controller as disclosed herein may be executed by a distributed processing system, without departing from the scope of the present technology.

[0014] In some embodiments, the controller may be configured to employ data collected by one or more sensors to continuously monitor operational parameters of the molding system. Additionally, the controller may be configured to employ one or more computer-implemented models to process captured data and adjust one or more parameters of the molding process based on inter alia a user input. For example, the controller may receive a user input, collect real-time data, analyze this data, and trigger one or more actions or commands for dynamically optimizing one or more operational parameters such as temperature, pressure, and cycle times, and so forth, in accordance with an optimization goal set via the user input.

[0015] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal other than maximum production. As a broad example, an optimization goal may be a given requirement for the given molding system that is important to the given operator of the given molding system. In this broad example, the embodiments of the present technology provide a controller that is configured to optimize operational parameters of one or more components of the given molding system towards that requirement.

[0016] In some non-limiting embodiments, the controller may be configured to optimize operational parameters of one or more components towards the optimization goal of the given molding system using a “feedback mechanism” during an iterative adjustment of operation of the given molding system.

[0017] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is the lowest energy expense i.e., optimize for lowest energy cost per molded article.

[0018] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is the lowest energy consumption i.e., optimize for lowest energy consumed per molded article.

[0019] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is productivity, i.e., attenuating production rate to meet a production requirement.

[0020] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is machine reliability, i.e., save machine life through optimizing production rate.

[0021] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is a peak load, i.e., to minimize peak electrical load. In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is the time, i.e., scheduling constraints or a production plan.

[0022] In some non-limiting embodiments, the operator of the given molding system may be desirous of optimizing the molding system for an optimization goal that is people, i.e., when skilled team members are accessible. In an alternative non-limiting embodiment, it may be desirous to automatically optimize performance of the given molding system during a startup procedure.

[0023] In a first broad aspect of the present technology, there is provided a method of operating an injection molding system. The injection molding system has a plurality of operational components and a controller. The controller is configured to control the plurality of operational components. The method is executable by the controller. The method comprises receiving an indication of an operational parameter to be optimized, the operational parameter selectable from a plurality of operational parameters of the plurality of operational components. The method comprises, based on the operational parameter to be optimized, selecting a target optimization model from a plurality of optimization models. The method comprises, by using the target optimization model, iteratively optimizing the target operational parameter until a pre-determined result is achieved.

[0024] In some embodiments of the present technology, the target optimization model further includes an indication of the pre-determined result.

[0025] In some embodiments of the present technology, the plurality of optimization models are physical models of operations of the injection molding system.

[0026] In some embodiments of the present technology, the plurality optimization models are organized in a hierarchical structure such that a first one from the plurality optimization models is ranked higher than a second one from the plurality of optimization models in the hierarchical structure based on an economic criterion.

[0027] In some embodiments of the present technology, the target optimization model is a target subgroup of optimization models from the plurality of optimization models associated with a subgroup of operational parameters from the plurality of operational parameters. The using the target sub-group of optimization models includes iteratively and concurrently optimizing the sub-group of target operational parameters until the pre-determined result is achieved. In some embodiments of the present technology, the plurality operational components include a blow-molding machine and a molding machine. The blow-molding machine is disposed downstream from the molding machine. The indication of the operational parameter is received from the blow-molding machine.

[0028] In some embodiments of the present technology, the plurality operational components include a dryer and a molding machine. The dryer is disposed upstream from the molding machine. The indication of the operational parameter is received from the dryer.

[0029] In a second broad aspect of the present technology, there is provided a method of operating an injection molding system. The injection molding system has a plurality of operational components and a controller. The controller is configured to control the plurality of operational component. The method is executable by the controller. The method comprises receiving an indication of an operational goal associated with the injection molding system. The method comprises accessing a database storing a plurality of models, each model of the plurality of models being associated with at least one of the plurality of operational components. The method comprises, based on the operational goal, selecting a target model from the plurality of models, the target model for optimizing target operational parameters of at least one target operational component from the plurality of operational component. The method comprises, by using the target model, iteratively optimizing the target operational parameters; wherein iteratively optimizing includes checking the target operational parameters of the at least one target operational component against a predetermined result. The method comprises in response to the pre-determined result being satisfied, using the target operational parameters to control the injection molding system.

[0030] In some embodiments of the present technology, a given model of the plurality of models is for indicating operational parameters of the respective operational components associated with a given one of the plurality of operational envelope.

[0031] In some embodiments of the present technology, the given model is for indicating an interdependency of an operational parameter of a first operational components with an operational parameter of a second operational component.

[0032] In some embodiments of the present technology, the given model is for enabling the controller to assess impact of the operational parameter of the first operational components and the operational parameter of the second operational component during optimization. In some embodiments of the present technology, the interdependence is based on a mathematical model.

[0033] In some embodiments of the present technology, the given model of the plurality of models further indicates operational parameters' sensitivity to change for achieving the operational goal.

[0034] In some embodiments of the present technology, the method further includes, in response to the optimization goal not meeting the pre-determined result during the iterative optimization of the target optimizing parameters, selecting an other target model from the plurality of models for optimizing other target operational parameters of at least one other target operational component.

[0035] In some embodiments of the present technology, the method further comprises receiving an operator input on the pre-determined results prior to using the target operational parameter to control the injection molding system.

[0036] In some embodiments of the present technology, the at least one other target operational component of the other target model is different from the at least one target operational component of the target model.

[0037] In some embodiments of the present technology, the operational parameters comprise at least one of a pump speed, accumulator charge, a fdl rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock.

[0038] In some embodiments of the present technology, in each model of the plurality of models includes an indication of operational parameters for both an upward direction for optimization and a downward direction for optimization.

[0039] In some embodiments of the present technology, the operational goal is a lowest energy cost per molded article.

[0040] In some embodiments of the present technology, the operational goal is a lowest energy consumed per molded article.

[0041] In some embodiments of the present technology, the operational goal is a lowest production rate for a pre-determined production requirement.

[0042] In a third broad aspect of the present technology, there is provided an injection molding system having a plurality of operational components, and one or more controllers. The one or more controllers are configured to receive an indication of an operational parameter to be optimized, the operational parameter selectable from a plurality of operational parameters of the plurality of operational components. The one or more controllers are configured to, based on the operational parameter to be optimized, select a target optimization model from a plurality of optimization models. The one or more controllers are configured to, by using the target optimization model, iteratively optimize the target operational parameter until a pre-determined result is achieved.

[0043] In some embodiments of the injection molding system, the target optimization model further includes an indication of the pre-determined result.

[0044] In some embodiments of the injection molding system, the plurality of optimization models are physical models of operations of the injection molding system.

[0045] In some embodiments of the injection molding system, the plurality optimization models are organized in a hierarchical structure such that a first one from the plurality optimization models is ranked higher than a second one from the plurality of optimization models in the hierarchical structure based on an economic criterion.

[0046] In some embodiments of the injection molding system, the target optimization model is a target sub-group of optimization models from the plurality of optimization models associated with a subgroup of operational parameters from the plurality of operational parameters. By using the target sub-group of optimization models the injection molding system is configured to iteratively and concurrently optimize the sub-group of target operational parameters until the pre-determined result is achieved.

[0047] In some embodiments of the injection molding system, the plurality operational components include a blow-molding machine and a molding machine. The blow-molding machine is disposed downstream from the molding machine. The indication of the operational parameter is received from the blow-molding machine.

[0048] In some embodiments of the injection molding system, the plurality operational components include a dryer and a molding machine. The dryer is disposed upstream from the molding machine. The indication of the operational parameter being received from the dryer.

[0049] In a fourth broad aspect of the present technology, there is provided an injection molding system having a plurality of operational components, and one or more controllers. The one or more controllers are configured to receive an indication of an operational goal associated with the injection molding system. The one or more controllers are configured to 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 operational components. The one or more controllers are configured to, based on the operational goal, select a target model from the plurality of models, the target model for optimizing target operational parameters of at least one target operational component from the plurality of operational component. The one or more controllers are configured to, by using the target model, iteratively optimize the target operational parameters; wherein iteratively optimizing includes checking the target operational parameters of the at least one target operational component against a pre-determined result. The one or more controllers are configured to, in response to the pre-determined result being satisfied, use the target operational parameters to control the injection molding system.

[0050] In some embodiments of the injection molding system, a given model of the plurality of models is for indicating operational parameters of the respective operational components associated with a given one of the plurality of operational envelope.

[0051] In some embodiments of the injection molding system, the given model is for indicating an interdependency of an operational parameter of a first operational components with an operational parameter of a second operational component.

[0052] In some embodiments of the injection molding system, the given model is for enabling the controller to assess impact of the operational parameter of the first operational components and the operational parameter of the second operational component during optimization.

[0053] In some embodiments of the injection molding system, the interdependence is based on a mathematical model.

[0054] In some embodiments of the injection molding system, the given model of the plurality of models further indicates operational parameters' sensitivity to change for achieving the operational goal.

[0055] In some embodiments of the injection molding system, the one or more controllers are further configured to, in response to the optimization goal not meeting the pre-determined result during the iterative optimization of the target optimizing parameters, select an other target model from the plurality of models for optimizing other target operational parameters of at least one other target operational component. In some embodiments of the injection molding system, the one or more controllers are further configured to receive an operator input on the pre-determined results prior to using the target operational parameter to control the injection molding system.

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

[0057] In some embodiments of the injection molding system, the operational parameters comprise at least one of a pump speed, accumulator charge, a fill rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock.

[0058] In some embodiments of the injection molding system, each model of the plurality of models includes an indication of operational parameters for both an upward direction for optimization and a downward direction for optimization.

[0059] In some embodiments of the injection molding system, the operational goal is a lowest energy cost per molded article.

[0060] In some embodiments of the injection molding system, the operational goal is a lowest energy consumed per molded article.

[0061] In some embodiments of the injection molding system, the operational goal is a lowest production rate for a pre-determined production requirement.

[0062] In the context of the present specification, the words “first”, “second”, “third”, etc. have been used as adjectives only for the purpose of allowing for distinction between the nouns that they modify from one another, and not for the purpose of describing any particular relationship between those nouns. Further, as is discussed herein in other contexts, reference to a “first” element and a “second” element does not preclude the two elements from being the same actual real-world element.

[0063] These and other aspects and features of non-limiting embodiments of the present technology will now become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the technology in conjunction with the accompanying drawings. Embodiments of the present technology each have at least one of the above-mentioned object and / or aspects, but do not necessarily have all of them. It should be understood that some aspects of the present technology that have resulted from attempting to attain the above-mentioned object may not satisfy this object and / or may satisfy other objects not specifically recited herein.

[0064] 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.

[0065] BRIEF DESCRIPTION OF THE DRAWINGS

[0066] A better understanding of the embodiments of the present technology (including alternatives and / or variations thereof) may be obtained with reference to the detailed description of the nonlimiting embodiments along with the following drawings, in which:

[0067] FIG. 1 depicts a schematic diagram of an injection system including a controller implemented in accordance with non-limiting embodiments of the present technology;

[0068] FIG. 2 is a schematic view of components of a control system including a controller of the molding system of FIG. 1 ;

[0069] FIG. 3 is a block diagram showing components of a supervisory control layer of the control system of FIG. 2;

[0070] FIG. 4 is a schematic representation of a table representation of a list of tools maintained by a tools and action database of the supervisory control layer of FIG. 3;

[0071] FIG. 5 depicts a schematic diagram of a flow chart of a method controlling the injection molding system of FIG. 1 ; and

[0072] FIG. 6 depicts a schematic representation of an envelope information maintained in the envelope database of the supervisory control layer of FIG. 3.

[0073] DETAILED DESCRIPTION

[0074] Reference will now be made in detail to various non-limiting embodiment(s) of a molding system and a related method for the operation thereof. It should be understood that other non-limiting embodiment(s), modifications and equivalents will be evident to one of ordinary skill in the art in view of the non-limiting embodiment(s) disclosed herein and that these variants should be considered to be within scope of the appended claims.

[0075] Furthermore, it will be recognized by one of ordinary skill in the art that certain structural and operational details of the non-limiting embodiment(s) discussed hereafter may be modified or omitted (i.e., non-essential) altogether. In other instances, well known methods, procedures, and components have not been described in detail.

[0076] Injection Molding System

[0077] With reference to FIG. 1, there is depicted a non-limiting embodiment of the injection molding machine 100 which can be adapted to implement embodiments of the present technology. For illustration purposes only, it shall be assumed that the injection molding machine 100, which is part of a molding system 200, is configured to make preforms (not depicted) for subsequent blowmolding into a final shaped container. It should be noted that the molding machine 100 can be implemented as any suitable type of a molding machine, such as but not limited to, a hydraulic machine, an all-electric machine, and hybrids thereof.

[0078] It should be understood that in alternative non-limiting embodiments, the 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, and the like. Also, the molding system 200 could include additional machines such as a blow-molding machine (not depicted) disposed downstream from the molding machine 100 and / or a dryer disposed upstream from the molding machine 100. Alternatively, the blow-molding machine can be located separately from the molding system 200, such as being in a different facility, be under control of a different entity, and the like.

[0079] In the non-limiting embodiment of FIG. 1 , the molding machine 100 includes a stationary platen 102 and a movable platen 104. The stationary platen 102 is also referred to as a fixed platen 102. In some embodiments of the present technology, the molding machine 100 may include a third non-movable platen (not depicted). Alternatively, or additionally, the molding machine 100 may include turret blocks, rotating cubes, turning tables and the like (all not depicted but known to those of skill in the art).

[0080] The injection molding machine 100 further includes an injection unit 106 for plasticizing and injection of the molding material. The injection unit 106 can be implemented as a single stage or a two-stage injection unit. In some alternative non-limiting embodiments of the present technology, the injection unit 106 can be implemented as one or more injection units (not depicted). It is however noted that the working principle of the molding machine 100 can be implemented in any known working principles.

[0081] In operation, the movable platen 104 is moved towards and away from the stationary platen 102 by means of stroke cylinders (not shown) or any other suitable means. Clamp force (also referred to as closure or mold closure tonnage) can be developed within the molding machine 100, for example, by using tie bars 108, 110 (typically, four tie bars 108, 110 are present in the molding machine 100) and a tie-bar clamping mechanism 112, as well as (typically) an associated hydraulic system (not depicted) that is usually associated with the tie-bar clamping mechanism 112. It will be appreciated that clamp tonnage can be generated using alternative means, such as, for example, using a column-based clamping mechanism, a toggle-clamp arrangement (not depicted) or the like.

[0082] The molding machine 100 has a mold 113 having a first mold half 114, and a second mold half 116. The first mold half 114 can be associated with the stationary platen 102 and the second mold half 116 can be associated with the movable platen 104. In the non-limiting embodiment of FIG. 2, the first mold half 114 defines the mold cavities 118. The mold cavities 118 generally form an array, such that there is a given number of columns and a given number or rows. In some embodiments, there could be eight columns and twelve rows of mold cavities 118 making 96 cavities in total, but it should be noted that embodiments of the present technology are not limited to any specific cavitation number. As will be appreciated by those of skill in the art, the mold cavities 118 may be formed by using suitable mold inserts (such as a cavity insert, a gate insert and the like) or any other suitable means. As such, the first mold half 114 can be generally thought of as a “mold cavity half’.

[0083] The second mold half 116 includes mold cores 120 complementary to the mold cavities 118, such that the mold cores 120 also generally form an array similar to the array formed by the mold cavities 118, where the array formed by the mold cores 120 has the same number of columns and rows as the array formed by the mold cavities 118. As will be appreciated by those of skill in the art, the mold cores 120 may be formed by using suitable mold inserts or any other suitable means. As such, the second mold half 116 can be generally thought of as a “mold core half’. Even though not depicted in FIG. 1, the first mold half 114 may be further associated with a melt distribution network, commonly known as a hot runner, for distributing molding material from the injection unit 106 to each of the mold cavities 118. Also, the second mold half 116 is provided with neck rings (not depicted) produce preforms with the neck portions of the preforms (not depicted). The first mold half 114 can be coupled to the stationary platen 102 by any suitable means, such as a suitable fastener (not depicted) or the like. The second mold half 116 can be coupled to the movable platen 104 by any suitable means, such as a suitable fastener (not depicted) or the like. It should be understood that in an alternative non-limiting embodiment of the present technology, the position of the first mold half 114 and the second mold half 116 can be reversed and, as such, the first mold half 114 can be associated with the movable platen 104 and the second mold half 116 can be associated with the stationary platen 102. In an alternative non-limiting embodiment of the present technology, the stationary platen 102 need not be stationary and may be movable in relation to other components of the molding machine 100.

[0084] FIG. 1 depicts the first mold half 114 and the second mold half 116 in a so-called “mold open position” where the movable platen 104 is positioned generally away from the stationary platen 102 and, accordingly, the first mold half 114 is positioned generally 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 a so-called “mold closed position” (not depicted), the first mold half 114 and the second mold half 116 are urged together (by means of movement of the movable platen 104 towards the stationary platen 102) and cooperate to define (at least in part) molding cavities into which the molten plastic (or other suitable molding material) can be injected, as is known to those of skill in the art. Due to the arrangement of the mold cavities 118 and the mold cores 120, the molding cavities generally form a mold array, also referred to as molding cavity array. The molding cavity array has the same number of columns and rows as the array formed by the mold cavities 118 and the mold cores 120.

[0085] The injection molding machine 100 can further include a robot 122, sometimes referred to as a retrieval device and / or a removal device, operatively coupled to the stationary platen 102. Those skilled in the art will readily appreciate how the robot 122 can be operatively coupled to the stationary platen 102 and, as such, it will not be described in detail herein. The robot 122 includes a mounting structure 124, an actuating arm 126 coupled to the mounting structure 124 and a takeoff plate 128 coupled to the actuating arm 126. The take-off plate 128 includes a plurality of molded article receptacles 130.

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

[0087] Schematically depicted in FIG. 1, the robot 122 is of a side-entry type. However, it should be understood that in alternative non-limiting embodiments of the present technology, the robot 122 can be of a top-entry type or other. It should also be expressly understood that the term “robot” is meant to encompass 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 could be omitted and / or replaced with a differently implemented device for moving the molded articles 50.

[0088] It is to be noted that the robot 122 is configured to manipulate the molded articles 50 while preserving the molding cavity array (i.e., the robot 122 does not scramble the positioning of the molded articles).

[0089] The molding machine 100 further includes a post-mold treatment device 132 operatively coupled to the movable platen 104. Those skilled in the art will readily appreciate how the post-mold treatment device 132 can be operatively coupled to the movable platen 104 and, as such, it will not be described here in any detail. The post-mold treatment device 132 includes a mounting structure 134 used for coupling the post-mold treatment device 132 to the movable platen 104. The post-mold treatment device 132 further includes a plenum 129 coupled to the mounting structure 134. Coupled to the plenum 129 is a plurality of treatment pins 133. The number of treatment pins within the plurality of treatment pins 133 generally corresponds to the number of receptacles within the plurality of molded article receptacles 130. In at least some embodiments, it is also contemplated that the post-mold treatment device 132 could be omitted and / or replaced with a differently implemented device for treating the molded articles. It should be noted that even though in the depicted embodiment, the post-mold treatment device 132 is coupled to the movable platen 104, in alternative non-limiting embodiments of the present technology, the post-mold treatment device 132 can be implemented differently, for example, it does not need to be coupled to the movable platen 104 for movement therewith.

[0090] The molding machine 100 further includes a computing apparatus 140, also referred to herein as a processor 140 or as a “controller”, 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 that the molding machine 100 is part of, and which will be described in greater detail below. As will be appreciated by those skilled in the art, the computing apparatus 140 may include a plurality of processors or computer-implemented devices operatively connected together.

[0091] The processor 140 includes a human-machine interface (not separately numbered) or an HMI, for short. The HMI of the processor 140 can be implemented in any suitable interface. As an example, the HMI of the processor 140 can be implemented in a multi-functional touch screen. An example of the HMI that can be used for implementing non-limiting embodiments of the present technology is disclosed in co-owned United States Patent 6,684,264, content of which is incorporated herein by reference, in its entirety.

[0092] Those skilled in the art will appreciate that the processor 140 may be implemented using preprogrammed hardware or firmware elements (e.g., application specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), etc.), or other related components. In other embodiments, the functionality of the processor 140 may be achieved using a processor that has access to a code memory (not shown) which stores computer-readable program code for operation of the computing apparatus, in which case the computer-readable program code could be stored on a medium which is fixed, tangible and readable directly by the various network entities, (e.g., removable diskette, CD-ROM, ROM, fixed disk, USB drive), or the computer- readable program code could be stored remotely but transmittable to the processor 140 via a modem or other interface device (e.g., a communications adapter) connected to a network (including, without limitation, the Internet) over a transmission medium, which may be either a non-wireless medium (e.g., optical or analog communications lines) or a wireless medium (e.g., microwave, infrared or other transmission schemes) or a combination thereof.

[0093] In alternative non-limiting embodiments of the present technology, the HMI does not have to be physically attached to the processor 140. As a matter of fact, the HMI for 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, for example) that is “paired” or otherwise communicatively coupled to the processor 140.

[0094] The processor 140 can perform several functions including, but not limited to, receiving from an operator control instructions, controlling the molding machine 100 based on the operator control instructions or a pre-set control sequence stored within the processor 140 or elsewhere within the molding machine 100, acquiring one or more operational parameters associated with the molding system and the like (such as one or more of: a pump speed, accumulator charge, a fdl rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock). According to non-limiting embodiments of the present technology, the processor 140 is further configured to process one or more of the acquired operational parameters associated with the molding system 200 and output information to the operator using the HMI and the like. It is contemplated that the processor 140 may be implemented in a distributed manner over a plurality of physical machines, without departing from the scope of the present technology.

[0095] The molding machine 100 further includes a number of monitoring devices (not depicted), the monitoring devices being configured to acquire various operational parameters associated with the performance of the molding machine 100. Generally speaking, these monitoring devices are known in the art and, as such, will not be described here at any length.

[0096] Just as an example, the injection molding machine 100 may include a counter to count mold opening and closing to determine 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 a number of pressure gauges to measure pressure within various components of the injection molding machine 100 (such as hydraulic fluid pressure or molding material pressure).

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

[0098] The processor 140 can acquire operational parameters (such as one or more of: a pump speed, accumulator charge, a fill rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock) by monitoring the operation of the molding machine 100. Just as an example, the processor 140 can acquire the cycle time by monitoring the performance of the molding machine 100. Naturally, the processor 140 can acquire some of the machine variables by either the operator entering them using the HMI or by reading a memory tag (not depicted) associated with the mold (i.e., the above described first mold half 114 and the second mold half 116) that is used in the molding machine 100. Various implementations of the memory tag (not depicted) are known in the art. Generally speaking, the memory tag (not depicted) may store information about the mold, the molded article to be produced, pre-defined control sequences, setup sequences and the like. In some non-limiting embodiments of the present technology, the processor 140 can acquire the operational parameters by receiving an indication of those parameters from the operator. However, within some implementations of the molding machine 100, it is possible for the processor 140 to acquire some (or even all) of the operational parameters by monitoring performance of the molding machine 100. In some non-limiting embodiments of the present technology, this can be implemented by inspecting the molded articles and assessing compliance with specification, then accepting, rejecting articles on the basis of the assessment. This can be done by suitable devices (such as for example, an optical inspection device) or by an operator. Naturally, other ways for the processor 140 to acquire some or all of these or other operational parameters are possible, some of which will be described below.

[0099] In alternative non-limiting embodiments of the present technology, the processor 140 can acquire the operational parameters from other components of the molding system 200, such as but not limited to the aforementioned blow-molding machine (not depicted) disposed downstream from the molding machine 100 and / or a dryer disposed upstream from the molding machine 100. Additionally or alternatively, the operational parameters can be associated with a quality parameter of the blown or fdled container made from the molded article (this is particularly applicable in those embodiments, where the molded article is a preform for subsequent blow-molding into a final shaped container).

[0100] Additionally or alternatively, the operational parameters can be obtained from an inspection (which can be executed by an inspection system, not depicted, or an operator of the molding system 200). One example of such operational parameter that can be obtained through the inspection includes but is not limited to parameters of a molded article, such as physical attributes - dimensions, weight, etc. In other words, the inspection system can generate an indication of a current value of the operational parameter; the current value being used for triggering a process of iterative optimization of the target operational parameters.

[0101] Additionally or alternatively, the inspection can identify impurities present in the molded articles or molding related defects (such as but not limited to short shots and the like).

[0102] Another example of a source of operational parameters can be a device for supplying material to the molding machine 100, such as a post consumer plastic recycling device (which can be implemented as a liquid state polycondensation device or the like), which can provide operational parameters that include but are not limited to resin parameters (such as viscosity, quality of flakes, moisture level, residence time, and the like). Broadly speaking, the operational parameters obtained by the controller 140 from such devices can include viscosity, temperature profile of the melt, status of the filter elements, quality of the incoming flakes (i.e., ground up old bottles or other types of recycled material), moisture of the incoming flakes, residence time in the hopper, etc.

[0103] In some embodiments of the present technology, the aforementioned inspection of the molded articles can provide operational parameters from which to define statistical ranges (of key dimensions). These statistical ranges can be used by the controller 140 to determine what operational parameters are within acceptable ranges for the optimal performance of the molding machine 100, based at least in part on the statistical variation.

[0104] Developers of the present technology have appreciated that some of the operating parameters of the molding system 200 have a very tight variation - i.e., small standard deviation. The controller 140 can be configured, in response to the current operating parameters being within the tight range, to determine that the molding system 200 runs at an optimized performance. To the contrary, if the controller 140 determines a variation outside of the tight range, the controller 140 can determine that further optimization is required.

[0105] In some non-limiting embodiments of the present technology, the controller 140 can generate a database (not depicted) that correlates performance of the molding system 200 to optimized and non-optimized configurations. The database can be used by the controller 140 to execute various optimization routines described herein.

[0106] Control system

[0107] With reference to FIG. 2, there is depicted a block diagram of a non-limiting embodiment of a control system 200 for operating the injection molding system 200. The control system 200 can be implemented, for example, in the computing apparatus 140 (FIG. 1). As shown, the control system 200 is configured in layers. The tiers include an enterprise platform layer 202, a supervisory control layer 204 and a control layer 206.

[0108] The control layer 206 includes a plurality of control modules, each of which generally controls operation of a respective subsystem of the injection molding system 200. For example, the depicted control modules 207-1, 207-2, ...207-10 are responsible for controlling injection unit 106, the movable platen 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.

[0109] The supervisory control layer 204 includes a supervisory controller 205. The supervisory controller 205 interfaces with control modules of the control layer 206 in order to direct and coordinate molding operations, manage configuration of the subsystems and to direct production of articles. The supervisory control layer 204 is interconnected with enterprise platform layer 202 by way of a network. The network may be a local-area network (LAN) or a wide-area network such as the internet.

[0110] The supervisory controller 205 is operable to receive and interpret operating information associated with the molding system 200, such as status messages, from each of the control modules 207 of the control layer 206. In an example, sending of messages may be initiated by the control modules. For example, messages may be sent in response to initiation or completion of a processing step, or on a periodic basis.

[0111] The supervisory controller 205 also implements the aforementioned human machine interface (HMI) or operator interface functionality, 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.

[0112] The enterprise platform layer 202 comprises one or more servers 203 and may serve as a data repository for operational data required for production of molded articles using the injection molding system 200.

[0113] In accordance with the non-limiting embodiments of the present technology, master data structures listing may be maintained as part of enterprise platform layer 202 and at any given time, only a subset of data may be copied to the supervisory control layer 204 and the control layer 206. For example, data may be stored in a master database at the enterprise layer 202 and subsets of data may be written to memory at the supervisory control layer 204 or at the control modules of control layer 206. The data copied may, for example, be only data relating to configurations that are possible with tooling physically available for installation.

[0114] The enterprise platform layer 202 and / or the supervisory layer 204 may be configured to monitor production / output of the injection molding system 200. Specifically, orders defining production requirements may be input or received at the enterprise platform layer 202 and / or the supervisory layer 204. The production requirements may include, for example, types and quantities of the molded articles required, and time at which the articles are required. Based on the production requirements, the enterprise platform layer 202 and / or the supervisory layer 204 can schedule production of specific types and quantities, and send instructions to the supervisory control layer 204 to cause production according to the schedule. In some embodiments, enterprise platform layer 202 communicates with supervisory control layers 204 of multiple molding systems and can schedule and direct production by each of the molding systems.

[0115] In some embodiments, the enterprise platform layer 202 is interconnected with the supervisory control layers 204 of a plurality of different molding systems (not depicted). In such embodiments, the enterprise platform layer 202 may request configuration information for each molding system, to determine which systems are capable of producing articles of the desired types and in the desired quantities. Production scheduling may involve distributing production instructions based on the capabilities and current production scheduled.

[0116] In some embodiments, the enterprise platform layer 202 and / or the supervisory control layer 204 provide an interface for outside users such as operators to input instructions and monitor production. For example, operators may access the enterprise platform layer 202 and / or the supervisory control layer 204 through the interface to place production orders, or to retrieve data on production progress, or the like. The interface may be provided to by way of a wide-area network (WAN) such as the internet. For example, users may interact with the enterprise platform layer 202 and / or the supervisory control layer 204 through a website or mobile application or by calls to one or more APIs.

[0117] With reference to FIG. 3, the supervisory control layer 204 may store or otherwise have access to an envelope database 302, a tools and action database 304, and a plurality of machine learning algorithms 306, such as neural network-based machine learning algorithms (MLAs) or decision tree based MLA.

[0118] Broadly speaking, the envelope database 302 stores information about observed operational parameters of the injection molding machine 100. These operational parameters could be number of preforms manufactured, number of cycles, energy consumption, oil pressure, pump speed, accumulator charge, fill rate, hold time, cooling time, transfer speed, injection start, injection finish, hold start, hold finish, clamp tonnage, tonnage lock engagement and disengagement time, and the like. The envelope database 302 can have an indication of historical performance parameters of the injection molding machine 100, such as cycle time, part weight, mold breathing occurrence, and the like. It should be noted that an architecture of the envelope database 302 may depend on inter alia various aspects of the molding system’s operations. In some embodiments, the envelope database 302 may comprise data acquisition modules to capture and record real-time operational information from respective operational components such as real-time temperature, pressure, material usage, cycle times, and other relevant process variables, for example. This collected data is then processed, systematically organized and / or stored in a manner to ensure accurate and reliable storage.

[0119] In some embodiments, to maintain data integrity and security, the envelope database 302 incorporates encryption and access control mechanisms, allowing only authorized entities to access specific information while protecting sensitive data from unauthorized entities. The envelope database 302 may be configured to maintain recurrent backup and redundancy measures, safeguarding against potential data loss and system downtime.

[0120] Furthermore, the database facilitates seamless integration with external systems and third-party applications, allowing for data exchange and interoperability across various platforms. This interoperability promotes enhanced communication between molding systems and other production units, fostering a more comprehensive understanding of the overall manufacturing process.

[0121] In at least some embodiments of the present technology, the envelope database 302 may store data that is accessible by one or more data analytical tools and / or machine learning algorithms to derive meaningful insights from the accumulated data. It is contemplated that the processor 140 may be configured to access the envelope database 302 for retrieving target data and use the target data during an optimization process towards an operational goal of the moulding system 100.

[0122] Tools and action database 400

[0123] With reference to FIG. 4, a non-limiting example of content of the tools and action database 400 is depicted. Broadly speaking, the tools and action database 400 stores information representative of a plurality of models 402 (in the depicted embodiment, four models are depicted - Power Consumption, Cycle Time, Part Weight, and Mold Breathing). Each of the plurality of models 402 being associated with at least one of the operational components of the injection molding system 200, which have been described above with reference to FIG. 1. In the depicted illustration, four models are depicted. However, it should be understood that a particular implementation of the tools and action database 400 may have fewer or more of the models. In accordance with the non-limiting embodiments of the present technology, the plurality of models 402 are used for optimizing an operational parameter of the injection molding machine 100 (based on the operator selection of the optimization goal from a plurality of possible optimization goals) until a pre-determined result is achieved. Thus, the plurality of models 402 can also be referred to herein below from time to time as “optimization models” or a “plurality of optimization models”.

[0124] In some non-limiting embodiments of the present technology, a given model of the plurality of models 402 is for indicating operational parameters of the respective operational components associated with the injection molding machine 100 that can be changed in order to achieve the associated optimization goal. In a sense, the operational parameters can be considered ’’tools” for the optimization task, while the given model can be considered as a “tool box” that can be used for achieving a selected optimization goal.

[0125] In some non-limiting embodiments of the present technology, each one of the plurality of models 402 further includes operational parameters' sensitivity to change for achieving the operational goal. For example, in some embodiments of the present technology, the given one of the plurality of models 402 can indicate that by changing a given operational parameter by X amount, a change in performance of the associated operating component will be Y.

[0126] By the same token, the given one of the plurality of models 402 can indicate that by changing a given operational parameter by X amount, an impact on another operational parameter of the given one of the plurality of models 402 will be Z. In some non-limiting embodiments of the present technology, the sensitivity parameter can be used in the optimization process of the operational parameter. For example, and as will be described in greater detail below, during an iterative optimization process, based on the sensitivity, at the beginning of the optimization process, a larger magnitude of change can be executed, while the optimization starts to be closer to the predetermined result, the magnitude of change can be reduced, based again on the sensitivity indication.

[0127] In some non-limiting embodiments, the given model is further configured to indicate an interdependency of an operational parameter of a first operational component with an operational parameter of a second operational component. In some non-limiting embodiments of the present technology, the interdependence is based on a mathematical model. Broadly speaking, in some non-limiting embodiments of the present technology, the given model of the plurality of models 402 is for enabling the computing apparatus 140 to assess impact of the operational parameter of the first operational components and the operational parameter of the second operational component during optimization.

[0128] Developers of the present technology have further realized that not all operational parameters of the injection molding machine 100 may have equal impact on an output of the injection molding machine 100 (such as cost per molded article produced, quality of the molded article produced, and the like). For example, the resin consumption by the injection molding machine 100 may have a comparatively higher impact on the total cost of production than other operational parameters. Thus, in some non-limiting embodiments of the present technology, the plurality of models 402 may be organized in a hierarchical structure, where some models of the plurality of models 402 may have a higher priority of optimization based on an economic criterion. For example, in some non-limiting embodiments, it is contemplated that those models of the plurality of models 402 that are associated with operational parameters with higher economic implications may get a comparatively higher priority in such a hierarchy of models. In some embodiments of the present technology, those models of the plurality of models 402 that are associated with operational parameters with higher economic implications may get a comparatively higher execution priority in such a hierarchy of models.

[0129] In other non-limiting embodiments of the present technology, by real time collection of parameters associated with the resin used and concurrent optimization of several operational parameters based on two or more of the plurality of models 402 (such as for example, optimization for the type of resin used and for energy consumption) may be implemented. As an example, in some of these non-limiting embodiments of the present technology, some models of the plurality of models 402 may be organized in sub-groups, where optimization to maintain optimum performance of two or more operational parameters (in a sub-group) can be executed substantially concurrently, based on real time collection of associated performance data. As a non-limiting example, developers of the present technology have appreciated that the largest source of variability may be attributable to the resin processed by injection molding machine 100. It is further appreciated that the quality of resin may be difficult to determine beforehand. Furthermore, resin variations used by the injection molding machine 100 (such as but not limited to PCR, PCR / virgin blends, virgin material variances, various types of additives, such as but not limited to AA blocked or other additives) may have an impact on overall molding efficiency and / or good molded article yield. How the plurality of models 402 are generated and / or populated are not particularly limited. In some non-limiting embodiments of the present technology, the plurality of models 402 are physical models that are associated with the operating components of the injection molding machine 100. In some of these embodiments, at least some of the plurality of models 402 are mathematical models. In some of these embodiments, at least some of the plurality of models 402 are derived from first principles.

[0130] The plurality of the models 402 can be generated by the manufacturer of the injection molding machine 100 using an observation of a plurality of injection molding machine during testing or during operations at customers’ sites. In other non-limiting embodiments of the present technology, the plurality of models 402 can be generated using computer modelling or using machine learning algorithms (MLA).

[0131] In the particular illustration, the plurality of models 402 includes:

[0132] • A first model 404 associated with power consumption;

[0133] • A second model 406 associated with cycle time;

[0134] • A third model 408 associated with the part weight; and

[0135] • A fourth model 410 associated with mold breathing occurrence.

[0136] It should be expressly understood that the plurality of models 402 can have fewer or more models.

[0137] Broadly speaking, the plurality of models 402 maps optimization goals (such as power consumption (Gla / Glb), cycle time (G2a / G2b), part weight (G3a / G3b), mold breathing (G4a / G4b), etc.) to available tools (i.e., operational parameters that can be varied, such as pump speed (Tl), accumulator charge (T2), fill rate (T3), hold time (T4), cooling time (T5), transfer speed (T6), injection start (T7), clamp tonnage (T8), tonnage lock (T9), and the like).

[0138] It can be observed in FIG. 4, that each model of the plurality of models 402 has at least some of the tools (i.e., the operational parameters that can be varied) that are different as compared to another one of the plurality of models 402. For example, only the first model 404 has the pump speed (Tl) as the available tool (i.e., the operational parameters that can be varied). In other words, it can be said that at least one available tool in terms of the operational parameters that can be varied for an operational component of one model is different from at least one available tool in terms of the operational parameters that can be varied for the operational component of another model. Using the example of the first model 404 and the second model 406, it will now be described how the information within the plurality of models 402 is stored and how it can be used for optimization of an operator-selectable parameter (also referred herein as an “optimization goal”).

[0139] The first model 404 is associated with power consumption as the optimization goal. It comprises two optimization goals - one for increasing power consumption (Gia) and one for lowering power consumption (Gib). Put another way, each model of the plurality of models 402 includes an indication of operational parameters for both an upward direction for optimization and a downward direction for optimization.

[0140] The first model 404 shows six tool dependencies for the optimization goal of power consumption. More specifically, the available tools for the increasing the power consumption (Gia) are:

[0141] • Increasing pump speed (T1 a)

[0142] • Increasing accumulator charge (T2a)

[0143] • Increasing fill rate (T3a)

[0144] • Increasing transfer speed (T6a)

[0145] • Increasing clamp tonnage (T8a)

[0146] • Neutral on tonnage lock (T9b)

[0147] The available tools for the decreasing the power consumption (Gib) are:

[0148] • Decreasing pump speed (Tib)

[0149] • Decreasing accumulator charge (T2b)

[0150] • Decreasing fill rate (T3b)

[0151] • Decreasing transfer speed (T6b)

[0152] • Decreasing clamp tonnage (T8b)

[0153] • Enabling on tonnage lock (T9a)

[0154] Similarly, the second model 406 shows four tool dependencies for the optimization goal of cycle time. More specifically, the available tools for the increasing the cycle time (G2a) are:

[0155] • Neutral on fill rate (T3b)

[0156] • Increasing hold time (T4a)

[0157] • Increasing cooling time (T5a)

[0158] • Delay injection start (T7b) The available tools for the decreasing the cycle time (G2b) are:

[0159] • Increasing on fill rate (T3a)

[0160] • Decreasing hold time (T4b)

[0161] • Decreasing cooling time (T5b)

[0162] • Making the injection start earlier (T7a)

[0163] The plurality of models 402 also store information about interdependencies of the various models of the plurality of models 402. An interdependency 412 is schematically illustrated in FIG. 4. In the illustrated embodiment, the interdependency 412 is illustrative of an interdependency between a change to one of the available tool for one optimization goal on another optimization goal. For example, a decrease in the accumulator charge will negatively impact the cycle time. The interferences can be generated based on the information stored in the envelope database 302.

[0164] Machine learning algorithms

[0165] In some embodiments, the processor 110 may be configured to employ machine learning algorithms for controlling the operational parameters of a molding system. Broadly, the machine learning algorithms may be trained on data stored in the envelope database 302 and / or the tools and actions database 304 to generate signals to be used for autonomous control of the operational components of the molding system.

[0166] Training data may comprise a combination of one or more operational parameters of one or more operational components such as temperature, pressure, material characteristics, cycle times, and other relevant process variables. These machine learning algorithms are designed to analyze and identify complex patterns, trends, and relationships within the historical data, thereby generating a model of the molding process dynamics.

[0167] In some embodiments, a given machine learning model may be trained to analyze and identify complex patterns, trends, and relationships of a respective one amongst the first model 404 associated with power consumption, the second model 406 associated with cycle time, the third model 408 associated with the part weight, and the fourth model 410 associated with mold breathing occurrence.

[0168] During real-time operation, the processor 140 may use one or more trained machine learning algorithms to autonomously interpret data acquired from the operational components of the molding system. The processor 140 may also employ feedback control loops for continuously comparing real-time data with the pre-trained patterns and adjust the system's control parameters accordingly. By doing so, the algorithms optimize the molding process in response to varying conditions, optimization goals, and / or constraints, enabling the molding system to adapt to dynamic production environments while maintaining product quality and consistency.

[0169] Optimization Process

[0170] With reference to FIG. 5, a flow chart of a method 500 of operating the injection molding system 200 (including the injection molding machine 100) will now be described. The description below will use the example of controlling the injection molding machine 100, but the same method can be applied in a 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, as well as components located both upstream and downstream of the injection molding machine 100).

[0171] The method 500 has a user side 502 and a machine side 504. The machine side 504 illustrates steps performed by the computing apparatus 140. The user side 502 illustrates interactions between the computing apparatus 140 and an operator of the injection molding machine 100. It should be noted that in some non-limiting embodiments of the present technology, at least some of the steps of the user side 502 can be made optional. In some other non-limiting embodiments of the present technology, at least some of the steps of the user side 502 can be implemented by the computing apparatus 140 or by another computing device, such as a server implementing a machine learning algorithm (MLA) or the like.

[0172] The method 500 starts with the computing apparatus 140 receiving from an operator of the injection molding machine 100 an indication of an operational goal associated with the injection molding machine 100 to be optimized. This depicted as step 510 - where the operator enters the optimization goals using the aforementioned HMI. For the purposes of this illustration - the optimization goal is decreasing the power consumption. Broadly speaking, this step 510 is where the computing apparatus 140 receives from an operator of the injection molding machine 100 an indication of an operational parameter to be optimized, the operational parameter selectable from a plurality of operational parameters of the plurality of operational components. In alternative nonlimiting embodiments of the present technology, the indication of the operational parameter to be optimized can be received from a supervisory plant model that is trying to optimize the whole plant. In some of these non-limiting embodiments, the use of the plurality of models 402 can be used to optimize performance on the plant level, and not just at the level of the injection molding machine 100. In some non-limiting embodiments of the present technology, a single optimization goal from a plurality of possible optimization goals can be selected at any given time. However, in alternative non-limiting embodiments of the present technology, it is envisioned that two or more optimization goals can be selected for a join optimization thereof.

[0173] At step 511, the computing apparatus 140 determines which one of the plurality of models 402 are applicable for the optimization goal selected at step 510. Broadly speaking, the computing apparatus 140, based on the operational parameter to be optimized, selects a target optimization model from the plurality of models 402.

[0174] In non-limiting embodiments of the present technology, the step 511 contains one or more of steps 512 - 520:

[0175] At step 512, the computing apparatus 140 checks references from the plurality of models 402. The computing apparatus 140 access the tools and action database 400 and determines which of the plurality of models 402 is associated with the selected optimization goal. In this example - it is the first model 404 associated with the power consumption. Based on the indicated interdependency 412, the computing apparatus 140 further identifies the second model 406 associated with the cycle time as an interdependent model. In some embodiments, during the step 512 the computing apparatus 140 is configured to assess whether the selected optimization goal is an achievable optimization goal.

[0176] At step 514, the computing apparatus 140 retrieves envelope information from the envelope database 302. Based on the selected optimization goal, based on the identified (or target) model of the plurality of models 402 and the information stored in the envelope database 302, the computing apparatus 140 identified historical performance information. In some embodiments, during the step 514 the computing apparatus 140 may be configured to determine a set of historical optimization parameters when the molding system has been operating in accordance with the optimization goal. In some non-limiting embodiments of the present technology, the computing apparatus 140 retrieves information in regard to a “benchmark machine” - i.e., an other injection molding machine that has operated at or near the optimized parameters.

[0177] In the illustrated embodiments, the computing apparatus 140 determines that:

[0178] • The power is high during the fill function

[0179] • The fill rate is at a high end • Fill time is below average

[0180] At step 516, the computing apparatus 140 accesses the tools and action database 304 to look up the toolbox. Based on the parameters determined at step 514, the computing apparatus 140 selects an appropriate tool in the first model 404 - in this example, it is the lowering of the accumulator charge (T2b) to achieve lower power consumption (Gib). In some embodiments, the selection of an appropriate tool in the first model 404 may be based on the set of historical optimization parameters determined at step 514.

[0181] At step 518, the computing apparatus 140 accesses the impact of the various tools available within the tools and action database 304. Recalling that the tools and action database 400 includes an indication of the interdependency 412, the computing apparatus 140 determines the following impact in this example:

[0182] • Lowering the accumulator charge will lower the fill time, thus will have an impact on the cycle time

[0183] • And will additionally result in the deficit of the clamp tonnage

[0184] At step 520, the computing apparatus 140 selects a target tool from the plurality of tools available for the given model. In the illustrated embodiment, the computing apparatus 140 selects the lowering accumulator charge (T2b) as the applicable tool to use for optimization.

[0185] At step 522, the computing apparatus 140, using the target optimization model, iteratively optimizes the operational parameter until a pre-determined result (also called an “envelope”) is achieved. In some non-limiting embodiments of the present technology, the optimization can be done by modelling the behavior of the injection molding machine 100. In other non-limiting embodiments of the present technology, the optimization can be done by running the injection molding machine 100 using a newly set parameter and observing feedback - i.e., new performance parameters of the injection molding machine 100.

[0186] At step 524, the computing apparatus 140, checks if the envelope has been reached. In some nonlimiting embodiments of the present technology, the computing apparatus 140 checks if by optimizing the given operational parameters, the performance of the injection molding machine 100 is pushed beyond the acceptable envelope on another operational parameter. In the illustrated embodiments and based on the independency 412, the computing apparatus 140 can verily if optimization of the power consumption at the current iteration has negatively impacted cycle time. If “NO”, then the computing apparatus 140, returns to step 522.

[0187] If “YES”, then the computing apparatus 140, proceeds to step 526, where the computing apparatus 140, generates a report and presents the report to the operator using the aforementioned HMI.

[0188] At step 528, the operator monitors progress of the optimization and intervenes through the aforementioned HMI, if necessary. For example, the operator can adjust the optimization goal, select a different optimization goal, abort the optimization, or decide that the optimization has been completed.

[0189] At step 530, the operator decides if the optimization goal has been achieved and if the optimization is acceptable. In some non-limiting embodiments of the present technology, this step involves checking if the pre-determined result has been achieved. In case of “YES”, the method 500 terminates at 550.

[0190] In case of “NO”, the method 500 proceeds to step 532, where the computing apparatus 140 determines if the envelope can be further pushed. In order to do so, the computing apparatus 140 may return to step 512, where the computing apparatus 140, a different model of the plurality of models 402 to attempt to optimize the operational parameters of the injection molding machine based on the original optimization goal (or another optimization goal).

[0191] In other words, in response to the optimization goal not meeting the pre-determined result during the iterative optimization of the target optimizing parameters, the computing apparatus 140 can select an other target model from the plurality of models 402 for optimizing other target operational parameters of at least one other target operational component.

[0192] It should be noted that even though step 530 has been described as performed on the user side 502, these steps can also be automated and implemented as part of the machine side 504, where the computing apparatus 140 checks the current performance in the optimization cycle against the predetermined result. For example, in the present example of optimizing energy consumption, the computing apparatus 140 can check that the energy use has been minimized without negatively impacting the cycle time (based on the interdependency 412).

[0193] In accordance with the non-limiting embodiments of the present technology, once the optimization goal has been reached, the computing apparatus 140 can then use the so-optimized operational parameters to control the injection molding system 200. In some non-limiting embodiments of the present technology, the injection molding machine 100 can be operated using the optimized operational parameter. In some implementations, the processor 140 can adjust the operational parameter(s) of the injection molding machine to be inline with the optimized operational parameter.

[0194] In some non-limiting embodiments of the present technology, adjusting operational parameters to be in-line with the optimized operational parameter can be executed with the use of active mold components (or other components of the injection molding machine 100) i.e., components that are adaptable and / or programmable. Examples of such active components are disclosed in co-owned patent applications bearing publication numbers WO 2005 / 102654 (published on November 3rd, 2005), W02005 / 102661 (published on November 3rd, 2005), W02005 / 102650 (published on November 3rd, 2005), W02005 / 102649 (published on November 3rd, 2005), W02010 / 121349 (published on October 28, 2010) and WO2011 / 054080 (published on May 12, 2011) the content of which is incorporated herein by reference in its entirety. It should be noted that at least some of the steps of the method 500 can overlap therebetween or be executed substantially at the same time.

[0195] It should be noted that in alternative non-limiting embodiments of the present technology, the active components that can be used for adjusting operational parameters may include but are not limited to: a molding component having an adaptable molding surface; an adjustable heat removal component and / or process; a dynamic gate orifice; and the like.

[0196] Additional non-limiting examples of optimization

[0197] In accordance with non-limiting embodiments of the present technology, the following operational parameters can be optimized.

[0198] • Power pack energy consumption optimized to the application

[0199] • Injection fill rate tailored to part geometry and features

[0200] • Injection start timed for minimal mold breath

[0201] • Hold and cooling time split for part weight and exit temperature

[0202] • Recovery rate and transfer speed reduced to minimum

[0203] • Barrel heats temperature adjusted for resin conditions • Operations during a start-up process

[0204] • Processing temperature of the molding machine 100 and / or upstream and downstream devices

[0205] • Resin consumption

[0206] In some embodiments of the present technology, during the start up process of the molding machine 100, it may be desirable to optimize performance of the molding machine 100 (for example to slow it down or the like). This can be a tedious process that is typically executed by an operator of the molding system 200. In accordance with some non-limiting embodiments of the present technology, the processor 140 can be configured to automatically adjust operation of the molding system 200 to a reduced rate to preclude the need to vent start and stop, currently done manually. This can lead directly or indirectly to an optimized energy consumption, optimized operator time use, as well as elimination of manual steps that are susceptible to operator error and inefficiencies.

[0207] In some embodiments of the present technology, the controller 140 may be configured to implement a bidirectional communication from upstream / downstream devices (not depicted). As such, in some embodiments of the present technology, the controller 140 may be configured to optimize performance of the molding machine 100 during an unplanned machine stoppage, which optimization may include transmitting a command to an upstream dryer to control the dryer to a different temperature setpoint to avoid ’’cooking” the material.

[0208] In some embodiments of the present technology, the optimization of the resin consumption can be based on a molded article weight optimization. In some of these embodiments, an inspection (manual or automatic) may include weighing of the molded articles. When optimizing for resin consumption, the controller 140 can be configured to optimize the part weight distribution to allow for part weight reduction i.e., make molded article more consistent in weight distribution or amongst themselves.

[0209] In some embodiments of the present technology, the controller 140 can optimize a maintenance cycle of the molding system 200. This can have a technical effect of improved efficiency, at least a lower downtime occurrence, and improved timing when scheduled maintenance occurs (i.e., planning maintenance of multiple service points on the same occasion (in parallel) as opposed to servicing them piecemeal). In other words, the controller 140 can optimize the performance of the molding system 200 so that some parts will synchronize with others in terms of life (serviceable life).

[0210] For example,

[0211] • It may so happen that at a given time for the molding system 200, in response to determining that there are 3 month till service on the valves and 1 month for a motor, the controller 140 can execute an optimization routine that slows down the motor so that to compensate for the gap and to allow these service points to synchronize (at least in part).

[0212] • If the molding system 200 has a busy season (production commitment) and in 2 months it is going to be low season, the controller 140 can optimize performance such that to “stretch out” the equipment to last two months more reliably versus finding out in a month that you are going to be replacing something.

[0213] In some embodiments of the present technology, the controller 140 can optimize an overall performance the molding system 200, where a given molding system 200 may be run at lower performance due to an infrastructure constraint. Examples of such infrastructure constraints include but not limited to lack of mold cooling or tower water cooling. The former would extend cycle time cooling to maintain part quality and the latter could result in a slower cycle to limit pump motor power to match tower water cooling capacity.

[0214] In a typical molding system 200, both of these services have sensor (thermocouple) feedback and these signals could be the supervisory, i.e., “do not exceed”, limits to control the overall system performance. This signal feedback could be used for optimizing the performance of the molding system 200 and determining a “cycle time equilibrium”.

[0215] In some non-limiting embodiments of the present technology the controller 140 can be further configured to execute polymer optimization and / or chemical chain optimization. That is, optimize the molding process to minimize degradation of the molding material e.g., reductions in molecular weight, intrinsic viscosity, formation of harmful end groups. In these embodiments of the present technology, the controller 140 can receive an indication of a parameter of the resin used (for example, one or more of: an indication of the content of recycled polymer; intrinsic viscosity of the resin, indications of molding byproducts such as harmful end groups). This information can be obtained, for example, from one or more of Infrared Spectroscopy, Gas Chromatography - Flame Ionisation (GC-FID), Gas Chromatography Mass Spectrometry (GC-MS) or Reversed Phase High Performance Liquid Chromatography (RP HPLC). The indication may include a detailed identification and quantification of one or more parameters of the resin, e.g., impurities, or it may provide a more general indication e.g., degradation of the resin beyond a predefined limit. Based on this information, the controller 140 can be configured to optimize one or more operational parameters of the molding system 200 or other downstream or upstream equipment. For example, the controller 140 can then adapt the resin plasticizing based on the indication of the parameter of the resin. For example, the controller 140 can optimize the rotational speed of the plasticizing screw. Additionally or alternatively, the controller 140 can optimize the temperature to which the polymers are heated to during plasticizing. Additionally or alternatively, the controller 140 can optimize the amount of AA scavengers added.

[0216] Non-limiting examples of the envelope information

[0217] With reference to FIG. 6, there is depicted an example of envelope information 602 stored in the envelope database 302.

[0218] In the illustrated embodiment, the envelope information 602 is representative of a fill rate. As has been alluded to above, the envelope information 602 can be generated by the manufacturer of the injection molding machine 100 by observing one or more injection molding machines during testing or operations at a customer site.

[0219] The envelope information 602 depicts in “inside the envelope” section 604 and “outside the envelope” section 606. The “inside the envelope” section 604 represents an acceptable range of the operational parameter (in this case the fill rate). The “outside the envelope” section 606 represents an un-acceptable range of the operational parameter (in this case the fill rate).

[0220] In other words, the aforementioned reference to the “check the envelope” is meant to denote whether the current value of the associated operational parameter falls within one of inside the envelope” section 604 and “outside the envelope” section 606.

[0221] Modifications and improvements to the above-described embodiments of the present technology may become apparent to those skilled in the art. The foregoing description is intended to be exemplary rather than limiting. The scope of the present technology is therefore intended to be limited solely by the scope of the appended claims.

[0222] The description of the embodiments of the present technology provides only examples of the present technology, and these examples do not limit the scope of the present technology. It is to be expressly understood that the scope of the present technology is limited by the claims only. The concepts described above may be adapted for specific conditions and / or functions and may be further extended to a variety of other applications that are within the scope of the present technology. Having thus described the embodiments of the present technology, it will be apparent that modifications and enhancements are possible without departing from the concepts as described.

Claims

CLAIMS1. A method of operating a molding system, the molding system having a plurality of operational components and a controller, the controller configured to control at least some of the plurality of operational components, the method executable by the controller and comprising: receiving an indication of an operational parameter to be optimized, the operational parameter selectable from a plurality of operational parameters of the plurality of operational components; based on the operational parameter to be optimized, selecting a target optimization model from a plurality of optimization models; using the target optimization model, iteratively optimizing the target operational parameter until a pre-determined result is achieved.

2. The method of claim 1, wherein the target optimization model further includes an indication of the pre-determined result.

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

4. The method of claim 1, wherein the plurality optimization models are organized in a hierarchical structure such that a first one from the plurality optimization models is ranked higher than a second one from the plurality of optimization models in the hierarchical structure based on an economic criterion.

5. The method of claim 1, wherein the target optimization model is a target sub-group of optimization models from the plurality of optimization models associated with a sub-group of operational parameters from the plurality of operational parameters, and wherein the using the target sub-group of optimization models includes: iteratively and concurrently optimizing the sub-group of target operational parameters until the pre-determined result is achieved.

6. The method of claim 1, wherein the plurality operational components include a blowmolding machine and a molding machine, the blow-molding machine being disposed downstream from the molding machine, the indication of the operational parameter being received from the blow-molding machine.

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

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

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

10. The method of claim 9, wherein the given model is for indicating an interdependency of an operational parameter of a first operational components with an operational parameter of a second operational component.

11. The method of claim 10, wherein the given model is for enabling the controller to assess impact of the operational parameter of the first operational components and the operational parameter of the second operational component during optimization.

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

13. The method of claim 9, wherein the given model of the plurality of models further indicates:(ii) operational parameters' sensitivity to change for achieving the operational goal.

14. The method of claim 8, wherein: in response to the optimization goal not meeting the pre-determined result during the iterative optimization of the target optimizing parameters, selecting an other target model from the plurality of models for optimizing other target operational parameters of at least one other target operational component.

15. The method of claim 8, wherein the method further comprises receiving an operator input on the pre-determined results prior to using the target operational parameter to control the molding system.

16. The method of claim 8, wherein the at least one other target operational component of the other target model is different from the at least one target operational component of the target model.

17. The method of claim 8, wherein the operational parameters comprise at least one of a pump speed, accumulator charge, a fdl rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock.

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

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

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

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

22. The method of claim 8, wherein the operational goal is an optimized maintenance schedule.

23. The method of claim 8, wherein the operational goal is an optimized part weight.

24. The method of claim 8, wherein the operational goal is an optimized resin consumption.

25. The method of claim 8, wherein the operational goal is an optimized resin plasticizing.

26. The method of claim 25, wherein before the iteratively optimizing the target operational parameters, the method further comprising receiving an indication of a current operating parameter of the target operational parameters.

27. The method of claim 26, wherein the receiving the indication of a current operating parameter of the target operational parameters 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 comprises an indication of a content of the polymer.

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

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

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

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

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

34. The molding system of claim 31, wherein the plurality optimization models are organized in a hierarchical structure such that a first one from the plurality optimization models is ranked higher than a second one from the plurality of optimization models in the hierarchical structure based on an economic criterion.

35. The molding system of claim 31, wherein the target optimization model is a target subgroup of optimization models from the plurality of optimization models associated with a sub-group of operational parameters from the plurality of operational parameters, and wherein by using the target sub-group of optimization models the molding system is configured to: iteratively and concurrently optimize the sub-group of target operational parameters until the pre-determined result is achieved.

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

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

38. A molding system having a plurality of operational components, and one or more controllers configured to: receive an indication of an operational goal 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 operational components; based on the operational goal, select a target model from the plurality of models, the target model for optimizing target operational parameters of at least one target operational component from the plurality of operational component; by using the target model, iteratively optimize the target operational parameters; wherein iteratively optimizing includes checking the target operational parameters of the at least one target operational component against a pre-determined result; and in response to the pre-determined result being satisfied, use the target operational parameters to control the molding system.

39. The molding system of claim 38, wherein: a given model of the plurality of models for indicating:(i) operational parameters of the respective operational components associated with a given one of the plurality of operational envelope.

40. The molding system of claim 38, wherein the given model is for indicating an interdependency of an operational parameter of a first operational components with an operational parameter of a second operational component.

41. The molding system of claim 40, wherein the given model is for enabling the controller to assess impact of the operational parameter of the first operational components and the operational parameter of the second operational component during optimization.

42. The molding system of claim 40, wherein the interdependence 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) operational parameters' sensitivity to change for achieving the operational goal.

44. The molding system of claim 38, wherein the one or more controllers are further configured to:in response to the optimization goal not meeting the pre-determined result during the iterative optimization of the target optimizing parameters, select an other target model from the plurality of models for optimizing other target operational parameters of at least one other target operational component.

45. The molding system of claim 38, wherein the one or more controllers are further configured to receive an operator input on the pre-determined results prior to using the target operational parameter to control the molding system.

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

47. The molding system of claim 38, wherein the operational parameters comprise at least one of a pump speed, accumulator charge, a fill rate, a hold time, a cooling time, a transfer speed, an injection start, a clamp tonnage, and a tonnage lock.

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

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

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

51. The molding system of claim 38, wherein the operational goal is a lowest production rate for a pre-determined production requirement.

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

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

54. The molding system of claim 38, wherein the operational goal is an 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 is further configured to receivean indication of a current value of the operational parameter from the inspection system; the current value being used for triggering a process to iteratively optimize the target operational parameters.

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