Injection molding techniques and optimizations therefor

Abstract

. Optimization techniques for injection molding are described. Systems of this disclosure are configured to form a golden reference curve that shows parameter value ranges associated with producing injection molding product of at least a threshold quality. In one example, a system includes an injection molding apparatus and a computing device communicatively coupled to the injection molding apparatus. The injection molding apparatus includes a nozzle, and sensor hardware disposed inside the nozzle. The computing device is configured to receive, from the injection molding apparatus, over multiple cycles associated with production of acceptable injection molding products, measurement data reflecting a parameter associated with the nozzle and measured by the sensor hardware. The computing device is further configured to form a reference cycle with respect to the parameter based on the measurement data received over the multiple cycles.

Description

PA102572W002INJECTION MOLDING TECHNIQUES AND OPTIMIZATIONS THEREFORTECHNICAL FIELD

[0001] This disclosure generally relates to the technical field of injection molding.BACKGROUND

[0002] Injection molding is a manufacturing process for producing parts by injecting molten material into a mold. Injection molding can be performed with a host of materials, including metals (for which the process is called die-casting), glasses, elastomers, confections, and most commonly, thermoplastic and thermosetting polymers. Material for the part is fed into a heated barrel, mixed (using a helical screw), and injected into a mold cavity, where it cools and hardens to the configuration of the cavity. After a product is designed, molds are made by a mold-maker (or toolmaker) from metal (e.g., steel or aluminum), and precision- machined to form the features of the desired part. Injection molding is widely used for manufacturing a variety of parts, from the smallest components to entire body panels of cars. Advances in additive manufacturing 3D printing technology7, using photopolymers that do not melt during the injection molding of some lower-temperature thermoplastics, can be used for some injection molds.

[0003] Injection molding uses a special -purpose machine that has three parts, namely, the injection unit, the mold, and the clamp. Parts to be injection-molded must be very7carefully designed to facilitate the molding process. The material used for the part, the desired shape and features of the part, the material of the mold, and the properties of the molding machine may all be taken into account. The versatility of injection molding is facilitated by this breadth of design considerations and possibilities.SUMMARY

[0004] In injection molding, process temperature control is controlled indirectly via heater-bands and thermocouples measuring the steel temperature. These techniques do not give actual melt temperature, which leads to variability in molded part aesthetics and performance. Day- to-day fluctuations in melt temperature are undiscemed and may be a root cause in the process variation. After start-up. there is no direct indication when the process is stabilized.

[0005] Injection molding involves the balance of four primary variables, namely, melt temperature, melt pressure, melt shear rate, and melt cooling rate. Numerous process inputs affect these primary7variables in complex ways. Visibility and control over these inputs are critical for process consistency and repeatability. Additionally, current molding processesare limited in the ability to view, control, and compare data from environment to environment. Such environment changes may include one or more of (i) different day, (ii) different press, (iii) different location, and / or (iv) raw material variations. Challenges include data input standardization, data comparisons (e.g.. featurization), and correlation of data outputs to machine inputs & environment change.

[0006] In one example, this disclosure is directed to a system. The system includes an injection molding apparatus and a computing device communicatively coupled to the injection molding apparatus. The injection molding apparatus includes a nozzle, and sensor hardware disposed inside the nozzle. The computing device is configured to receive, from the injection molding apparatus, over multiple cycles associated with production of acceptable injection molding products, measurement data reflecting a parameter associated with the nozzle and measured by the sensor hardware. The computing device is further configured to form a reference cycle with respect to the parameter based on the measurement data received over the multiple cycles.

[0007] Aspects of this disclosure are directed to designs that alleviate the melt-temperature- related problems described above by incorporating a sensor that physically contacts the plastic melt. The sensor is positioned in the nozzle-body of the machine. The machine includes a feedback loop to track and record melt temperature (and pressure) of the resin in the nozzle-body. Access to the direct, resin melt temperature (and pressure) enables users of the systems of this disclosure to monitor process progress to steady state. A confirmed steady-state operation reduces the molding risk during sampling, e.g., reduces stuck-part problems, underfilled regions, and / or tool fouling. More specifically, a confirmed steadystate operation delivers these performance improvements by ensuring that there is a stable melt before parts are molded.

[0008] Additional aspects of this disclosure are directed to computing systems configured to alleviate the data-related deficiencies described above. These computing systems of this disclosure execute software and run analytics to improve ease and accuracy of cycle comparisons and process parameter tuning. For instance, the computing systems may extract key features (e.g. maximum pressure, hold pressure, time to reach maximum pressure, etc.) and run comparisons to show the difference (absolute or relative) between corresponding data points. Additionally, the computing systems may calculate a mean square error of the whole pressure curve and golden reference curve. The computing systems may add upper and lower bounds in control charts. The computing systems may form surrogate models for efficient process parameter tuning. The computing systems may determine a match value (e.g. in the form of a percentage) and identify it by the corresponding cycle number.BRIEF DESCRIPTION OF DRAWINGS

[0009] FIG. 1 is a set of plots showing a reference cycle and real-time measurements captured by in-nozzle sensors with respect to temperature and pressure.

[0010] FIG. 2 is a graph showing a reference cycle in comparison to plots of pressure subsequently captured by in-nozzle sensors according to this disclosure.

[0011] FIG. 3 is a set of plots showing maximum pressure values and predictions.

[0012] FIG. 4 is a set of plots showing a time to reach a maximum pressure and predictions thereof.

[0013] FIGS. 5A & 5B are a set of plots showing models for different materials.

[0014] FIG. 6 is a block diagram illustrating an example system of this disclosure.

[0015] FIG. 7 illustrates examples of an injection unit, a nozzle body, and a tool of one or more example implementations of the system shown in FIG. 6.

[0016] FIG. 8 is a flowchart illustrating an example process of this disclosure.

[0017] FIG. 9 illustrates graphs of first shot pressure and first shot temperature as measured by the data collection system of this disclosure.DETAILED DESCRIPTION

[0018] The system designs and techniques of this disclosure create a standard, qualified process in combination with the use of one or more sensors. The sensor(s) may be disposed in a variety of locations, such as in the nozzle-body, in the tool, in other auxiliary equipment, etc. The systems and techniques of this disclosure may leverage the sensor(s) to capture one or more classes of processing data to ensure identical parts on a cycle-to-cycle basis.

[0019] Identically molded parts are achievable, from various molding machines using the same tool, due to transference of the process data. The process data captures the machine outputs and are matched to what was molded in a previously used machine. Upper and lower limit bounds are included to determine the tolerance of noise in the experimental data. Such noise may be caused by environmental conditions and / or material fluctuations.

[0020] Maintenance prompts are indicated by observing data variance (e.g. between resin pressure and machine melt pressure observations). This data variance is detected using data analysis tools of this disclosure. The systems and techniques of this disclosure leverage the captured process data to confirm proper functioning of all connected auxiliary molding equipment (e.g. valve-gate, 2-shot molding, dryers, hopper position, mold water temp controllers, among others).

[0021] Code generation software of this disclosure captures all process data into a 2D barcode (QR) that can be packaged with and travel with the molded part package. Systems andtechniques of this disclosure may use one or more simulations of the molding to create an initial process reference curve from the initial guess of the process parameters. This simulated curve is a result of data analytics aspects of this disclosure.

[0022] The systems and techniques of this disclosure provide various technical improvements in the technical field of injection molding. As one example, the systems and techniques of this disclosure provide, in many instances, an identical process transfer between machines, thereby improving data precision and also reducing resource wastage in the form of unnecessary reconfiguration and parameter tuning. As another example, the systems and techniques of this disclosure provide faster scale-up with respect to manufactured part(s). thereby improving machine efficiency in the form of enhanced throughput. As another example, the systems and techniques of this disclosure improve data precision and the resources and time taken to provide an understanding of parameters and condition information, thereby resulting in fewer real experiments needed to be run to understand input parameters to part output conditions.

[0023] As another example, the systems and techniques of this disclosure enhance data precision by delivering increased and improved visibility on process variation, thereby also reducing machine downtime to improve throughput. Additionally, the systems and techniques of this disclosure may automate part quality control decisions based on process data outputs, thereby- improving data precision by reducing the scope for human error. Additionally, the systems and techniques of this disclosure reduce resource wastage and unnecessary machine downtime by enabling the implementation of preventive maintenance on the machine in response to process data-based prompting instead of routine time stamps.

[0024] FIG. 1 is a set of plots showing a reference cycle and real-time measurements captured by in-nozzle sensors with respect to temperature and pressure. A “golden reference curve” is shown on the right, while real-time plots of pressure (top left) and temperature (bottom left) are shown on the left. As used herein, a “golden reference curve” or a “reference cycle” refers to a reference range for a parameter that the systems of this disclosure determine as being associated with the injection molding-based production of an acceptable product. Examples of parameters for which the systems of this disclosure may determine reference cycles include temperature and / or pressure.

[0025] For instance, a system of this disclosure may include, be, or be part of an injection molding apparatus. In turn, the injection molding apparatus of this disclosure may include a nozzle and sensor hardware disposed inside the nozzle. The system may further include a computing device communicatively coupled to the injection molding apparatus. In this example, the computing device may be configured to receive, from the injection moldingapparatus, real-time or near-real-time measurement data reflecting pressure and / or temperature associated with the nozzle. By leveraging a sensor hardware placement inside the nozzle, the computing device may use real-time or near-real-time pressure and / or temperature measurements to associate acceptable product molding with corresponding innozzle conditions.

[0026] For instance, the computing devices of this disclosure may preselect all temperature measurements (as collected from the in-nozzle sensor hardware) that were collected during the manufacturing cycle of a product that was later deemed to be of acceptable quality'. In cases where the preselected temperature measurements show variation, the computing device of this disclosure may form a range, also referred to herein as the "reference cycle” or “golden reference curve” with respect to in-nozzle temperature. That is, the systems of this disclosure may use multi-cycle data collection from the in-nozzle sensor hardware to form a range of in-nozzle temperature measurements at which the injection molding apparatus produced an acceptable product.

[0027] By using the multi-cycle data collection techniques of this disclosure, the systems of this disclosure provide the technical improvement of enhanced data precision. For instance, by forming a reference cycle using potentially varying temperature measurements that all produced acceptable products and that occurred during different injection molding cycles, the systems of this disclosure leverage a range (potentially the entire range) of in-nozzle temperature data points that were measured during the production of a product that was deemed to be of acceptable quality. In turn, by forming a reference cycle reflecting a potentially broad range of in-nozzle temperature measurements, the systems of this disclosure may enable the pre-selection of acceptable in-nozzle temperature measurements for injection molding-based products while accommodating variability in production conditions such as in-nozzle temperature.

[0028] In other examples, the computing devices of this disclosure may preselect all pressure measurements (as collected from the in-nozzle sensor hardware) that were collected during the manufacturing cycle of a product that was later deemed to be of acceptable quality. In cases where the preselected pressure measurements show variation, the computing device of this disclosure may form a range, also referred to herein as the “reference cycle” or “golden reference curve” with respect to in-nozzle pressure. That is. the systems of this disclosure may use multi-cycle data collection from the in-nozzle sensor hardware to form a range of innozzle pressure measurements at which the injection molding apparatus produced an acceptable product.

[0029] By using the multi-cycle data collection techniques of this disclosure, the systems of this disclosure provide the technical improvement of enhanced data precision. For instance, by forming a reference cycle using potentially varying in-nozzle pressure measurements that all produced acceptable products and that occurred during different injection molding cycles, the systems of this disclosure leverage a range (potentially the entire range) of in-nozzle pressure data points that were measured during the production of a product that was deemed to be of acceptable quality. In turn, by forming a reference cycle reflecting a potentially broad range of in-nozzle pressure measurements, the systems of this disclosure may enable the preselection of acceptable in-nozzle pressure measurements for injection molding-based products while accommodating variability in production conditions such as in-nozzle pressure.

[0030] It will be appreciated that “sensor hardware’' as used herein may represent various configurations of one or more sensors. That is, the in-nozzle sensor hardware described herein may. in various use case scenarios, represent a single sensor, or multiple sensors. In any event, the placement of the sensor hardware within the nozzle may be implemented in various ways, depending on nozzle body construction, melt composition, or other factors.

[0031] The systems of this disclosure may use the golden reference curve or reference cycle determined for in-nozzle parameters for pre-determination of product quality on a per-cycle basis in production. In a temperature-based example, the systems of this disclosure may fit the in-nozzle temperature data received from the sensor hardware against the reference cycle. For instance, in a production cycle, the systems of this disclosure may determine whether one or more of the in-nozzle temperature data points fit within the reference cycle determined for that particular combination of injection molding equipment and melt composition. If a particular in-nozzle temperature measurement falls within the corresponding reference cycle, the systems of this disclosure may detect a “fit” with respect to the temperature, and may determine, based on the measured in-nozzle temperature, that the product will be of acceptable quality. Conversely, if the in-nozzle temperature measurement falls outside of the corresponding reference cycle, the systems of this disclosure may determine that the temperature is not within the acceptable range for a production cycle, and may determine, based on the measured in-nozzle temperature, that the product will not be of acceptable quality. In some instances, the systems of this disclosure may output a “discard product” indication in the latter scenario in which the temperature is not a fit with respect to the corresponding reference cycle.

[0032] In a pressure-based example, the systems of this disclosure may fit the in-nozzle pressure data received from the sensor hardware against the corresponding reference cycle. Forinstance, in a production cycle, the systems of this disclosure may determine whether one or more of the in-nozzle pressure data points fit within the reference cycle determined for that particular combination of injection molding equipment and melt composition. If a particular in-nozzle pressure measurement falls within the corresponding reference cycle, the systems of this disclosure may detect a ’Tit" with respect to the pressure, and may determine, based on the measured in-nozzle pressure, that the product will be of acceptable quality.Conversely, if the in-nozzle pressure measurement falls outside of the corresponding reference cycle, the systems of this disclosure may determine that the pressure is not within the acceptable range for a production cycle, and may determine, based on the measured innozzle pressure, that the product will not be of acceptable quality. In some instances, the systems of this disclosure may output a ‘’discard product” indication in the latter scenario in which the pressure is not a fit with respect to the corresponding reference cycle.

[0033] In this manner, the techniques of this disclosure enable injection molding systems to enhance product quality in various ways. As one example, the systems of this disclosure may use feedback data received from the in-nozzle sensor hardware in a preemptive manner by detecting “outside boundary" situations with respect to the reference cycle with respect to the measured parameter. In these preemptive scenarios, the systems of this disclosure may effectuate a change in the injection molding conditions, such as by making changes that bring the temperature or pressure to within the corresponding reference cycle, thereby “correcting course” with respect to the present production cycle. In other scenarios, as discussed above, the systems of this disclosure may output a “discard product” signal if the parameter tracked using the in-nozzle sensor hardware falls outside of the reference cycle boundary. In these scenarios, an unsatisfactory or potentially unsatisfactory injection-molded product is flagged for being discarded, thereby avoiding the deployment of an unsatisfactory product. By remediating any occurrences of unsatisfactory product pre-detected using any of the methods listed above, and limiting the output products to those coming out of production cycles in which the measured parameters fall within the corresponding reference cycle, the systems of this disclosure improve production precision and prevent back-end processes that would otherwise be necessary to winnow down products based on quality control checks.

[0034] FIG. 2 is a graph showing a reference cycle in comparison to plots of pressure subsequently captured by in-nozzle sensor hardware according to aspects of this disclosure. The reference cycle or golden reference curve of FIG. 2 is described above with respect FIG. 1.

[0035] FIG. 3 is a set of plots showing maximum pressure values and predictions. The predictions for the maximum pressure are generated with a constant melt temperature value.A simulation of molding is used to generate an initial golden reference curve. Various data analysis tools may be used to generate the molding simulations, such as Moldex3D software and / or machine learning models trained using Python.

[0036] FIG. 4 is a set of plots showing a time to reach a maximum pressure and predictions thereof. The plots illustrated in FIG. 4 may also be generated using data tools such as those described above with respect to FIG. 3.

[0037] FIGS. 5A & 5B are a set of plots showing models for different materials. Examples of melt materials include, but are not limited to, polycarbonate (PC), Acry lonitrile Butadiene Styrene (ABS). polyamide 66 (PA66), polypropylene (PP), and others.

[0038] FIG. 6 is a block diagram illustrating an example system of this disclosure. The data collection system shown in FIG. 6 is configured to implement the reference cycle (or golden reference curve) calculation techniques of this disclosure. Additionally, the data collection system of FIG. 6 is configured to fit production cycle data (e.g., temperature and / or pressure parameter data) against the corresponding golden reference curve to determine whether the production cycle product is of acceptable quality or of unacceptable quality.

[0039] FIG. 7 illustrates examples of an injection unit, a nozzle body, and a tool of one or more example implementations of the system shown in FIG. 6. Each of the components shown in FIG. 7 is an example location for sensor placement with respect to both reference cycle calculation and production cycle evaluation according to various techniques of this disclosure.

[0040] FIG. 8 is a flowchart illustrating an example process of this disclosure. The process of FIG. 8 may begin with hardware and software installation. In turn, data tags and historian setup may be implemented. The data collection system may cycle the process and collect parameter data (e.g., temperature and / or pressure data). The data collection system may evaluate the resultant product(s) and correlate the corresponding process data to the evaluations of the produced product. Using the parameter measurements associated with cycles that produced an acceptable product, the data collection system may calculate the reference cycle or golden reference curve (GRC), also referred to as a golden reference cycle.

[0041] FIG. 9 illustrates graphs of first shot pressure and first shot temperature as measured by the data collection system of this disclosure. With respect to production cycle pressure measurements, FIG. 9 illustrates the percentage match between the pressure measured during particular production cycles (identified by cycle number) within the corresponding GRC.

[0042] The data collection system may overlay subsequent (e.g. production) cycles on the GRC. Using the overlay, the data collection system may calculate a percentage match and / or other analytic data comparing the production cycle parameter value(s) to the corresponding GRC.The system of FIG. 6 may output, for display, the analytics graphically. By outputting the analytics information graphically, the systems of this disclosure may enable a technician or other human in the loop to determine whether the production cycle is likely to produce an acceptable product or an unacceptable product, in terms of product quality.

[0043] In the present detailed description of the example embodiments, reference is made to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. The illustrated examples are not intended to be exhaustive of all embodiments according to the invention. It is to be understood that other embodiments may be utilized, and structural or logical changes may be made without departing from the scope of the present invention. The detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0044] Unless otherwise indicated, all numbers expressing feature sizes, amounts, and physical properties used in the specification and claims are to be understood as being modified in all instances by the term "about” or “approximately” or “substantially.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification and attached claims are approximations that can vary7depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein.

[0045] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” encompass embodiments having plural referents, unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and / or” unless the content clearly dictates otherwise.

[0046] It is to be recognized that depending on the example, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

[0047] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, CPUs, GPUs, DSPs, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), processing circuitry (e.g., fixed function circuitry, programmable circuitry, or any combination of fixed function circuitry and programmable circuitry ), or any other equivalent integrated or discrete logic circuitry7, as well as any combinations of such components. The term “processor” or “processing circuitry” maygenerally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit comprising hardware may also perform one or more of the techniques of this disclosure.

[0048] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various operations and functions described in this disclosure. In addition, any of the described units, modules or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components or integrated within common or separate hardware or software components.

[0049] The techniques described in this disclosure may also be embodied or encoded in a computer-readable medium, such as a computer-readable storage medium, containing instructions. Instructions embedded or encoded in a computer-readable storage medium may cause a programmable processor, or other processor, to perform the method, e.g., when the instructions are executed. Computer readable storage media may include random access memory’ (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory' (EPROM), electronically erasable programmable read only memory (EEPROM), flash memory, a hard disk, a CD-ROM, a floppy disk, a cassette, magnetic media, optical media, or other computer-readable media.

[0050] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A system comprising: an injection molding apparatus comprising: a nozzle; and sensor hardware disposed inside the nozzle; and a computing device communicatively coupled to the injection molding apparatus, tire computing device being configured to: receive, from the injection molding apparatus, over multiple cycles associated with production of acceptable injection molding products, measurement data reflecting a parameter associated with tire nozzle and measured by tire sensor hardware; and form a reference cycle with respect to the parameter based on the measurement data received over the multiple cycles.

2. The system of claim 1, wherein the reference cycle represents a range of the parameter associated with the production of the acceptable injection molding products.

3. The system of claim 1, wherein the parameter represents temperature data measured by the sensor hardware.

4. The system of claim 1 , wherein the parameter represents pressure data measured by the sensor hardware.

5. The system of claim 1, wherein the computing device is further configured to: receive, from the injection molding apparatus, for a production cycle of an injection molding product, measurement data reflecting the parameter associated with the nozzle and measured by the sensor hardware during the production cycle; and fit the measurement data reflecting tire parameter measured by the sensor hardware during the production cycle against the reference cycle with respect to the parameter.

6. The system of claim 5, wherein to fit the measurement data reflecting the parameter measured by tire sensor hardware during the production cycle against the reference cycle with respect to the parameter, the computing device is configured to calculate a percentage match between the measurement data reflecting the parameter and the reference cycle with respect to the parameter.

7. The system of claim 1, wherein the computing device is further configured to: determine, based on the fit of the measurement data reflecting the parameter measured by the sensor hardware during the production cycle against the reference cycle with respect to the parameter, that the measurement data reflecting the parameter measured by tire sensor hardware during the production cycle falls within tire reference cycle with respect to the parameter.

8. The system of claim 7, wherein the computing device is further configured to: determine, based on the parameter measured by the sensor hardware during the production cycle falling within the reference cycle with respect to the parameter, that an injection- molded product produced during the production cycle is of an acceptable quality.

9. The system of claim 8, wherein the computing device is further configured to: output an indication, via interface hardware, of the acceptable quality associated with the injection-molded product produced during the production cycle.

10. The system of claim 9, wherein to output the indication, the computing device is configured to output the indication prior to a completion of the production cycle.

11. The system of claim 1, wherein the computing device is further configured to: determine, based on the fit of the measurement data reflecting the parameter measured by tire sensor hardware during the production cycle against the reference cycle with respect to the parameter, that the measurement data reflecting the parameter measured by the sensor hardware during the production cycle falls outside of the reference cycle with respect to the parameter.

12. The system of claim 11, wherein the computing device is further configured to: determine, based on tire parameter measured by the sensor hardware during the production cycle falling outside of tire reference cycle with respect to the parameter, that an injection- molded product produced during the production cycle is of an unacceptable quality.

13. The system of claim 12, wherein the computing device is further configured to: output an indication, via interface hardware, of the unacceptable quality associated with the injection-molded product produced during the production cycle.

14. The system of claim 13, wherein to output the indication, tire computing device is configuredto output the indication prior to a completion of the production cycle.

15. The system of claim 13, wherein the indication comprises a ‘'discard product” notification.

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