CN115545367A - Abnormality monitoring method in injection molding process, electronic device, and storage medium - Google Patents
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Abstract
The application provides an abnormity monitoring method, electronic equipment and a storage medium in an injection molding process, wherein the method comprises the following steps: acquiring data of a plurality of parts in the injection molding process; calling an abnormality monitoring model trained in advance to perform abnormality analysis on the data of each component to obtain a process risk value and at least one influence parameter of each component; judging whether the corresponding part is abnormal or not according to the process risk value; and when the target component is determined to have the abnormality according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component. Through the application, the abnormal conditions in the forming process can be found in time, so that the reject ratio of the formed product is reduced.
Description
Technical Field
The present disclosure relates to the field of data analysis, and more particularly, to an anomaly monitoring method in an injection molding process, an electronic device, and a storage medium.
Background
The quality monitoring of injection molding is currently carried out by checking the quality of the finished product. For the injection molding production field of continuous production, quality supervision personnel check the quality of finished products in a regular inspection or random sampling inspection mode. However, the period from the production of the product to the detection of the abnormal product is usually longer in this way, and more waste products may be generated in the period. In addition, quality supervision personnel can inform relevant personnel to process after finding that the finished product quality is abnormal, and the process needs to consume a large amount of time and cannot feed back information in time. Meanwhile, related personnel need to process according to self experience, and the processing process depends on personnel capacity seriously and has great uncontrollable property.
Disclosure of Invention
In view of the above, it is desirable to provide an abnormality monitoring method, an electronic device and a storage medium for an injection molding process, which can detect an abnormal condition in the molding process in time, thereby reducing a defective rate of a molded product.
The application provides an abnormity monitoring method for an injection molding process, which comprises the following steps: acquiring data of a plurality of parts in the injection molding process; calling an abnormality monitoring model which is trained in advance to perform abnormality analysis on the data of each component to obtain a process risk value and at least one influence parameter of each component; judging whether the corresponding part is abnormal or not according to the process risk value; and when the target component is determined to have the abnormality according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component.
In a possible implementation manner, the acquiring data of a plurality of components in the injection molding process includes: acquiring controller data of the injection molding machine, first sensor data of the mold and second sensor data of the mold temperature controller.
In one possible implementation manner, after acquiring the controller data of the injection molding machine, the first sensor data of the mold, and the second sensor data of the mold temperature controller, the method further includes: and segmenting the controller data according to the workshop section information in the controller data to obtain a plurality of workshop section data, wherein different workshop section data correspond to different injection molding processes.
In a possible implementation manner, the invoking of the pre-trained anomaly monitoring model to perform anomaly analysis on the data of each component to obtain the process risk value and the at least one influence parameter of each component includes: calling an abnormality monitoring model which is trained in advance to perform abnormality analysis on controller data of the injection molding machine to obtain a first process risk value and at least one first influence parameter; calling a pre-trained abnormity monitoring model to perform abnormity analysis on the first sensor data of the mold to obtain a second process risk value and at least one second influence parameter; and calling a pre-trained abnormity monitoring model to perform abnormity analysis on the second sensor data of the mold temperature controller to obtain a third process risk value and at least one third influence parameter.
In one possible implementation, before invoking the pre-trained anomaly monitoring model to perform anomaly analysis on the data of each component, the method further includes: setting a molding working period of the injection molding machine; and calling an abnormality monitoring model trained in advance every other molding working period to perform abnormality analysis on the data of each part.
In one possible implementation, the method further includes: and drawing a first parameter periodic variation state diagram according to the at least one first influence parameter, drawing a second parameter periodic variation state diagram according to the at least one second influence parameter and drawing a third parameter periodic variation state diagram according to the at least one third influence parameter every other molding working period.
In a possible implementation manner, the determining whether the corresponding component has an abnormality according to the process risk value includes: judging whether the first process risk value is larger than a preset first threshold value or not, and outputting a first result when the first process risk value is larger than the preset first threshold value, wherein the first result indicates that the injection molding machine is abnormal; judging whether the second process risk value is greater than a preset second threshold value, and outputting a second result when the second process risk value is greater than the preset second threshold value, wherein the second result indicates that the mold is abnormal; and judging whether the third process risk value is greater than a preset third threshold value, and outputting a third result when the third process risk value is greater than the preset third threshold value, wherein the third result indicates that the mold temperature controller is abnormal.
In one possible implementation, when it is determined that there is an abnormality in the target component according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component includes: when the injection molding machine is determined to be abnormal, inputting the controller data and the at least one first influence parameter into a pre-trained intelligent adjusting model, outputting the at least one first equipment parameter of the injection molding machine by the intelligent adjusting model, and adjusting the equipment parameter of the injection molding machine according to the at least one first equipment parameter; when the abnormal condition of the mold is determined, inputting the first sensor data and the at least one second influence parameter into a pre-trained intelligent adjusting model, outputting at least one second equipment parameter of the mold by the intelligent adjusting model, and adjusting the equipment parameter of the mold according to the at least one second equipment parameter; when the mold temperature machine is determined to be abnormal, the second sensor data and the at least one third influence parameter are input into a pre-trained intelligent adjusting model, the intelligent adjusting model outputs the at least one third equipment parameter of the mold temperature machine, and the equipment parameter of the mold is adjusted according to the at least one third equipment parameter.
The application further provides an electronic device, which comprises a processor and a memory, wherein the processor is used for realizing the abnormal monitoring method of the injection molding process when executing the computer program stored in the memory.
The application also provides a computer readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method for monitoring an abnormality of an injection molding process.
According to the abnormity monitoring method and the relevant equipment in the injection molding process, the abnormal conditions are monitored and timely feedback prompt is carried out by collecting the data of the molding process in real time, so that the system abnormity can be timely found, the generation of defective products is reduced, the raw materials are saved, and the effective value time of the injection molding equipment is prolonged.
Drawings
Fig. 1 is a schematic structural diagram of an electronic device according to a preferred embodiment of a method for monitoring an abnormality in an injection molding process according to an embodiment of the present disclosure.
Fig. 2 is a flowchart of an exemplary embodiment of an abnormality monitoring method for an injection molding process according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in detail below with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, fig. 1 is a schematic view of an electronic device according to an embodiment of the present application. Referring to fig. 1, the electronic device 1 includes, but is not limited to, a memory 11 and at least one processor 12, which may be connected via a communication bus 13 or directly connected.
The electronic device 1 may be a computer, a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), or other devices with application programs installed therein. It will be understood by those skilled in the art that the schematic diagram 1 is only an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, and may include more or less components than those shown, or combine some components, or different components, for example, the electronic device 1 may further include an input-output device, a network access device, a bus, etc.
Fig. 2 is a flow chart of a preferred embodiment of the abnormality monitoring method for the injection molding process according to the present invention. The abnormality monitoring method for the injection molding process is applied to the electronic device 1. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs. In this embodiment, the method for monitoring an abnormality in an injection molding process includes:
and S21, acquiring data of a plurality of parts in the injection molding process.
In an embodiment of the present application, the obtaining data of a plurality of parts in an injection molding process includes:
(1) Controller data of the injection molding machine is obtained. The injection molding machine is a main molding device for manufacturing thermoplastic plastics or thermosetting plastics into plastic products with various shapes by utilizing a mold. The injection molding machine can heat the plastic, apply high pressure to the molten plastic, and inject the plastic to fill the mold cavity. In specific implementation, a Programmable Logic Controller (PLC) communication board interface is in communication connection with the injection molding machine to collect PLC data in real time, wherein the PLC data includes: the injection molding machine system parameters, the injection molding machine on-off state and the injection molding machine internal data. And sending the PLC data to the electronic equipment through the PLC communication board interface.
(2) First sensor data of a mold is acquired. The mould is manufactured according to the shape and the structure of a real object in proportion in the injection molding process, and the material is made into a tool with a certain shape by a pressing or pouring method. When the temperature measuring device is specifically implemented, a temperature sensor is arranged, the temperature sensor is used for measuring the temperature data of the die, a distance sensor is arranged, and the distance sensor is used for measuring the position data and the size data of the die. Sending the temperature data of the mold, the position data of the mold, and the mold size data to the electronic device as the first sensor data.
(3) And acquiring second sensor data of the mold temperature controller. The mould temperature machine is mechanical equipment with heating and cooling functions, is used for industrial temperature control, and is used for controlling the temperature of the mould in the injection molding process. During specific implementation, a distance sensor is arranged to measure the size data of the circulating pump inside the mold temperature controller, the size data of the heating pipe and the size data of electrical accessories through the distance sensor, and a temperature sensor is arranged to measure the temperature data of the circulating pump inside the mold temperature controller, the temperature data of the heating pipe and the temperature data of the electrical accessories through the temperature sensor. And sending the size data of the circulating pump, the size data of the heating pipe, the size data of the electrical accessories, the temperature data of the circulating pump, the temperature data of the heating pipe and the temperature data of the electrical accessories to the electronic equipment as the second sensor data.
Through acquiring the data of each part in the injection molding process, the abnormity analysis can be carried out on the injection molding process more comprehensively, so that the analysis result is more accurate.
As an optional implementation manner, after the step S21, the method further includes: and segmenting the controller data according to the workshop section information in the controller data to obtain a plurality of workshop section data, wherein different workshop section data correspond to different injection molding processes.
In one embodiment of the present application, injection molding is a cyclic process, each cycle comprising: quantitative feeding, melting and plasticizing, pressure injection, mold filling and cooling, mold opening and mold taking. And taking out the plastic part, closing the mold again, and performing the next cycle. The controller data can be divided into charging process data, plasticizing process data, injection process data, cooling process data and pickup process data according to the cycle period.
By dividing the data of the controller, the process of the abnormal data can be accurately positioned when the data is analyzed abnormally subsequently, so that the abnormal monitoring efficiency is improved.
And S22, calling the pre-trained abnormality monitoring model to perform abnormality analysis on the data of each component to obtain a process risk value and at least one influence parameter of each component.
In an embodiment of the application, an anomaly monitoring model is trained in advance, and in the concrete implementation, a first training data set is obtained and comprises a first preset number of first training data, and the first training data is input into a pre-built neural network framework for training to obtain the anomaly monitoring model. And obtaining a first test data set, wherein the first test data set comprises a preset second amount of first test data, and using the first test data to test the abnormity monitoring model and obtain a first test passing rate. And when the first test passing rate is smaller than a preset first passing rate threshold value, adjusting model parameters of the abnormal monitoring model until the first test passing rate is larger than the preset first passing rate threshold value.
In an embodiment of the application, the invoking of the pre-trained anomaly monitoring model to perform anomaly analysis on the data of each component to obtain the process risk value and the at least one influence parameter of each component includes:
(1) And calling the trained abnormity monitoring model to perform abnormity analysis on the controller data of the injection molding machine to obtain a first process risk value and a parameter value of at least one first influence parameter. During specific implementation, inputting the feeding process data to the trained abnormal monitoring model, and outputting a first feeding risk value; inputting the plasticizing process data to the trained abnormal monitoring model, and outputting a first plasticizing risk value; inputting the injection process data to the trained anomaly monitoring model, and outputting a first injection risk value; inputting the cooling process data to the trained anomaly monitoring model, and outputting a first cooling risk value; inputting the pickup process data to the trained anomaly monitoring model, and outputting a first pickup risk value; setting the proportion of each working section according to the importance of the working sections, for example, the proportion of a feeding process is 10 percent, the proportion of a plasticizing process is 30 percent, the proportion of an injection process is 20 percent, the proportion of a cooling process is 30 percent, and the proportion of a workpiece taking process is 10 percent; and obtaining the first process risk value according to the first feeding risk value, the first plasticizing risk value, the first injection risk value, the first cooling risk value, the first workpiece taking risk value and the ratio of each working section. The larger the first process risk value is, the higher the possibility that the injection molding machine is abnormal is, and the greater the risk of the injection molding process is. The first influencing parameter is an equipment parameter of the injection molding machine, and comprises the following steps: injection quantity, injection pressure, injection rate, plasticizing capacity, mold clamping area, mold clamping force, mold opening and closing speed and idle cycle time.
(2) And calling the trained abnormity monitoring model to perform abnormity analysis on the first sensor data of the mold to obtain a second process risk value and a parameter value of at least one second influence parameter. The larger the second process risk value is, the higher the possibility that the mold is abnormal is, and the greater the risk of the molding process is. The second influencing parameter is an equipment parameter of the mold, and comprises: material type, tensile strength, thickness, burr height, double-sided clearance, corner collapse depth and regrinding life.
(3) And calling the trained abnormal monitoring model to perform abnormal analysis on the second sensor data of the mold temperature controller to obtain a third process risk value and a parameter value of at least one third influence parameter. Wherein, the larger the third process risk value is, the higher the possibility that the mold temperature controller is abnormal is, and the larger the risk of the mold manufacturing process is. The third influence parameter is an equipment parameter of the mold temperature controller, and comprises: temperature control range, temperature control precision, heat transfer medium, expansion tank capacity, pumping power and pumping pressure.
The process risk value and the influence parameter of the injection molding machine, the process risk value and the influence parameter of the mold and the process risk value and the influence parameter of the mold temperature controller are obtained by utilizing the abnormal monitoring model trained in advance, so that the traditional manual monitoring method is replaced, and the monitoring accuracy and efficiency can be improved.
As an optional implementation manner, before the step S22, the method further includes: setting a molding working period of the injection molding machine; and calling an abnormality monitoring model trained in advance every other molding working period to perform abnormality analysis on the data of each part.
As an optional implementation manner, after the step S22, the method further includes: and drawing a first parameter periodic variation state diagram according to the at least one first influence parameter, drawing a second parameter periodic variation state diagram according to the at least one second influence parameter and drawing a third parameter periodic variation state diagram according to the at least one third influence parameter every other molding working period.
And S23, judging whether the corresponding part is abnormal or not according to the process risk value.
In an embodiment of the present application, the determining whether there is an abnormality in the corresponding component according to the process risk value includes: judging whether the first process risk value is larger than a preset first threshold value or not, and outputting a first result when the first process risk value is larger than the preset first threshold value, wherein the first result indicates that the injection molding machine is abnormal; judging whether the second process risk value is greater than a preset second threshold value, and outputting a second result when the second process risk value is greater than the preset second threshold value, wherein the second result indicates that the mold is abnormal; and judging whether the third process risk value is greater than a preset third threshold value, and outputting a third result when the third process risk value is greater than the preset third threshold value, wherein the third result indicates that the mold temperature controller is abnormal.
Illustratively, the first threshold value is set to 0.3, the second threshold value is set to 0.6, and the third threshold value is set to 0.5. The first process risk value is 0.1, the second process risk value is 0.5, the third process risk value is 0.7, and the third process risk value is larger than the third threshold value, and a prompt that the mold temperature machine is abnormal is output.
Whether each part is abnormal or not is judged by utilizing the process risk value, abnormal conditions are monitored and timely feedback prompt is carried out by acquiring forming process data in real time, so that system abnormity can be timely found, defective products are reduced, raw materials are saved, and the effective value time of injection molding equipment is prolonged.
And S24, when the target component is determined to be abnormal according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component.
In an embodiment of the application, the intelligent regulation model is trained in advance, and in the concrete implementation, a second training data set is obtained, the second training data set comprises a preset third number of second training data, and the second training data is input into a pre-built neural network framework for training to obtain the intelligent regulation model. And acquiring a second test data set which comprises a preset fourth amount of second test data, and testing the intelligent regulation model by using the second test data and acquiring a second test passing rate. And when the second test passing rate is greater than a preset second passing rate threshold value, taking the intelligent regulation model as the intelligent regulation model after training is completed, and when the second test passing rate is smaller than the preset second passing rate threshold value, adjusting the model parameters of the intelligent regulation model until the second test passing rate is greater than the preset second passing rate threshold value.
In an embodiment of the present application, when it is determined that there is an abnormality in the target component according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component includes: when the injection molding machine is determined to be abnormal, inputting the controller data and the parameter value of the at least one first influence parameter into a pre-trained intelligent regulation model, outputting the at least one first equipment parameter of the injection molding machine by the intelligent regulation model, and adjusting the equipment parameter of the injection molding machine according to the at least one first equipment parameter; when the mold is determined to be abnormal, inputting the data of the first sensor and the parameter value of the at least one second influence parameter into an intelligent regulation model which is trained in advance, outputting at least one second equipment parameter of the mold by the intelligent regulation model, and regulating the equipment parameter of the mold according to the at least one second equipment parameter; when the mold temperature machine is determined to be abnormal, the data of the second sensor and the parameter value of the at least one third influence parameter are input into a pre-trained intelligent adjusting model, the intelligent adjusting model outputs the parameter value of the at least one third equipment parameter of the mold temperature machine, and the equipment parameter of the mold is adjusted according to the at least one third equipment parameter.
By utilizing the intelligent adjustment model to adjust the parameters of the injection molding machine, the mold and the mold temperature controller, on one hand, intelligent adjustment can be realized, the waste of manpower and material resources is reduced, the adjustment accuracy of the equipment is improved, and on the other hand, important reference information can be provided for field personnel to debug the equipment.
Referring to fig. 1, in the present embodiment, the memory 11 may be an internal memory of the electronic device 1, that is, a memory built in the electronic device 1. In other embodiments, the memory 11 may also be an external memory of the electronic device 1, that is, a memory externally connected to the electronic device 1.
In some embodiments, the memory 11 is used for storing program codes and various data, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 1.
The memory 11 may include random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In one embodiment, the Processor 12 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any other conventional processor or the like.
The program code and various data in the memory 11 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the processes in the methods of the embodiments described above, for example, the steps in the methods for prolonging the service life of the battery, may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can be executed by a processor to implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), or the like.
It is understood that the above described module division is a logical function division, and there may be other division ways in actual implementation. In addition, functional modules in the embodiments of the present application may be integrated into the same processing unit, or each module may exist alone physically, or two or more modules are integrated into the same unit. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.
Claims (10)
1. An abnormality monitoring method for an injection molding process, characterized in that the abnormality monitoring method for an injection molding process comprises:
acquiring data of a plurality of parts in the injection molding process;
calling an abnormality monitoring model trained in advance to perform abnormality analysis on the data of each component to obtain a process risk value and at least one influence parameter of each component;
judging whether the corresponding part is abnormal or not according to the process risk value;
and when the target component is determined to have the abnormality according to the process risk value, adjusting the equipment parameter of the target component according to at least one influence parameter of the target component.
2. The method of claim 1, wherein the obtaining data for a plurality of components in an injection molding process comprises:
acquiring controller data of the injection molding machine, first sensor data of the mold and second sensor data of the mold temperature controller.
3. The method of claim 2, wherein after acquiring the controller data of the injection molding machine, the first sensor data of the mold, and the second sensor data of the mold temperature machine, the method further comprises:
and segmenting the controller data according to the workshop section information in the controller data to obtain a plurality of workshop section data, wherein different workshop section data correspond to different injection molding processes.
4. The method of claim 2 or 3, wherein the invoking of the pre-trained anomaly monitoring model to perform anomaly analysis on the data of each component to obtain the process risk value and the at least one influencing parameter of each component comprises:
calling an abnormality monitoring model which is trained in advance to perform abnormality analysis on controller data of the injection molding machine to obtain a first process risk value and at least one first influence parameter;
calling a pre-trained abnormity monitoring model to perform abnormity analysis on the first sensor data of the mold to obtain a second process risk value and at least one second influence parameter;
and calling a pre-trained abnormity monitoring model to perform abnormity analysis on the second sensor data of the mold temperature controller to obtain a third process risk value and at least one third influence parameter.
5. The method of claim 4, wherein prior to invoking the pre-trained anomaly monitoring model to perform anomaly analysis on the data for each component, the method further comprises:
setting a molding working period of the injection molding machine;
and calling an abnormality monitoring model trained in advance every other molding working period to perform abnormality analysis on the data of each part.
6. The method of claim 5, further comprising:
and drawing a first parameter periodic variation state diagram according to the at least one first influence parameter, drawing a second parameter periodic variation state diagram according to the at least one second influence parameter, and drawing a third parameter periodic variation state diagram according to the at least one third influence parameter at intervals of the molding working period.
7. The method of claim 6, wherein determining whether the corresponding component is abnormal according to the process risk value comprises:
judging whether the first process risk value is larger than a preset first threshold value or not, and outputting a first result when the first process risk value is larger than the preset first threshold value, wherein the first result indicates that the injection molding machine is abnormal;
judging whether the second process risk value is greater than a preset second threshold value, and outputting a second result when the second process risk value is greater than the preset second threshold value, wherein the second result indicates that the mold is abnormal;
and judging whether the third process risk value is greater than a preset third threshold value, and outputting a third result when the third process risk value is greater than the preset third threshold value, wherein the third result indicates that the mold temperature controller is abnormal.
8. The method of claim 7, wherein the adjusting the device parameter of the target component according to the at least one impact parameter of the target component when the target component is determined to be abnormal according to the process risk value comprises:
when the injection molding machine is determined to be abnormal, the controller data and the at least one first influence parameter are input into a pre-trained intelligent adjusting model, the intelligent adjusting model outputs at least one first equipment parameter of the injection molding machine, and the equipment parameter of the injection molding machine is adjusted according to the at least one first equipment parameter;
when the mold is determined to be abnormal, inputting the first sensor data and the at least one second influence parameter into a pre-trained intelligent adjusting model, outputting at least one second equipment parameter of the mold by the intelligent adjusting model, and adjusting the equipment parameter of the mold according to the at least one second equipment parameter;
and when the abnormality of the mold temperature controller is determined, inputting the second sensor data and the at least one third influence parameter into a pre-trained intelligent adjusting model, outputting the at least one third equipment parameter of the mold temperature controller by the intelligent adjusting model, and adjusting the equipment parameter of the mold according to the at least one third equipment parameter.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for anomaly monitoring of an injection molding process according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that it stores at least one instruction which, when executed by a processor, implements a method of anomaly monitoring of an injection molding process according to any one of claims 1 to 8.
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| TW567132B (en) * | 2000-06-08 | 2003-12-21 | Mirle Automation Corp | Intelligent control method for injection molding machine |
| TWI257894B (en) * | 2003-05-08 | 2006-07-11 | Ming-Shyan Huang | A cavity pressure based quality control method for injection molding process |
| JP4499601B2 (en) * | 2005-04-01 | 2010-07-07 | 日精樹脂工業株式会社 | Control device for injection molding machine |
| TWI587110B (en) * | 2015-07-03 | 2017-06-11 | 元智大學 | Real time monitoring system and mothod thereof of optical film manufacturing process |
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