Method for determining manufacturing conditions of polyarylene sulfide resin composition and method for manufacturing resin composition

Abstract

A method for determining manufacturing conditions of a polyarylene sulfide resin composition uses a dataset to execute a machine learning algorithm, thereby determining, in a case where a characteristic value of a characteristic improvement target item of the polyarylene sulfide resin composition is used as a target variable, an item in which a degree of importance of a change in the characteristic value for the characteristic improvement target item among a plurality of items contained in manufacturing condition data and physical property measurement data is high, the dataset including the manufacturing condition data and the physical property measurement data, the manufacturing condition data including at least a compounding component of the polyarylene sulfide resin composition, a mixing condition, and a temperature of a compound during melt mixing as manufacturing condition items, the physical property measurement data including at least an impact resistance of the polyarylene sulfide resin composition manufactured according to the manufacturing condition shown by the manufacturing condition data as a characteristic value item.

Description

Technical Field

[0001] This invention relates to a method for determining the manufacturing conditions of polyarylene sulfide resin compositions and a method for manufacturing resin compositions.

[0002] This application claims priority based on, and incorporates the contents of, US62 / 982,756, a provisional U.S. patent application filed on February 28, 2020, and Japanese patent application Japanese Patent Application No. 2020-159132, filed on September 23, 2020. Background Technology

[0003] Polyaryl sulfide resins, represented by polyphenylene sulfide resins, have excellent heat resistance and chemical resistance, and are widely used in electrical and electronic components, automotive parts, mechanical parts, water heater parts, fibers, and films.

[0004] Conventional techniques exist for manufacturing polyarylether sulfide resin compositions via melt blending. According to this technique, granules of two or more resins are mixed, and the mixed granules are rotated using a screw to simultaneously stir and heat the mixture. The blended resin is then extruded, thereby achieving polymer blending (see, for example, Patent Document 1).

[0005] In addition, in applications such as automotive parts, resin materials with excellent mechanical properties, such as impact resistance, are required as alternatives to metals for the purpose of weight reduction.

[0006] In the past, in order to improve the impact resistance of polyarylene sulfide resins, the formulation methods of elastomer components were sometimes studied.

[0007] Patent document 2 discloses a method for manufacturing a polyarylene sulfide resin composition, wherein the polyarylene sulfide resin (A) and thermoplastic elastomer particles (B) with a volume average particle size of 0.1 mm to 3.0 mm are melt-blended at a ratio of 0.1% to 2.0% by mass of the thermoplastic elastomer particles (B) relative to all the compounding components.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent Document 1: Japanese Patent Application Publication No. 2017-149002

[0011] Patent Document 2: Japanese Patent Application Publication No. 2008-163112 Summary of the Invention

[0012] The problem the invention aims to solve

[0013] Based on the melt-blending process described above, there are many types of parameters that control the manufacturing conditions, and these parameters interact with each other in a complex manner. Determining the appropriate parameter values ​​to improve the impact resistance of the polyarylether sulfide resin composition relies on the experience and intuition of skilled technicians.

[0014] Therefore, the object of the present invention is to provide a method for determining the manufacturing conditions of a polyarylene sulfide resin composition that can determine control parameters without relying on the experience and intuition of a technician.

[0015] Furthermore, when elastomer components are incorporated into polyarylene sulfide resin compositions, it is expected that the higher the blending ratio of the elastomer components and the higher the dispersibility of the elastomer components, the better the impact resistance.

[0016] However, in the polyarylene sulfide resin composition of Patent Document 2, if the elastomer component is to be mixed in a larger proportion than described above, there is still room for improvement in making the elastomer component more well dispersed in the resin composition.

[0017] Therefore, the object of the present invention is to provide a method for manufacturing a polyarylene sulfide resin composition exhibiting excellent impact resistance.

[0018] Solution for solving the problem

[0019] In order to solve the above-mentioned problems, the inventors conducted in-depth research and found that by using the dataset obtained based on the data described later, and performing analysis based on the machine learning algorithm, suitable control parameters can be determined, thus completing the present invention.

[0020] In addition, the inventors conducted in-depth research to solve the above-mentioned problems and found that by increasing the amount of elastomer components and mixing them at a shear rate of a certain value or higher and at a specific set temperature, a polyarylene sulfide resin composition exhibiting excellent impact resistance can be obtained, thus completing the present invention.

[0021] That is, the present invention has the following aspects.

[0022] One aspect of the present invention provides a method for determining the manufacturing conditions of a polyarylene sulfide resin composition. This method uses a dataset to execute a machine learning algorithm. With the characteristic value of the target improvement item of the polyarylene sulfide resin composition as the target variable, it determines the item with the highest importance for the change in the characteristic value of the target improvement item among multiple items contained in the manufacturing condition data and physical property measurement data. The dataset includes the aforementioned manufacturing condition data and the aforementioned physical property measurement data. The manufacturing condition data includes at least the compounding components of the polyarylene sulfide resin composition, mixing conditions, and the temperature of the compound during melt mixing as manufacturing condition items. The physical property measurement data includes at least the impact resistance of the polyarylene sulfide resin composition manufactured according to the manufacturing conditions shown in the aforementioned manufacturing condition data as a characteristic value item.

[0023] In addition, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, the aforementioned machine learning algorithm refers to the algorithm that uses random forest to calculate the importance of each of the multiple items contained in the aforementioned manufacturing condition data and the aforementioned physical property measurement data, thereby determining the items with high importance for the change of characteristic value of the aforementioned characteristic improvement target item.

[0024] In addition, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, the aforementioned manufacturing condition data includes a first type of manufacturing condition item and a second type of manufacturing condition item as the aforementioned manufacturing condition item. The first type of manufacturing condition item is the controlled object of the manufacturing apparatus for the polyarylene sulfide resin composition, and the second type of manufacturing condition item is not the controlled object of the aforementioned manufacturing apparatus.

[0025] Furthermore, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, the manufacturing conditions item of the second type mentioned above includes: the internal temperature of each of the multiple parts of the mixing section of the polyarylene sulfide resin mixing section of the aforementioned manufacturing apparatus.

[0026] Furthermore, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, among the internal temperatures of each of the multiple parts of the aforementioned mixing section included in the manufacturing conditions item of the second type, the upstream side where the raw material of the polyarylene sulfide resin composition is fed into the aforementioned mixing section is more important than the downstream side where the mixed polyarylene sulfide resin composition is extruded.

[0027] In addition, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, the items with higher calculated importance are used as new target variables, and the aforementioned machine learning algorithm is executed to determine the items with higher importance for changes in the characteristic values ​​of the aforementioned new target variables.

[0028] In addition, in the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to one aspect of the present invention, a regression operation using the aforementioned dataset is performed with the calculated items of higher importance as the analytical axis, thereby estimating the correspondence between the changes in the characteristic values ​​of the aforementioned items of higher importance and the changes in the characteristic values ​​of the aforementioned target variable.

[0029] One aspect of the present invention relates to a method for manufacturing a polyarylene sulfide resin composition comprising a polyarylene sulfide resin (A) and a thermoplastic elastomer (B).

[0030] The mixing ratio of the aforementioned thermoplastic elastomer (B) relative to the total mass of the aforementioned polyarylene sulfide resin composition is 5% to 30% by mass.

[0031] The manufacturing method includes a step of melt-blending raw material polyarylether sulfide resin (A) and raw material thermoplastic elastomer (B) using an extruder.

[0032] The shear rate of the aforementioned extruder relative to the inner wall of the barrel, generated by the screw rotation, is 1000–6500 s. -1 ,

[0033] In the region where the length of the aforementioned barrel is more than 3 / 15 of its total length, the set temperature of the aforementioned barrel is below 300°C.

[0034] In one embodiment of the present invention, in the method for manufacturing a polyarylene sulfide resin composition, the aforementioned thermoplastic elastomer (B) is a polyolefin-based thermoplastic elastomer.

[0035] In one embodiment of the present invention, the method for manufacturing a polyarylene sulfide resin composition, wherein the aforementioned thermoplastic elastomer (B) is a glycidyl-modified polyolefin thermoplastic elastomer.

[0036] In one aspect of the method for manufacturing the polyarylene sulfide resin composition of the present invention, the aforementioned thermoplastic elastomer (B) is relative to the total mass (100% by mass) of the structural units constituting the aforementioned thermoplastic elastomer (B).

[0037] Contains 0.1–30% by mass of structural units derived from glycidyl (meth)acrylate.

[0038] Contains 0.1 to 50% by mass of structural units derived from methyl acrylate.

[0039] In one aspect of the method for manufacturing a polyarylene sulfide resin composition of the present invention, the aforementioned thermoplastic elastomer (B) has a dispersion structure with an average dispersion diameter of 0.20 μm or less.

[0040] In one embodiment of the method for manufacturing the polyarylene sulfide resin composition of the present invention, the Charpy impact value of the molded article of the aforementioned resin composition, measured according to ISO 179-1, at 23°C with notches, is 40 kJ / m. 2 above.

[0041] The effects of the invention

[0042] According to the present invention, a method for determining the manufacturing conditions of a polyarylene sulfide resin composition can be provided, which can determine the control parameters without relying on the experience and intuition of a technician.

[0043] According to the present invention, a method for manufacturing a polyarylene sulfide resin composition exhibiting excellent impact resistance can be provided. Attached Figure Description

[0044] Figure 1 This diagram illustrates the functional configuration of the twin-screw extruder of this embodiment.

[0045] Figure 2 This diagram illustrates the machine learning algorithm used in this embodiment.

[0046] Figure 3 This is a diagram used to illustrate the dataset used in this embodiment.

[0047] Figure 4 This is a diagram illustrating an example of the importance calculated using the machine learning algorithm according to this embodiment.

[0048] Figure 5 This diagram illustrates the results of calculations performed using the machine learning algorithm according to this embodiment, with the elastomer blending amount and IR1 temperature as high-importance items and the Charpy impact value as the target variable.

[0049] Figure 6 This is a flowchart illustrating a series of processes used to determine the manufacturing conditions of the polyarylene sulfide resin composition of this embodiment.

[0050] Figure 7 This is a schematic diagram illustrating an example of the composition of the resin composition according to the embodiments.

[0051] Figure 8 This is a schematic diagram illustrating the configuration of the extruder used in the embodiments.

[0052] Figure 9 An image showing the shape of the test piece used in the Charpy impact value determination. Detailed Implementation

[0053] [Overview of Twin-Screw Extruders]

[0054] Hereinafter, the method for determining the manufacturing conditions of the polyarylene sulfide resin composition according to embodiments of the present invention will be described with reference to the accompanying drawings.

[0055] Figure 1 This is a diagram used to explain the functional configuration of the twin-screw extruder 10 according to this embodiment. The functional configuration of the twin-screw extruder 10 according to this embodiment will be explained with reference to the same diagram. The twin-screw extruder 10 includes: a drive unit 11, a feeder 12, a barrel 13, a screw 14, and an infrared temperature sensor IR.

[0056] The feeder 12 is an inlet for feeding the raw materials of the polyarylether sulfide resin composition of the embodiment.

[0057] In this embodiment, the polyarylene sulfide resin composition broadly includes resin compositions containing polyarylene sulfides. The raw materials for the polyarylene sulfide resin composition may include polyarylene sulfides. Polyphenylene sulfide can be an example of a polyarylene sulfide. It should be noted that the raw materials for the polyarylene sulfide resin composition include the compounding components constituting the polyarylene sulfide resin composition and precursors of the compounding components.

[0058] The raw materials for the polyarylene sulfide resin composition may also include any components that are mixed with the polyarylene sulfide. When the polyarylene sulfide is mixed with an elastomer, the elastomer is also fed into the feeder 12. There may be one or more feeders 12 to separately feed the raw materials for the polyarylene sulfide resin composition.

[0059] The barrel 13 has a cylindrical shape. One end of the barrel 13 is connected to the feeder 12, and the other end is connected to the mold 19. In the following description, the feeder 12 side of the barrel 13 is sometimes referred to as the upstream side, and the mold 19 side is sometimes referred to as the downstream side. The screw 14 is housed inside the barrel 13. The raw material introduced into the feeder 12 is heated by a heater (not shown), at least a portion of the raw material (e.g., polyarylene sulfide) is melted inside the barrel 13, and the entire input raw material is mixed by the screw 14. In the following description, the barrel 13 is also referred to as the mixing section. In the mixing section, the raw material of the polyarylene sulfide resin composition is mixed. Hereinafter, the contents inside the barrel during melt mixing are referred to as the mixture.

[0060] In this embodiment, multiple heating units (not shown) can be arranged at different positions along the x-axis. These multiple heating units at different positions along the x-axis heat the barrel 13 according to their respective temperatures. The twin-screw extruder 10 has multiple heating units that heat the barrel 13 according to their respective temperatures, thereby heating the compound at different temperatures on the upstream and downstream sides of the barrel 13. A barrel covering the barrel 13 can be used as an example of a heating unit.

[0061] An infrared temperature sensor IR measures the temperature of the barrel 13. Specifically, the infrared temperature sensor IR measures the temperature of the molding material present inside the barrel 13 during melt mixing. The twin-screw extruder 10 may be equipped with multiple infrared temperature sensors IR. In this embodiment, the twin-screw extruder 10 is equipped with a first infrared temperature sensor IR1, a second infrared temperature sensor IR2, a third infrared temperature sensor IR3, and a fourth infrared temperature sensor IR4, sequentially from the upstream side of the barrel 13.

[0062] The screw 14 is driven to rotate by the drive unit 11. By rotating, the screw 14 guides the compound inside the barrel from the upstream side to the downstream side. The raw material fed into the feeder 12 is melt-mixed via the barrel 13 and extruded from the die 19 in the form of the resulting polyarylene sulfide resin composition.

[0063] The drive unit 11 includes a motor and gearbox (not shown). The drive unit 11 controls the speed and torque of the motor to a predetermined screw speed (rpm), thereby rotating the screw 14.

[0064] Figure 2 This is a diagram used to explain the machine learning algorithm 20 of this embodiment. The machine learning algorithm 20 will be explained with reference to the same diagram.

[0065] Data set DS is input from storage device 30 to machine learning algorithm 20. Machine learning algorithm 20 calculates high-importance items HC based on the input data set DS. The calculated high-importance items HC are stored in storage device 30.

[0066] Specifically, the method for determining the manufacturing conditions of the polyarylene sulfide resin composition in this embodiment is as follows: a machine learning algorithm is executed using the dataset DS to determine the items with high importance for improving properties, namely, the high importance items HC.

[0067] More specifically, the method for determining the manufacturing conditions of the polyarylene sulfide resin composition in this embodiment is as follows: using the dataset DS to execute a machine learning algorithm, and taking the characteristic value of the target item for improving the properties of the polyarylene sulfide resin composition as the target variable, the method determines the item with high importance for the change of the characteristic value of the target item for improving the properties among the multiple items contained in the manufacturing condition data CD and the physical property measurement data MD.

[0068] Storage device 30 stores dataset DS and high-importance items HC. The dataset DS will be explained with reference to the references.

[0069] Figure 3 This is a diagram used to illustrate the dataset DS in this embodiment.

[0070] The dataset DS contains: manufacturing condition data CD and physical property measurement data MD.

[0071] The manufacturing condition data (CD) should include at least the formulation components of the polyarylene sulfide resin composition, the mixing conditions, and the temperature of the compound during melt mixing as manufacturing condition items.

[0072] In this embodiment, the manufacturing condition data CD includes: a first type of manufacturing condition item (hereinafter also referred to as a control variable or first manufacturing condition data CD1) and a second type of manufacturing condition item (hereinafter also referred to as a measured variable or second manufacturing condition data CD2). The first type of manufacturing condition item is a controlled object of the manufacturing apparatus for the polyarylene sulfide resin composition, while the second type of manufacturing condition item is not a controlled object of the manufacturing apparatus. The second manufacturing condition data CD2 refers to the measured value obtained through actual measurement, that is, the value obtained based on the trend.

[0073] Specifically, the first manufacturing condition data CD1 includes: the first modification amount of the elastomer, the second modification amount of the elastomer, the total modification amount, the elastomer compounding amount, and the screw speed. The second manufacturing condition data CD2 includes: current, compound pressure, temperature of IR1, temperature of IR2, temperature of IR3, and temperature of IR4. That is, the second type of manufacturing condition item (second manufacturing condition data CD2) includes: the respective temperatures of various parts of the compounding section of the polyarylene sulfide compounding process. It should be noted that the respective temperatures of various parts of the compounding section include the internal temperature of the compounding section, specifically, the compound temperature or the resin temperature.

[0074] It should be noted that the mixing conditions include: the first modification amount of the elastomer used, the second modification amount of the elastomer used, the total modification amount including the first and second modification amounts of the elastomer used, the elastomer blending amount, and the screw speed.

[0075] It should be noted that the elastomer blending amount can be set as the content ratio (mass%) of the elastomer relative to the total mass of the polyarylene sulfide resin composition.

[0076] It should be noted that the amount of modification of the elastomer corresponds, for example, to the amount of functional groups optionally present in the thermoplastic elastomer (B) described later.

[0077] It should be noted that the current refers to the current required to drive the screw 14 to rotate when the compound is extruded from the barrel 13. As a variation, the extrusion torque can be used instead of the current as the measured variable.

[0078] It should be noted that the compound pressure refers to the value measured by a pressure sensor (not shown) located in the die section on the downstream side of the barrel 13.

[0079] It should be noted that in this embodiment, the items that are determined to be important in the second manufacturing condition data CD2, which is used as the measured variable, can be used as the control variable, i.e., the first manufacturing condition data CD1. The items that are not determined to be unimportant in the control variable, i.e., the first manufacturing condition data CD1, can be used as the measured variable, i.e., the second manufacturing condition data CD2.

[0080] The physical property measurement data MD represents the characteristic value of the polyarylene sulfide resin composition manufactured according to the manufacturing conditions shown in the manufacturing condition data CD. That is, the storage device 30 stores the manufacturing condition data CD and the physical property measurement data MD corresponding to the manufacturing condition data CD as a dataset DS. In the following description, the physical property measurement data MD will also be referred to as a physical property variable.

[0081] Specifically, the physical property data (MD) includes at least the impact resistance of the polyarylene sulfide resin composition manufactured under the manufacturing conditions indicated by the manufacturing condition data (CD) as a characteristic value. More specifically, the physical property data (MD) includes: melt viscosity, average dispersion diameter of the elastomer, and Charpy impact value.

[0082] Melt viscosity is a measure of the viscosity obtained by applying a load to the polyarylether sulfide resin composition. For example, melt viscosity can be measured as follows: The barrel temperature is set to 300°C, the orifice length to 10 mm, and granules of the polyarylether sulfide resin composition are added to a flow tester with an orifice diameter of 1 mm. After preheating for 6 minutes, a load of 50 kg is applied, and the viscosity is then measured.

[0083] The average dispersion diameter of the elastomer refers to the value measured from the obtained polyarylene sulfide resin composition. Specifically, the average dispersion diameter of the elastomer can be obtained as follows: The obtained polyarylene sulfide resin composition is made into a molding material, which is then molded into a multi-faceted test piece using an injection molding machine. The molded multi-faceted test piece is cut at the center of its length, the cut surface is ground, impregnated in xylene, and subjected to ultrasonic treatment at 50°C. The elastomer dispersion located in the cross-section is removed by extraction with xylene, and then dried at 130°C for 2 hours. The cross-section is observed using SEM, and the image is measured to obtain the average dispersion diameter. It should be noted that the area where the elastomer has been removed is called the porous phase, which is represented by circles with low brightness black. Using image analysis software, the equivalent diameter of the circular area of ​​all the black circles identified in the image field of view is measured (the value obtained by calculating the diameter of a circle corresponding to the area of ​​the circle), and divided by the number of circles. The average value obtained can be taken as the average dispersion diameter of the elastomer.

[0084] Charpy impact value refers to the value measured from a obtained polyarylene sulfide resin composition. For example, the Charpy impact value can be determined as follows: the obtained polyarylene sulfide resin composition is made into a molding material, and then molded in an injection molding machine at a barrel temperature of 300°C and a mold temperature of 130°C to obtain a test piece with a length of 80 mm × width of 10.0 mm × thickness of 4.0 mm. The test piece is then cut according to ISO 2818, and the test is conducted at 23°C according to ISO 179-1, thereby determining the Charpy impact value.

[0085] It should be noted that, in addition to the items mentioned above, the physical property measurement data MD may also include values ​​such as high temperature heat resistance and high temperature elastic modulus, which can be used as targets for property improvement.

[0086] Figure 3 In one example, datasets DS1 and DS2 are shown as dataset DS. Dataset DS1 is as follows: the first modification amount of the elastomer is "3", the second modification amount of the elastomer is "27", the total modification amount is "30", the elastomer blending amount is "2", the screw speed is "150", the current is "75", the resin pressure is "1.5", the temperature of IR1 is "306", the temperature of IR2 is "310", the temperature of IR3 is "307", the temperature of IR4 is "308", the melt viscosity is "156", the gas production amount is "0.14", the average dispersion diameter of the elastomer is "0.178", and the Charpy impact value is "3.5".

[0087] The dataset DS2 is as follows: the first modification amount of the elastomer is "3", the second modification amount of the elastomer is "27", the total modification amount is "30", the elastomer blending amount is "2", the screw speed is "300", the current is "88", the resin pressure is "1.4", the temperature of IR1 is "316", the temperature of IR2 is "319", the temperature of IR3 is "316", the temperature of IR4 is "316", the melt viscosity is "136", the gas generation is "0.14", the average dispersion diameter of the elastomer is "0.118", and the Charpy impact value is "3.6".

[0088] That is, in the example shown in the same figure, the changes in the measured variables and physical property variables are shown as control variables when the screw speed is changed from "150" to "300". Specifically, by changing the screw speed from "150" to "300", the Charpy impact value is increased from "3.5" to "3.6".

[0089] It should be noted that measured variables and physical property variables can be the average value based on the results of multiple measurements.

[0090] Back Figure 2Machine learning algorithm 20 calculates the importance (%) of each item in the obtained dataset DS. Specifically, machine learning algorithm 20 uses a random forest method to calculate the importance. As an example, machine learning algorithm 20 calculates the importance of each item relative to the Charpy shock value as the target variable and items other than the Charpy shock value as explanatory variables. In the following explanation, the target variable set as the object used to improve the characteristic is also referred to as the characteristic improvement object item.

[0091] The machine learning algorithm 20 is as follows: take all the control variables, measured variables and physical property variables in the obtained dataset DS as the analysis objects, and calculate the importance (%).

[0092] Here, the hyperparameters used to calculate importance can be appropriately changed to determine the optimal value for the coefficient (Score). Alternatively, grid search or Bayesian optimization methods can be used to apply the optimized values.

[0093] Figure 4 This is a diagram illustrating an example of the importance calculated according to the machine learning algorithm 20 of this embodiment. An example of the importance calculated according to the machine learning algorithm 20 will be described while referring to the same diagram.

[0094] According to machine learning algorithm 20, the importance of each item corresponding to the dataset DS is calculated. In the example shown in the same figure, since the result is the calculation of the importance of each item relative to the Charpy impact value, the Charpy impact value is represented as "target". Specifically, regarding the importance of each item relative to the Charpy impact value, the importance for the first modification amount of the elastomer is "1.4", the importance for the second modification amount of the elastomer is "1.2", the importance for the total modification amount is "1.1", the importance for the elastomer blending amount is "57.6", the importance for the screw speed is "3.2", the importance for the current is "0.8", the importance for the compound pressure is "5.3", the importance for the temperature of IR1 is "11.3", the importance for the temperature of IR2 is "10.2", the importance for the temperature of IR3 is "1.1", the importance for the temperature of IR4 is "1.3", the importance for the melt viscosity is "4.7", the importance for the gas generation is "0.1", and the importance for the average dispersion diameter of the elastomer is "0.9". It should be noted that this importance is obtained by multiplying each calculated value by 100 to make the total 100.

[0095] Next, the machine learning algorithm 20 determines the high-importance item HC according to a prescribed method. That is, the machine learning algorithm 20 calculates the importance of each of the multiple items contained in the manufacturing condition data CD and the physical property measurement data MD, thereby determining the item (i.e., the high-importance item HC) that has a higher importance for the change in the characteristic value of the characteristic improvement target item (in this example, the Charpy impact value).

[0096] Figure 4 In the example shown, the high-importance items HC are the elastomer blending amount, the temperature of IR1, and the temperature of IR2.

[0097] In this embodiment, among the temperatures of IR1 to IR4, which are measured variables, the temperatures of IR1 and IR2 are of higher importance. That is, among the compound temperatures in the various parts of the mixing section included in the manufacturing conditions item (measured variables) of the second type, the upstream side of the raw material into which the polyarylether sulfide resin composition is added in the mixing section is of higher importance than the downstream side where the mixed polyarylether sulfide resin composition is extruded.

[0098] The method for determining the high importance (HC) of an item can be configured as follows: the top three items with the highest calculated importance are designated as high importance (HC).

[0099] It should be noted that, as a variation, machine learning algorithm 20 can be configured as follows: based on a predetermined threshold, high-importance items HC are selected. For example, if the predetermined threshold is 10, Figure 4 In the example shown, the elastomer blending amount, the temperature of IR1, and the temperature of IR2 are high-importance items HC.

[0100] Next, for machine learning algorithm 20, the items with higher calculated importance are used as new target variables, and the items with higher importance for changes in the feature values ​​of the new target variables are determined. Specifically, Figure 4 In one example, the elastomer mixing amount, the temperature of IR1, and the temperature of IR2 are high-importance items (HC). Therefore, the temperature of IR1, which is one of the high-importance items (HC), is taken as the target variable, and the items other than the temperature of IR1 are taken as explanatory variables. The random forest algorithm is used to calculate the importance of each item relative to the temperature of IR1.

[0101] As described above, according to machine learning algorithm 20, the items specified in the dataset DS are used as target variables, and the items other than the target variable are used as explanatory variables. The random forest algorithm is then used to calculate the importance of each item relative to the target variable. For the high-importance items HC calculated by machine learning algorithm 20, specific items are further used as target variables, and the importance is recalculated using the random forest algorithm, thereby gradually determining the items with high importance.

[0102] Next, for machine learning algorithm 20, support vector regression is performed on items determined to be high-importance items (HC). Specifically, for machine learning algorithm 20, using the calculated high-importance items (HC) as analytical axes, regression operations are performed using the dataset DS to infer the correspondence between changes in the characteristic values ​​of high-importance items (HC) and changes in the characteristic values ​​of the target variable. Here, the items determined to be high-importance items (HC) can be determined comprehensively based on the items determined in the initial calculation and those determined in the second and subsequent calculations. Here, the hyperparameters used in support vector regression can be appropriately changed to maximize the coefficient (Score). Alternatively, optimized values ​​can be used using grid search or Bayesian optimization procedures.

[0103] In this embodiment, the elastomer blending amount and the temperature of IR1 are taken as high-importance items HC, and the Charpy impact value is taken as target variables. The algorithm of support vector regression is used for calculation.

[0104] Figure 5 This is a graph illustrating the results of calculations performed using a support vector regression algorithm according to this embodiment, with the elastomer blending amount and IR1 temperature as high-importance items (HC), and the Charpy impact value as the target variable. In the same graph, the IR1 temperature, elastomer blending amount, and Charpy impact value are presented as a three-dimensional plot.

[0105] Figure 5 As shown in the example, it can be seen that a higher elastomer blending ratio and a compound temperature of IR1 below 320°C are manufacturing conditions that improve Charpy impact value.

[0106] Figure 6 This is a flowchart illustrating a series of processes for determining the manufacturing conditions of the polyarylene sulfide resin composition of this embodiment. The series of processes for determining the manufacturing conditions of the polyarylene sulfide resin composition of this embodiment will be described with reference to the same figure.

[0107] (Step S110) In the machine learning algorithm 20, the dataset DS is obtained from the storage device 30 according to the specified communication method.

[0108] (Step S120) In machine learning algorithm 20, the importance is calculated using random forest based on the obtained dataset DS. At this point, machine learning algorithm 20 calculates the importance of all control variables, measured variables, and property variables contained in the dataset DS.

[0109] (Step S130) In the machine learning algorithm 20, the item with high importance, i.e. the high importance item HC, is selected from the multiple importance values ​​calculated according to the specified conditions.

[0110] (Step S140) In the machine learning algorithm 20, it is determined whether to further set a target variable based on the prescribed conditions. If the machine learning algorithm 20 further sets the target variable and calculates the importance (i.e., step S140; yes), the process proceeds to step S120. If the machine learning algorithm 20 further sets the target variable but does not calculate the importance (i.e., step S140; no), the process proceeds to step S150.

[0111] It should be noted that the condition for the machine learning algorithm 20 to determine whether to further set the target variable is that it can obtain the user's response based on the high importance item HC calculated by prompting the user.

[0112] It should be noted that the condition for the machine learning algorithm 20 to determine whether to further set the target variable is that it can be automatically determined based on the difference between the item ranked 1st in importance and the item ranked 2nd in importance.

[0113] (Step S150) In the machine learning algorithm 20, support vector regression analysis is performed on the calculated high importance item HC to obtain suitable manufacturing conditions.

[0114] (Step S160) In the machine learning algorithm 20, the improvement conditions are determined.

[0115] It should be noted that the machine learning algorithm 20 can be configured as follows: based on the results of the support vector regression analysis performed in step S150, the user's response is obtained, thereby determining the improvement conditions.

[0116] [Summary of Implementation Methods]

[0117] According to the embodiments described above, the method for determining the manufacturing conditions of the polyarylene sulfide resin composition is as follows: A machine learning algorithm 20 is executed using a dataset DS to determine items with high importance in relation to changes in the characteristic values ​​of the target improvement items. The dataset DS includes manufacturing condition data CD and physical property measurement data MD. The manufacturing condition data CD includes at least the compounding components of the polyarylene sulfide resin composition, mixing conditions, and the temperature of the compound during melt mixing. Furthermore, the physical property measurement data MD includes at least the impact resistance of the polyarylene sulfide resin composition manufactured according to the manufacturing conditions shown in the manufacturing condition data CD as a characteristic value item.

[0118] Previously, in order to determine the control parameters used to improve the property values ​​of polyarylether sulfide resin compositions, there were many types of parameters used for manufacturing condition control, relying on the experience and intuition of skilled technicians. According to this embodiment, items with high importance for changes in property values ​​for the target property improvement item can be easily calculated; therefore, control parameters can be determined without relying on the experience and intuition of technicians.

[0119] Furthermore, according to the implementation method described above, machine learning algorithm 20 refers to an algorithm that uses random forest. Additionally, machine learning algorithm 20 determines items with high importance for changes in characteristic values ​​of the target improvement item by calculating the importance of each of the multiple items contained in the manufacturing condition data CD and the physical property measurement data MD. Specifically, in machine learning algorithm 20, all control variables, measured variables, and physical property variables in the dataset DS are used as analysis objects, their importance is calculated, and based on the calculated importance, high importance items HC are calculated. That is, according to this implementation method, even when the physical property measurement data MD, which is not controlled as a control parameter but whose control result changes, is important, the parameter that changes according to this trend can also be determined as a high importance item HC.

[0120] Furthermore, according to the embodiments described above, the manufacturing condition data CD includes control variables that are controlled objects of the manufacturing apparatus for the polyarylene sulfide resin composition, and measured variables that are not controlled objects of the manufacturing apparatus, as manufacturing condition items CD. That is, according to this embodiment, if a parameter that changes according to a trend that is not controlled as a control parameter is important, the parameter that changes according to that trend can also be determined as a high-importance item HC.

[0121] Furthermore, according to the embodiment described above, the measured variables include the temperatures inside the barrel of each of the multiple locations of the barrel 13. That is, in the machine learning algorithm 20, the temperatures inside the barrel of each of the multiple locations of the barrel 13 (the temperature of the compound during melt mixing) are used as measured variables to calculate the items with high importance for changes in the characteristic values ​​of the target items for characteristic improvement. Therefore, according to this embodiment, the machine learning algorithm 20 can calculate the high-importance item HC with high accuracy.

[0122] Furthermore, according to the embodiment described above, among the various barrel temperatures (mixture temperatures during melt mixing) of the multiple parts of the barrel 13 included in the measured variables, the upstream side of the barrel 13 is more important than the downstream side. That is, according to this embodiment, it is possible to determine specific important parameters that could not be determined in the past without relying on the experience and intuition of skilled technicians as control parameters.

[0123] Furthermore, according to the implementation method described above, in machine learning algorithm 20, the calculated high-importance item HC is re-used as the target variable, and items with high importance for changes in the characteristic value of the target variable are determined. Therefore, machine learning algorithm 20 can determine parameters that are helpful to the most important parameters. Since machine learning algorithm 20 can determine parameters that are helpful to parameters determined to be important, parameters for improving the characteristic value of the target item can be selected more accurately.

[0124] Furthermore, according to the implementation method described above, using the calculated high-importance item HC as the analytical axis, a regression operation is performed using the dataset DS to estimate the correspondence between changes in the high-importance item HC and changes in the characteristic value of the target variable. That is, according to this implementation method, parameters for improving the characteristic value of the target item can be easily selected.

[0125] It should be noted that the functions of the various parts of the machine learning algorithm 20 in the above embodiments, or a part thereof, can be implemented as follows: the program for implementing these functions is stored in a storage medium that can be read by a computer, and the computer system reads and executes the program stored in the storage medium, thereby achieving the desired result. It should be noted that the term "computer system" here includes hardware such as the operating system and peripheral devices.

[0126] Furthermore, "a storage medium that can be read by a computer" refers to portable media such as magneto-optical discs, ROMs, and CD-ROMs, as well as storage units such as hard drives built into a computer system. Moreover, "a storage medium that can be read by a computer" can also include: media that dynamically maintains a program for a short period of time, such as communication lines used to transmit programs via networks like the Internet, and media that maintain a program for a constant time, such as servers or volatile memory within a user's computer system. Additionally, the aforementioned program can be a part of what is meant to achieve the functions described above, or it can be implemented by further combining the functions with programs already stored in the computer system.

[0127] The following describes a method for manufacturing the resin composition according to an embodiment of the present invention.

[0128] Resin Compositions

[0129] The method for manufacturing the polyarylene sulfide resin composition of the embodiment is a method for manufacturing a polyarylene sulfide resin composition containing polyarylene sulfide resin (A) and thermoplastic elastomer (B). The manufacturing method includes the following steps: melt-blending the raw material polyarylene sulfide resin (A) and the raw material thermoplastic elastomer (B) using an extruder, wherein the shear speed generated by the screw rotation relative to the inner wall of the extruder is 1000-6500 s. -1In the region where the length is more than 3 / 15 of the total length of the aforementioned barrel, the set temperature of the aforementioned barrel is below 300°C.

[0130] In this specification, the method for manufacturing the polyarylene sulfide resin composition of the embodiments is sometimes simply referred to as "the manufacturing method of the embodiments" or "the manufacturing method". Additionally, the polyarylene sulfide resin composition of the embodiments is sometimes simply referred to as "the resin composition of the embodiments" or "the resin composition".

[0131] In the manufacturing method of the embodiment, the raw materials polyarylene sulfide resin (A) and raw material thermoplastic elastomer (B) can be fed into an extruder in such a way that the mixing ratio of the aforementioned thermoplastic elastomer (B) is 5 to 30% by mass relative to the total mass (100% by mass) of the aforementioned polyarylene sulfide resin composition, and then melt-blended using the extruder.

[0132] In the manufacturing method of this embodiment, before melt-blending the raw material polyarylether sulfide resin (A) and the raw material thermoplastic elastomer (B), the two can be pre-dry-blended in a mixing apparatus to obtain a mixture containing the raw material polyarylether sulfide resin (A) and the raw material thermoplastic elastomer (B). Examples of such a mixture include one containing polyarylether sulfide resin particles (a) and thermoplastic elastomer particles (b), described later. From the perspective of enabling good dispersion of the aforementioned thermoplastic elastomer (B), it is preferable to feed this mixture into a melt-blending apparatus and melt-blend it.

[0133] Here, a method for pre-dry mixing the raw material polyarylether sulfide resin (A) and the raw material thermoplastic elastomer (B) can be exemplified by using a mixing device such as a Notta mixer, a roller, or a Henschel mixer. For example, when using a Notta mixer, the operating conditions can be described as follows: the rotational speed of the screw installed inside the aforementioned Notta mixer is in the range of 50 rpm to 80 rpm, and the revolution speed is in the range of 1.5 rpm to 2.5 rpm.

[0134] In the manufacturing method of the embodiment, the melt mixing using an extruder has a shear rate of 1000–6500 s relative to the inner wall of the barrel caused by the screw rotation. -1 Melt mixing is carried out under the aforementioned conditions, preferably at a shear rate of 1500–4800 s. -1 Melt mixing is performed under the aforementioned conditions, more preferably at a shear rate of 3000–4000 s. -1 Melt mixing is carried out under the conditions of [specific conditions].

[0135] Here, the shear rate is set to the value obtained by the following formula.

[0136] Shear rate (γ) = (π × D × N) / H

[0137] 60rpm is 1s -1 Therefore, with s -1 If the shear rate is calculated in units of , it can be obtained by conversion as follows.

[0138] Shear rate (γ) = [π × D × (N / 60)] / H

[0139] [D represents the screw diameter (mm), N represents the screw speed (rpm), and H represents the tip clearance (mm).]

[0140] Figure 7 This is a schematic diagram illustrating an example of the composition of the resin composition according to the embodiments.

[0141] Figure 7 The image shows a resin composition in which a polyarylene sulfide resin (A) is a matrix polymer and a thermoplastic elastomer (B) is dispersed in the matrix polymer. By mixing at a shear rate of at least the aforementioned lower limit, the dispersibility of the aforementioned thermoplastic elastomer (B) can be well achieved.

[0142] As an indicator of the good dispersibility of the thermoplastic elastomer (B), the average dispersion diameter of the thermoplastic elastomer (B) in the resin composition of the embodiment can be used. The smaller the average dispersion diameter, the better the dispersibility of the thermoplastic elastomer (B). In addition, by mixing at a shear rate below the above-mentioned upper limit, the deterioration and demolecularization of the thermoplastic elastomer (B) can be prevented, and the impact resistance of the resin composition of the embodiment can be improved.

[0143] The average dispersion diameter of the thermoplastic elastomer (B) in the resin composition can be determined by the following method.

[0144] The resin composition of the test object was used as the molding material and injection molded into a multi-object test piece (molded article) of ISO 3167 type A using an injection molding machine. Then, the multi-object test piece was cut at its center along its length, the cut surface was ground, impregnated in xylene, and ultrasonically treated at 50°C to remove the dispersion of thermoplastic elastomer (B) in the cross-section using xylene extraction. After drying at 130°C for 2 hours, the cross-section was observed using SEM to obtain an image. The areas where thermoplastic elastomer (B) was removed became the porous phase, represented as low-brightness black circles. Using image analysis software, the equivalent diameter of the circular area of ​​all (n=300 or more) black circles identified within the image field of view was measured (the value obtained by calculating the diameter of a perfect circle corresponding to the area of ​​the circle), divided by the number of circles, and the average value obtained was taken as the average dispersion diameter of the thermoplastic elastomer (B).

[0145] The average dispersion diameter of the thermoplastic elastomer (B) in the resin composition of the embodiment is preferably 0.20 μm or less, more preferably 0.01 μm or more and 0.18 μm or less, and even more preferably 0.05 μm or more and 0.17 μm or less.

[0146] Resin compositions with an average dispersion diameter of thermoplastic elastomer (B) within the above-mentioned numerical range tend to exhibit excellent impact resistance and are preferred.

[0147] In the manufacturing method of the embodiment, the screw speed of the extruder that generates the above-mentioned shearing speed can be 330 to 2000 rpm, 500 to 1500 rpm, or 970 to 1250 rpm, as an example.

[0148] Furthermore, regarding the melt mixing using an extruder in the manufacturing method of the embodiment, in the region where the length is 3 / 15 or more of the total length of the barrel of the aforementioned extruder, the set temperature of the aforementioned barrel is below 300°C, preferably above 220°C and below 300°C, more preferably above 230°C and below 295°C, further preferably above 250°C and below 290°C, and particularly preferably above 260°C and below 285°C.

[0149] The set temperature of the aforementioned barrel can be used as an example to cover the set temperature of the aforementioned barrel's feed cylinder.

[0150] By setting a "low-temperature zone" where the set temperature is within the above-mentioned numerical range, it is possible to simultaneously prevent thermal degradation of the resin composition and improve the dispersibility of the thermoplastic elastomer (B), thereby improving the impact resistance of the resin composition of the embodiment.

[0151] Based on the above viewpoints, it is preferable to ensure a certain length (3 / 15 or more) in the low-temperature region. On the other hand, setting an upper limit on the length of the low-temperature region, and setting a "high-temperature region" with a temperature higher than the low-temperature region in addition to the region that is the low-temperature region, can further improve the Charpy impact value of the resin composition, which is therefore preferred.

[0152] The aforementioned low-temperature region is a region with a length of 3 / 15 or more relative to the total length of the barrel of the aforementioned extruder, a region with a length of 3 / 15 or more but less than 15 / 15, a region with a length of 4 / 15 or more but less than 13 / 15, or a region with a length of 5 / 15 or more but less than 12 / 15.

[0153] The aforementioned low-temperature region can be continuous or discontinuous. If the aforementioned low-temperature region is discontinuous, the total length of the regions belonging to the low-temperature region can be used as the low-temperature region.

[0154] The aforementioned low-temperature region is preferably a continuous region. The aforementioned low-temperature region can be a region of continuous length of 3 / 15 or more relative to the total length of the barrel of the aforementioned extruder, a region of continuous length of 3 / 15 or more but less than 15 / 15, a region of continuous length of 4 / 15 or more but less than 13 / 15, or a region of continuous length of 5 / 15 or more but less than 12 / 15.

[0155] It should be noted that the total length of the barrel does not include the extruder's die and head section.

[0156] When an upper limit is set for the length of the low-temperature zone, the remaining barrel area can be set to any temperature value that exceeds the set temperature of the barrel in the low-temperature zone and is below 300°C.

[0157] For example, in a region where the barrel length is more than 3 / 15 of the total length of the extruder, and in a low-temperature region where the barrel's set temperature is below 300°C, the remaining high-temperature region can have a barrel set temperature between 300°C and 370°C.

[0158] In the region where the length of the barrel is more than 3 / 15 of the total length of the extruder described above, it can be designated as a low-temperature region where the set temperature of the barrel is above 230°C and below 295°C, and in the remaining high-temperature region, the set temperature of the barrel is set to be above 295°C and below 360°C.

[0159] In the region where the length is more than 3 / 15 of the total length of the extruder barrel, a low-temperature region can be defined where the set temperature of the barrel is above 250°C and below 290°C. In the remaining high-temperature region, the set temperature of the barrel is set to be above 290°C and below 340°C.

[0160] In the region where the length is more than 3 / 15 of the total length of the barrel of the aforementioned extruder, it can be set as a low-temperature region where the set temperature of the aforementioned barrel is above 260°C and below 285°C, and in the remaining high-temperature region, the set temperature of the barrel is set to be above 285°C and below 320°C.

[0161] Alternatively, in a region where the barrel's set temperature is below 300°C in a length that is 3 / 15 or more of the total length of the extruder's barrel, the set temperature of the barrel in the remaining high-temperature region can be, for example, above 300°C and below 370°C, above 300°C and below 360°C, above 300°C and below 340°C, or above 300°C and below 320°C.

[0162] When a temperature difference is set in the barrel's set temperature, in the region where the barrel's set temperature is above 220°C, the temperature difference between the highest and lowest set temperature values ​​of the barrel can be 10–150°C, 30–120°C, or 50–100°C, as an example.

[0163] The temperature difference between the highest set temperature of the barrel in the low-temperature zone and the lowest set temperature of the barrel in the high-temperature zone can be 5–150°C, 7–100°C, or 10–30°C, as an example.

[0164] In addition, a temperature difference can also be set in the low-temperature region. When a temperature difference is also set in the low-temperature region, taking the region where the barrel's set temperature is above 220°C as an example, the temperature difference between the highest and lowest set temperatures of the barrel in the low-temperature region can be 5–80°C, 10–70°C, or 20–60°C.

[0165] Thus, for the scheme of setting a temperature difference in the melt mixing process according to the region of the barrel, it is interesting that when a low temperature region is set on the upstream side of the raw material into the resin composition (the region of the barrel on the raw material inlet side when the total length of the barrel is divided into two equal parts), the improvement of the impact resistance of the resin composition in the embodiment can be more effective.

[0166] For example, when the total length of the barrel of the aforementioned extruder is divided into 15 equal parts, counting from the raw material inlet side (upstream side) where the compounding components of the resin composition are fed in, the aforementioned low-temperature region can be set in a manner that includes at least the 3 / 15 to 5 / 15 region, in a manner that includes at least the 2 / 15 to 6 / 15 region, and in a manner that includes at least the 1 / 15 to 7 / 15 region.

[0167] The reason for ideally placing the upstream side of the extruder in the aforementioned low-temperature region is as follows: The front half of the extruder absorbs heat during polymer melting, but since it is the location of raw material mixing, it is also considered a region where heat is released due to friction. Therefore, by creating a direction that lowers the temperature in the upstream region, which is the raw material inlet side, thermal degradation of the resin composition is suppressed, which is believed to contribute to an improvement in the Charpy impact value.

[0168] When mixing is performed at the above shear rate, there is a tendency for the actual barrel temperature to rise compared to the set temperature due to shear exothermic reaction.

[0169] The highest temperature of the compound being mixed in the barrel in the aforementioned low-temperature region is preferably 250°C or higher and lower than 320°C, more preferably 270°C or higher and lower than 315°C, and even more preferably 280°C or higher and lower than 310°C.

[0170] The maximum temperature of the compound being mixed in the barrel can be in the aforementioned high-temperature region, preferably exceeding 315°C and below 400°C. From the viewpoint of effectively suppressing the deterioration of thermoplastic elastomers, a temperature of 315°C or higher and below 350°C is more preferred, and 315°C or higher and below 330°C is even more preferred.

[0171] Examples of extruders include a single-screw extruder with one screw and a twin-screw extruder with two screws disposed inside the barrel of the extruder. From the perspective of high mixing efficiency of all the compounding components of the aforementioned polyarylene sulfide resin composition, the method of using the aforementioned twin-screw extruder is preferred.

[0172] A twin-screw extruder with a kneading disc for mixing is particularly preferred.

[0173] The screw length (L) / screw diameter (D) ratio of the aforementioned extruder is preferably 40 to 200, more preferably 50 to 150, and even more preferably 70 to 120. If the L / D value is above the lower limit, temperature control during mixing is easier, and a polyarylene sulfide resin composition exhibiting excellent impact resistance can be readily obtained. If the L / D value is below the upper limit, the production efficiency of the resin composition is excellent.

[0174] The aforementioned operating conditions for melt compounding in the twin-screw extruder can be exemplified by a method in which melt compounding is performed when the ratio of resin component discharge rate (kg / h) to screw speed (rpm) (discharge rate / screw speed) is 0.001 to 0.01 (kg / h·rpm). By manufacturing under these conditions, the dispersibility of the aforementioned thermoplastic elastomer (B) in the aforementioned polyarylene sulfide resin (A) becomes better.

[0175] <Polyaryl sulfide resin (A)>

[0176] The polyarylene sulfide resin (A) used in the manufacturing method of the embodiment has a resin structure in which an aromatic ring is bonded to a sulfur atom as a repeating unit. Specifically, a resin having the structural part shown in the following structural formula (1) as a repeating unit can be cited.

[0177]

[0178] (where R is in the formula) 1 and R 2 Each can independently represent a hydrogen atom, or an alkyl, nitro, amino, phenyl, methoxy, or ethoxy group having 1 to 4 carbon atoms.

[0179] Here, regarding the structural portion shown in the aforementioned structural formula (1), from the perspective of the mechanical strength of the aforementioned polyarylether sulfide resin (A), R in this formula...1 and R 2 Hydrogen atoms are particularly preferred. In this case, examples of para-bonded structures shown in the following structural formula (2) and meta-bonded structures shown in the following structural formula (3) can be cited.

[0180]

[0181] Among these, from the viewpoint of the heat resistance and crystallinity of the aforementioned polyarylene sulfide resin (A), it is particularly preferred that the sulfur atom in the repeating unit is bonded to the aromatic ring in the para-bonded structure shown in the aforementioned structural formula (2).

[0182] In addition, the aforementioned polyarylene sulfide resin (A) may contain only the structural part shown in the aforementioned structural formula (1), or it may contain any one or more structural parts shown in the following structural formulas (4) to (7) in an amount of 30 mol% or less relative to the total amount of the structural parts shown in the aforementioned structural formula (1) being 100 mol%.

[0183]

[0184] From the perspective of heat resistance and mechanical strength of polyarylene sulfide resin (A), it is particularly preferred that the polyarylene sulfide resin (A) contains one or more of the structural parts shown in the above structural formulas (4) to (7) at a rate of 10 mol% or less. In the case where the polyarylene sulfide resin (A) contains one or more of the structural parts shown in the above structural formulas (4) to (7), the bonding mode can be any of random copolymers or block copolymers.

[0185] Examples of the aforementioned polyarylene sulfide resin (A) include cross-linked polyarylene sulfide resins and so-called linear polyarylene sulfide resins that are essentially linear in structure. From the perspective of easy reaction control and excellent industrial production efficiency, the aforementioned polyarylene sulfide resin (A) can be manufactured, for example, by reacting sodium sulfide with p-dichlorobenzene in amide solvents such as N-methylpyrrolidone and dimethylacetamide, or sulfone solvents such as sulfolane.

[0186] The aforementioned raw material, polyarylene sulfide resin (A), preferably comprises polyarylene sulfide resin particles (a) having a volume average particle size in the range of 1.0 mm to 3.0 mm. When the volume average particle size of the aforementioned polyarylene sulfide resin particles (a) is 1.0 mm or more, the polyarylene sulfide resin particles (a) are less prone to re-aggregation, making handling easier and facilitating uniform mixing with the thermoplastic elastomer particles (b). Furthermore, when the volume average particle size of the polyarylene sulfide resin particles (a) is 3.0 mm or less, they are easily and uniformly mixed with the thermoplastic elastomer particles (b), thus improving the strength enhancement effect provided to the aforementioned polyarylene sulfide resin composition. Among these, polyarylene sulfide resin particles (a) with a volume average particle size in the range of 1.5 mm to 2.5 mm are more preferred.

[0187] The aforementioned polyarylene sulfide resin particles (a) with a volume average particle size in the range of 1.0 mm to 3.0 mm can be manufactured, for example, by the following methods (1) or (2).

[0188] Method (1): After the polymerization of the aforementioned polyarylene sulfide resin (A) is completed, the reaction solution of the polyarylene sulfide resin (A) is cooled, and then washed multiple times with water or hot water and dried to obtain polyarylene sulfide resin (A) particles. The obtained polyarylene sulfide resin (A) particles are compressed and fixed by a press such as a belt press to obtain plate-shaped solids. Then, they are crushed to obtain polyarylene sulfide resin particles (a) with a volume average particle size in the range of 1.0 mm to 3.0 mm.

[0189] Method (2): After the polymerization of the aforementioned polyarylene sulfide resin (A) is completed, water is added to the polyarylene sulfide resin (A) in a state where the reaction solution of the polyarylene sulfide resin (A) is dissolved in the reaction solvent before cooling, to obtain polyarylene sulfide resin particles (a) with a volume average particle size in the range of 1.0 mm to 3.0 mm.

[0190] To further improve the reactivity of the aforementioned polyarylene sulfide resin (A), the polyarylene sulfide resin (A) preferably has a carboxyl group as a functional group with active hydrogen atoms in its molecular structure. Specifically, the content of the aforementioned carboxyl group in the polyarylene sulfide resin (A), as determined by neutralization titration, is preferably in the range of 10 μmol / g to 200 μmol / g, more preferably in the range of 10 μmol / g to 100 μmol / g. When the content of the carboxyl group in the polyarylene sulfide resin (A), as determined by the aforementioned neutralization titration, is 10 μmol / g or more, the reactivity of the aforementioned polyarylene sulfide resin (A) can be improved; on the other hand, when it is 200 μmol / g or less, the reactivity of the aforementioned polyarylene sulfide resin (A) becomes easier to control.

[0191] A method for manufacturing polyarylene sulfide resin (A) having a carboxyl group as a functional group with active hydrogen atoms in the aforementioned molecular structure can be described as follows: After the polymerization of the polyarylene sulfide resin (A) is completed, it is cooled to room temperature, washed with water, filtered to remove the polyarylene sulfide resin (A), treated with acid, and then washed with water. From the perspective of effectively reducing the amount of residual metal ions without decomposing the aforementioned polyarylene sulfide resin (A), the acid that can be used at this time is preferably acetic acid, hydrochloric acid, sulfuric acid, phosphoric acid, silicic acid, carbonic acid, oxalic acid, or propionic acid, and more preferably acetic acid or hydrochloric acid.

[0192] In a further embodiment, the melt viscosity of the polyarylene sulfide resin (A), measured at 300°C, is preferably in the range of 60 Pa·s to 240 Pa·s. When the melt viscosity of the polyarylene sulfide resin (A) is 60 Pa·s or higher, the toughness of the aforementioned polyarylene sulfide resin composition is improved; on the other hand, when the melt viscosity of the aforementioned polyarylene sulfide resin (A) is 240 Pa·s or lower, it becomes easier to suppress the exothermic reaction under high shear conditions in the aforementioned polyarylene sulfide resin composition. Among these, from the perspective of balancing the strength improvement effect brought about by the addition of the aforementioned thermoplastic elastomer (B) with the flowability of the aforementioned polyarylene sulfide resin composition, the melt viscosity of the aforementioned polyarylene sulfide resin (A) is particularly preferably in the range of 80 Pa·s to 180 Pa·s.

[0193] Here, the melt viscosity of the polyarylene sulfide resin (A) measured at 300°C refers to the melt viscosity (Pa·s) of the aforementioned polyarylene sulfide resin (A) measured using a high-low flow tester with an orifice of 10 mm in length and 1 mm in diameter, after holding at 300°C and a test load of 50 kg for 6 minutes.

[0194] In the resin composition of the embodiments, the mixing ratio of polyarylene sulfide resin (A) relative to the total mass (100% by mass) of the polyarylene sulfide resin composition is preferably 50-95% by mass, more preferably 70-90% by mass, and even more preferably 75-85% by mass. The upper and lower limits of the numerical range of the mixing ratio of polyarylene sulfide resin (A) in the examples above can be freely combined.

[0195] <Thermoplastic Elastomers (B)>

[0196] The thermoplastic elastomer (B) used in the manufacturing method of the embodiment is preferably one with a melting point of 300°C or below and exhibiting rubber elasticity at room temperature. The thermoplastic elastomer (B) is preferred because of its excellent effect on improving the impact resistance provided by the aforementioned polyarylene sulfide resin composition. Furthermore, from the perspective of excellent heat resistance, the thermoplastic elastomer (B) is preferably a polyolefin-based thermoplastic elastomer or a nitrile-based thermoplastic elastomer, and more preferably a polyolefin-based thermoplastic elastomer.

[0197] The aforementioned polyolefin-based thermoplastic elastomers can be exemplified by thermoplastic elastomers comprising structural units derived from olefins. As an olefin, α-olefins are preferred. From the perspective of excellent reactivity and compatibility with the aforementioned polyarylene sulfide resin (A), it is preferable that the polyolefin-based thermoplastic elastomer has one or more functional groups or structures selected from the group consisting of hydroxyl, carboxyl, amino, mercapto, epoxy, anhydride, ester, and isocyanate groups in its molecular structure. Among these, for further improvements in reactivity and compatibility with the aforementioned polyarylene sulfide resin (A) and for obtaining a uniformly mixed polyarylene sulfide resin composition, it is more preferable that the aforementioned polyolefin-based thermoplastic elastomer has carboxyl, epoxy, anhydride, or ester structures in its molecular structure.

[0198] Among them, glycidyl-modified polyolefin thermoplastic elastomers with epoxy groups in their molecular structure can impart excellent impact resistance. On the other hand, it is difficult to disperse this elastomer well in conventional resin compositions with increased mixing ratios.

[0199] In contrast, according to the manufacturing method of the embodiment, when the thermoplastic elastomer (B) is a glycidyl-modified polyolefin thermoplastic elastomer, its dispersibility can be well achieved, and a polyarylene sulfide resin composition exhibiting excellent impact resistance can be manufactured.

[0200] The aforementioned polyolefin-based thermoplastic elastomers having carboxyl, epoxy, anhydride, or ester structures in their molecular structure can be obtained, for example, by copolymerizing α-olefins with vinyl polymeric compounds having carboxyl, epoxy, anhydride, or ester structures in their molecular structure. Examples of the aforementioned α-olefins include 2- to 8-carbon α-olefins such as ethylene, propylene, and butene.

[0201] The aforementioned polyolefin thermoplastic elastomers having carboxyl groups in their molecular structure can be obtained, for example, by copolymerizing α,β-unsaturated carboxylic acids such as acrylic acid and methacrylic acid, or maleic acid, fumaric acid, itaconic acid, and other unsaturated dicarboxylic acids with 4 to 10 carbon atoms with the aforementioned α-olefins.

[0202] The aforementioned polyolefin thermoplastic elastomers with epoxy groups in their molecular structure can be obtained by copolymerizing glycidyl acrylate, glycidyl methacrylate, etc., with the aforementioned α-olefins.

[0203] The aforementioned polyolefin thermoplastic elastomers with anhydride structures in their molecular structure can be obtained by copolymerizing anhydrides of α,β-unsaturated dicarboxylic acids, such as maleic acid, fumaric acid, itaconic acid, and other unsaturated dicarboxylic acids with 4 to 10 carbon atoms, with the aforementioned α-olefins.

[0204] The aforementioned polyolefin thermoplastic elastomers with ester structures in their molecular structure can be obtained by copolymerizing the aforementioned α-olefins with alkyl esters of α,β-unsaturated carboxylic acids such as acrylates and methacrylates, maleic acid, fumaric acid, itaconic acid, and monoesters and diesters of other unsaturated dicarboxylic acids with 4 to 10 carbon atoms.

[0205] Alternatively, copolymers containing two or more of these functional groups or structures can be used. Preferred examples include terpolymers of α-olefins, acrylates, and glycidyl methacrylate.

[0206] The thermoplastic elastomer (B) preferably contains 40 to 95% by mass, more preferably 50 to 90% by mass, and even more preferably 60 to 80% by mass, of the total mass (100% by mass) of the structural units constituting the thermoplastic elastomer (B) relative to the total mass of the structural units constituting the thermoplastic elastomer (B).

[0207] The thermoplastic elastomer (B) preferably contains 0.1 to 30% by mass of structural units derived from glycidyl methacrylate relative to the total mass (100% by mass) of the structural units constituting the thermoplastic elastomer (B), more preferably 0.5 to 15% by mass, and even more preferably 1 to 7% by mass.

[0208] The thermoplastic elastomer (B) preferably contains 0.1 to 50% by mass of structural units derived from methyl acrylate relative to the total mass (100% by mass) of the structural units constituting the thermoplastic elastomer (B), more preferably 3 to 40% by mass, and even more preferably 10 to 35% by mass.

[0209] As an example of the aforementioned copolymer, one can cite the following: relative to the total mass (100% by mass) of the structural units constituting the thermoplastic elastomer (B),

[0210] Contains 0.1–30% by mass of structural units derived from glycidyl (meth)acrylate.

[0211] Contains 0.1 to 50% by mass of structural units derived from methyl acrylate.

[0212] As an example of the aforementioned copolymer, one can cite the following: relative to the total mass (100% by mass) of the structural units constituting the thermoplastic elastomer (B),

[0213] Contains 40–95% by mass structural units derived from α-olefins.

[0214] Contains 0.1–30% by mass of structural units derived from glycidyl (meth)acrylate.

[0215] Contains 0.1 to 50% by mass of structural units derived from methyl acrylate.

[0216] Next, copolymers of unsaturated nitriles and conjugated dienes can be cited as examples of the aforementioned nitrile thermoplastic elastomers. Examples of unsaturated nitriles include acrylonitrile or methacrylonitrile, and examples of conjugated dienes include 1,3-butadiene, 2-methyl-1,3-butadiene, 2,3-dimethyl-1,3-butadiene, and 1,3-pentadiene. Among these, acrylonitrile-butadiene copolymers are preferred, and hydrogenated nitrile thermoplastic elastomers, which improve heat resistance by hydrogenating part or all of the double bonds of the aforementioned conjugated dienes while maintaining the triple bonds of the nitrile groups, are even more preferred.

[0217] Furthermore, from the perspective of excellent reactivity and compatibility with the aforementioned polyarylene sulfide resin (A), the aforementioned hydrogenated nitrile thermoplastic elastomer preferably has one or more functional groups selected from the group consisting of vinyl, hydroxyl, carboxyl, acid anhydride, glycidyl, amino, isocyanate, mercapto, oxazoline, isocyanurate, and maleimide in its molecular structure. Among these, from the perspective of excellent heat resistance and reactivity, hydrogenated nitrile thermoplastic elastomers having carboxyl groups are particularly preferred.

[0218] In the manufacturing method of the embodiment, the thermoplastic elastomer (B) used as raw material is preferably thermoplastic elastomer particles (b) with a volume average particle size in the range of 0.1 mm to 3.0 mm. When the volume average particle size of the thermoplastic elastomer particles (b) is 0.1 mm or more, the specific surface area of ​​the thermoplastic elastomer particles (b) becomes smaller, the re-aggregation of the thermoplastic elastomer particles (b) is less likely to occur, the operation becomes easier, and the mixing of the aforementioned thermoplastic elastomer particles (b) in the specified mixing amount becomes easier. On the other hand, when the volume average particle size of the aforementioned thermoplastic elastomer particles (b) is 3.0 mm or less, it becomes easier to uniformly mix the aforementioned polyarylene sulfide resin (A), and the strength improvement effect provided to the polyarylene sulfide resin composition is well manifested. In terms of the balance between the ease of operation, ease of uniform mixing, and the improvement of impact resistance and flexural strength of the aforementioned thermoplastic elastomer particles (b), the volume average particle size of the aforementioned thermoplastic elastomer particles (b) is particularly preferably in the range of 0.3 mm to 2.0 mm.

[0219] The aforementioned thermoplastic elastomer particles (b) having a volume average particle size in the range of 0.1 mm to 3.0 mm can be manufactured by: finely cutting the thermoplastic elastomer particles having a volume average particle size exceeding 3.0 mm using a cutting machine; or by cryogenically pulverizing the aforementioned thermoplastic elastomer particles having a volume average particle size exceeding 3.0 mm. Cryogenic pulverization can be achieved by freezing with dry ice or liquid nitrogen, followed by pulverization using a conventional hammer mill, cutter mill, or stone mill. Of the aforementioned methods, cryogenic pulverization is preferred for easily manufacturing the aforementioned thermoplastic elastomer particles (b).

[0220] In the resin composition of the embodiments, the mixing ratio of thermoplastic elastomer (B) relative to the total mass (100% by mass) of the polyarylene sulfide resin composition is 5 to 30% by mass, preferably 10 to 28% by mass, and more preferably 15 to 25% by mass. The upper and lower limits of the numerical range of the mixing ratio of thermoplastic elastomer (B) in the examples above can be freely combined.

[0221] Furthermore, from another perspective, relative to the total content (100 parts by mass) of polyarylene sulfide resin (A) and thermoplastic elastomer (B) in the polyarylene sulfide resin composition, the mixing ratio of the aforementioned thermoplastic elastomer (B) is preferably 5 to 30 parts by mass, more preferably 10 to 28 parts by mass, and even more preferably 15 to 25 parts by mass.

[0222] The mixing ratio of the aforementioned thermoplastic elastomer (B) is above the aforementioned lower limit value, thereby effectively improving the impact resistance provided by the aforementioned polyarylene sulfide resin composition. The mixing ratio of the aforementioned thermoplastic elastomer (B) is below the aforementioned upper limit value, thereby effectively reducing the amount of gas generated during the molding of the aforementioned polyarylene sulfide resin composition.

[0223] Previously, when the content of thermoplastic elastomer (B) was above the aforementioned lower limit, it was difficult to achieve a high degree of dispersion of the elastomer component in the resin composition. The reasons for this are unclear, but it is believed that the higher the content ratio of the elastomer component, the more energy is required for dispersion. In addition, the contact frequency between the elastomers that begin to disperse increases.

[0224] In contrast, according to the manufacturing method of the embodiment, when the thermoplastic elastomer (B) is contained at or above the aforementioned lower limit value, its dispersibility can also be good, and a polyarylether sulfide resin composition exhibiting excellent impact resistance can be manufactured.

[0225] In addition to the polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B), the resin composition of the embodiments may contain any other ingredients in such a manner that the total content (mass%) of these ingredients does not exceed 100% by mass.

[0226] In addition to the aforementioned components, the resin composition of the embodiment may be further formulated with an epoxy silane coupling agent (C). Due to the excellent reactivity of the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) with the epoxy silane coupling agent, the composition is preferred from the perspective that the uniform dispersion of the aforementioned thermoplastic elastomer (B) is improved, the adhesion at the interface between the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) is improved, and the strength improvement effect of the aforementioned polyarylene sulfide resin composition becomes more significant.

[0227] The aforementioned epoxy silane coupling agent (C) is preferably a silane compound having an alkyl group having 1 to 4 carbon atoms as a straight-chain alkyl group, such as epoxy propoxyalkyl or 3,4-epoxycyclohexylalkyl, which is bonded to two or more methoxy and ethoxy groups on silicon atoms.

[0228] Specifically, examples of such epoxy silane coupling agents (C) include γ-epoxypropoxypropyltrimethoxysilane, β-(3,4-epoxycyclohexyl)ethyltrimethoxysilane, γ-epoxypropoxypropyltriethoxysilane, and epoxy silicone oils.

[0229] Examples of epoxy silicone oils include compounds with polyepoxyalkyl groups consisting of 2 to 6 carbon atom alkoxy groups as repeating units.

[0230] Among the aforementioned epoxy silane coupling agents (C), epoxy silane compounds, particularly those with excellent reactivity with the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B), are especially preferred.

[0231] The content of the aforementioned epoxy silane coupling agent (C), relative to the total mass of the aforementioned polyarylene sulfide resin composition, is preferably in the range of 0.1% to 5% by mass. When it is 0.1% by mass or more, the compatibility between the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) becomes good. When it is 5% by mass or less, the gas generated during the melt molding of the aforementioned polyarylene sulfide resin composition is reduced. Among these, from the perspective of the compatibility between the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) and the balance of the amount of gas generated during the melt molding of the aforementioned polyarylene sulfide resin composition, it is particularly preferred to be in the range of 0.1% to 2% by mass relative to the total mass of the aforementioned polyarylene sulfide resin composition.

[0232] In the method for manufacturing the polyarylene sulfide resin composition of the embodiments, in addition to the aforementioned compound, inorganic fillers may also be appropriately compounded. Examples of such inorganic fillers include fibrous inorganic fillers and non-fibrous inorganic fillers.

[0233] Examples of the aforementioned fibrous inorganic fillers include glass fibers, PAN-based or pitch-based carbon fibers, silica fibers, zirconium oxide fibers, boron nitride fibers, silicon nitride fibers, boron fibers, aluminum borate fibers, potassium titanate fibers, and inorganic fibrous materials of metals such as stainless steel, aluminum, titanium, copper, and brass, as well as organic fibrous materials such as aromatic polyamide fibers.

[0234] In addition, the aforementioned non-fibrous inorganic fillers include, for example, silicates such as mica, talc, wollastonite, sericite, kaolin, clay, bentonite, asbestos, aluminum silicate, zeolite, and pyrophyllite; carbonates such as calcium carbonate, magnesium carbonate, and dolomite; sulfates such as calcium sulfate and barium sulfate; metal oxides such as alumina, magnesium oxide, silicon dioxide, zirconium oxide, titanium dioxide, and iron oxide; glass beads; ceramic beads; boron nitride; silicon carbide; and calcium phosphate. These aforementioned fibrous inorganic fillers and the aforementioned non-fibrous inorganic fillers can be used individually or in combination of two or more. There is no particular limitation on the mixing time of these non-fibrous inorganic fillers, but it is preferable to mix them when dry-mixing the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) using the aforementioned Nota mixer.

[0235] Regarding the mixing ratio of the aforementioned polyarylene sulfide resin (A) and the aforementioned inorganic filler, from the viewpoint of the melt characteristics of the aforementioned polyarylene sulfide resin composition and the mechanical properties of its molded articles, the ratio of the former to the latter is preferably in the range of 30 parts by mass to 100 parts by mass / 70 parts by mass to 0 parts by mass. Furthermore, from the viewpoint of the mechanical properties required for the molded articles, the mixing ratio of the aforementioned fibrous inorganic filler and the aforementioned non-fibrous inorganic filler can be any mixing ratio, but the ratio of the former to the latter is preferably in the range of 20 parts by mass to 100 parts by mass / 80 parts by mass to 0 parts by mass.

[0236] In the method for manufacturing the polyarylene sulfide resin composition according to the embodiments, from the viewpoint that the dispersibility of the aforementioned fibrous filler is improved, it is particularly preferable that the aforementioned fibrous filler is fed into the extruder from the side feeder of the aforementioned twin-screw extruder. Regarding the location of the aforementioned side feeder, the ratio of the distance from the resin input section of the extruder to the side feeder to the total length of the screw of the aforementioned twin-screw extruder is preferably in the range of 0.1 to 0.6, and among these, it is particularly preferable to be in the range of 0.2 to 0.4.

[0237] Furthermore, in the manufacturing method of the embodiments, appropriate amounts of antioxidants, processing heat stabilizers, plasticizers, mold release agents, colorants, lubricants, weather stabilizers, foaming agents, rust inhibitors, waxes, and other additives may be added without impairing the effects of the present invention. There are no particular limitations on the timing of mixing these additives, but it is preferable to mix them when the aforementioned polyarylene sulfide resin (A) and the aforementioned thermoplastic elastomer (B) are dry-mixed using the aforementioned Nota mixer.

[0238] In a further embodiment of the manufacturing method, other resin components not belonging to polyarylene sulfide resin (A) and thermoplastic elastomer (B) may be further suitably blended according to the required characteristics. The timing of blending other resin components is not particularly limited, but it is preferable to blend them by dry-mixing the aforementioned raw material polyarylene sulfide resin (A) and the aforementioned raw material thermoplastic elastomer (B) using the aforementioned Nota mixer. Examples of resin components that can be used here include homopolymers or copolymers of monomers such as ethylene, butene, pentene, butadiene, isoprene, chloroprene, styrene, α-methylstyrene, vinyl acetate, vinyl chloride, acrylates, methacrylates, and (meth)acrylonitrile; polyurethanes; polyesters such as polybutylene terephthalate and polyethylene terephthalate; polyacetals; polycarbonates; polysulfones; polyallyl sulfones; polyethersulfones; polyphenylene ethers; polyetherketones; polyetheretherketones; polyimides; polyamide-imides; polyetherimides; silicone resins; epoxy resins; phenoxy resins; liquid crystal polymers; and homopolymers, random copolymers, block copolymers, and graft copolymers of polyaryl ethers.

[0239] The aforementioned polyarylene sulfide resin composition, thus melt-blended, can be manufactured, for example, in granule form. The granules of the aforementioned polyarylene sulfide resin composition are fed into a molding machine and melt-molded to obtain the desired molded article. Examples of melt-molding methods include injection molding, extrusion molding, and compression molding, and there are no particular limitations.

[0240] The thermoplastic elastomer (B) of the molded article of the aforementioned polyarylene sulfide resin composition thus obtained has excellent dispersibility and is obtained by melt-mixing a relatively large amount of the thermoplastic elastomer (B), thus exhibiting exceptionally good impact resistance.

[0241] The aforementioned polyarylene sulfide resin composition obtained according to the manufacturing method of the embodiment may be suitable for use in automotive parts, vehicle parts used as electrical or electronic components of automotive parts, or vehicle components.

[0242] Examples of vehicle components include drive system components (such as transmission gears, drive motor components, etc.), control components (PCU, etc.), cooling components (piping, valves, pump components, etc.) and battery components in the engine compartment.

[0243] According to the method for manufacturing the resin composition described in the embodiments, a polyarylene sulfide resin exhibiting excellent impact resistance can be manufactured.

[0244] As an indicator of impact resistance, the Charpy impact value of the molded article of the resin composition described in the embodiments can be used.

[0245] The Charpy impact value of the molded article of the resin composition can be determined by the following method.

[0246] The resin composition of the test object was used as the molding material and molded in an injection molding machine at a barrel temperature of 300°C and a mold temperature of 130°C to obtain a test piece with a length of 80 mm × width of 10.0 mm × thickness of 4.0 mm. Then, according to ISO 2818, the test piece was cut with a notch (B... N The Charpy impact value (kJ / m²) with a notch was obtained at 23°C according to ISO 179-1, based on the test results of 8 mm. 2 ).

[0247] The Charpy impact value of the molded article of the resin composition of the embodiment, measured according to ISO 179-1, at 23°C with notches, is preferably 40 kJ / m. 2 Above, and more preferably 50 kJ / m 2 The above, further optimized, is 60kJ / m 2 The above, further optimized, is 62kJ / m 2 The above, especially the preferred 64kJ / m 2 above.

[0248] There is no particular upper limit to the Charpy impact value mentioned above; for example, it could be 70 kJ / m³. 2 The following can be 69kJ / m 2 The following can be 68kJ / m 2 the following.

[0249] As an example of the above-mentioned numerical range, the Charpy impact value of the molded article of the resin composition of the embodiment, measured according to ISO 179-1, at 23°C with notches, can be 40 kJ / m. 2 Above and 70kJ / m 2 The following can be 50 kJ / m 2 Above and 70kJ / m 2 The following can be 60 kJ / m 2 Above and 69kJ / m 2 The following can be 62kJ / m 2 Above and 68kJ / m 2 The following can be 64kJ / m 2 Above and 68kJ / m 2 the following.

[0250] As one embodiment of the present invention, a resin composition is provided, which is a polyarylene sulfide resin composition containing a polyarylene sulfide resin (A) and a thermoplastic elastomer (B), wherein the content of the thermoplastic elastomer (B) is 5 to 30% by mass relative to 100% by mass of the polyarylene sulfide resin composition, and the Charpy impact value of the molded article of the resin composition, measured according to ISO 179-1, at 23°C with a notch, is 40 kJ / m. 2 above.

[0251] The resin composition of the embodiments can be manufactured according to the method for manufacturing the resin composition described above.

[0252] The Charpy impact value of the molded resin composition as an embodiment, measured according to ISO 179-1, at 23°C with a notch, is 40 kJ / m. 2 The values ​​above can be used to illustrate the values ​​in the examples above.

[0253] In addition, as an embodiment of the resin composition of the present invention, a polyarylene sulfide resin composition and a method for manufacturing the same are provided. The polyarylene sulfide resin composition is a polyarylene sulfide resin composition containing a polyarylene sulfide resin (A) and a thermoplastic elastomer (B). The content of the aforementioned thermoplastic elastomer (B) is 5 to 30% by mass relative to 100% by mass of the aforementioned polyarylene sulfide resin composition, and the thermoplastic elastomer (B) has a dispersion structure with an average dispersion diameter of 0.20 μm or less.

[0254] The resin composition can be manufactured according to the method for manufacturing the resin composition described in the embodiments.

[0255] The average dispersion diameter of the thermoplastic elastomer (B) in the resin composition of the embodiment is preferably 0.20 μm or less, more preferably 0.01 μm or more and 0.18 μm or less, and even more preferably 0.05 μm or more and 0.17 μm or less.

[0256] Resin compositions with an average dispersion diameter of thermoplastic elastomer (B) within the above-mentioned numerical range tend to exhibit excellent impact resistance and are preferred.

[0257] Example

[0258] The invention will then be described in further detail with the presentation of embodiments, but the invention is not limited to the following embodiments.

[0259] <Ingredients>

[0260] ·Polyphenylene sulfide (manufactured by DIC Co., Ltd., MA-520)

[0261] • Polyethylene elastomer A (manufactured by Sumitomo Chemical Co., Ltd., Bondfast BF-7L)

[0262] • Polyethylene-based elastomer B (manufactured by Sumitomo Chemical Co., Ltd., Bondfast BF-E)

[0263] • Polyethylene-based elastomer C (manufactured by Sumitomo Chemical Co., Ltd., Bondfast BF-7M)

[0264] The ratios of the monomers used as raw materials for the above-mentioned polyethylene elastomers are shown below. The values ​​are the amount (mass%) of each monomer relative to the total mass of the monomers used as raw materials for the polyethylene elastomers.

[0265] [Table 1]

[0266] Elastomer A Elastomer B Elastomer C glycidyl dimethacrylate 3 12 6 Methyl acrylate 27 - 27 ethylene 70 88 67

[0267] As described in the above embodiments, the results of support vector regression analysis, aimed at producing resin compositions with good Charpy impact values, suggest that manufacturing conditions with higher elastomer blending ratios and compound temperatures IR1 and IR2 in the range of 300–315°C are favorable. Based on the above information, resin compositions of the various embodiments and comparative examples were manufactured under the following conditions.

[0268] <Preparation of Resin Compositions>

[0269] [Example 1]

[0270] The polyphenylene sulfide resin (80 parts by weight) and polyethylene elastomer A (20 parts by weight) are mixed and fed into a twin-screw extruder.

[0271] Figure 8 This is a schematic diagram illustrating the configuration of the twin-screw extruder used. The twin-screw extruder uses a screw diameter of 15 mm, a tip gap of 0.25 mm, a barrel length L to barrel inner diameter D ratio of L / D = 90, and 15 barrels (the barrels divided into 15 equal parts are designated as C1 to C15 from the raw material supply side to the discharge side). The screw configuration forms the kneading zone at positions C4, C7, C10, and C12.

[0272] Under the conditions of set temperature (°C) of the barrels for C2 to C15 as shown in Table 2 [set temperature of the heater (box type) covering the barrel. "Room temperature" for C1 is not set.] and screw speed, the above-mentioned polyphenylene sulfide resin and polyethylene elastomer A were melt-blended, and the filament produced from the mold was cooled and cut to obtain granules of the resin composition of Example 1.

[0273] [Table 2]

[0274]

[0275] [Example 2]

[0276] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 1 was performed to obtain the granules of the resin composition of Example 2.

[0277] [Example 3]

[0278] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 1 was performed to obtain the granules of the resin composition of Example 3.

[0279] [Example 4]

[0280] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 1 was performed to obtain the granules of the resin composition of Example 4.

[0281] [Comparative Example 1]

[0282] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 1 was performed to obtain the granules of the resin composition of Comparative Example 1.

[0283] [Comparative Example 2]

[0284] The screw speed was changed to the speed recorded in Table 2, and the same operation as in Comparative Example 1 was performed to obtain the granules of the resin composition of Comparative Example 2.

[0285] [Example 5]

[0286] The raw materials were changed to polyphenylene sulfide resin (80 parts by mass) and polyethylene elastomer B (20 parts by mass). The screw speed and barrel temperature were changed to the settings recorded in Table 2. Otherwise, the same operation as in Example 1 was performed to obtain the granules of the resin composition of Example 5.

[0287] [Example 6]

[0288] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 5 was performed to obtain the granules of the resin composition of Example 6.

[0289] [Example 7]

[0290] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 5 was performed to obtain the granules of the resin composition of Example 7.

[0291] [Example 8]

[0292] The set temperature of the barrel was changed to the set temperature recorded in Table 2. Otherwise, the same operation as in Example 5 was performed to obtain the granules of the resin composition of Example 8.

[0293] [Comparative Example 3]

[0294] The set temperature of the barrel was changed to the set temperature as described in Table 2. Otherwise, the same operation as in Example 5 was performed to obtain the granules of the resin composition of Comparative Example 3.

[0295] [Comparative Example 4]

[0296] The screw speed was changed to the speed recorded in Table 2, and the same operation as in Comparative Example 3 was performed to obtain the granules of the resin composition of Comparative Example 4.

[0297] [Example 9]

[0298] The raw materials were changed to polyphenylene sulfide resin (80 parts by weight) and polyethylene elastomer C (20 parts by weight), and the same operation as in Example 1 was performed to obtain the granules of the resin composition of Example 9.

[0299] [Comparative Example 5]

[0300] The set temperature of the barrel was changed to the set temperature as described in Table 2. Otherwise, the same operation as in Example 9 was performed to obtain the granules of the resin composition of Comparative Example 5.

[0301] <Measurement>

[0302] (Measurement of the temperature of the mixture inside the barrel during melt mixing)

[0303] When using a twin-screw extruder for melt mixing, infrared thermometers were installed at positions C5, C8, C11, and C13 in the barrels divided into 15 equal parts. Temperatures inside the barrel were measured (°C) at four points from the screw root to the screw tip (referred to as IR1, IR2, IR3, and IR4, respectively) as each barrel passed through. (Refer to...) Figure 8 ).

[0304] (Charpy Impact Value)

[0305] The resin compositions obtained in the various examples and comparative examples were used as molding materials and molded in an injection molding machine at a barrel temperature of 300°C and a mold temperature of 130°C to obtain test pieces with a length of 80 mm × width of 10.0 mm × thickness of 4.0 mm. Then, according to ISO 2818, the test pieces were cut with notches (B... NThe Charpy impact value (kJ / m²) with a notch was obtained at 23°C according to ISO 179-1, based on the test results of 8 mm. 2 ). Figure 9 The shape of the test piece used in the Charpy impact value determination is shown in the figure.

[0306] (Dispersion diameter of the elastomer)

[0307] The resin compositions obtained in the various examples and comparative examples were used as molding materials and molded into multi-object test pieces according to ISO 3167 type A using an injection molding machine. The multi-object test pieces were then cut at the center along their length, the cut surfaces were ground, impregnated in xylene, and subjected to ultrasonic treatment at 50°C to remove the elastomer dispersion in the cross-section using xylene extraction. After drying at 130°C for 2 hours, the cross-section was observed using SEM to obtain an image. The areas where the elastomer was removed became the porous phase, represented by low-brightness black circles. Using image analysis software, the equivalent diameter of the circular area of ​​all (n=300 or more) of these black circles identified within the image field of view was measured (the value obtained by calculating the diameter of a perfect circle corresponding to the area of ​​the circle). The average value obtained by dividing by the number of circles was taken as the average dispersion diameter (μm) of the elastomer.

[0308] (Melt viscosity)

[0309] In a flow tester with a barrel temperature of 300°C, an orifice length of 10 mm, and an orifice diameter of 1 mm, granules of the resin compositions obtained in each example and comparative example were added, preheated for 6 minutes, and then the melt viscosity was measured under a test load of 50 kgf.

[0310] The above measurement results are shown in Tables 3 to 5.

[0311] [Table 3]

[0312]

[0313] [Table 4]

[0314]

[0315] [Table 5]

[0316]

[0317] For Comparative Example 2, when the compounding was carried out under conditions where the screw speed was as low as 300 rpm, the average dispersion diameter of the elastomer in the resin composition was also larger, resulting in a poor Charpy impact value.

[0318] In Comparative Example 1, the screw speed was 1000 rpm and the average dispersion diameter of the elastomer was the smallest, indicating an improvement in the dispersion state of the elastomer in the resin composition.

[0319] In the corresponding embodiments, as described above, a dataset containing all control variables, measured variables, and physical property variables is prepared. Based on the results implied by regression analysis using a support vector machine algorithm to calculate the importance using a random forest algorithm and the items with higher importance, the resin compositions of each embodiment are manufactured using specified manufacturing conditions for the elastomer blending ratio, the screw speed of the twin-screw extruder, and the compound temperature in the barrel of the extruder.

[0320] As a result, molded articles of resin compositions with extremely high Charpy impact values ​​were obtained, confirming the rationale for developing high-performance materials using machine learning.

[0321] In Examples 1 and 2, by lowering the set temperature of the barrels at positions C2 to C7 or C2 to C8 corresponding to IR1 to IR2, molded articles of resin compositions with excellent Charpy impact values ​​were obtained compared to Comparative Example 1, which set the barrel temperature at all positions (C2 to C15) to 300°C.

[0322] In addition, in Example 3, by lowering the set temperature of the barrels at positions C11 to C15 corresponding to IR3 to IR4, and in Example 4, by lowering the set temperature of the barrels at positions C7 to C15 corresponding to IR2 to IR4, similarly to Examples 1 and 2, a molded article of a resin composition with excellent Charpy impact value was obtained compared to Comparative Example 1.

[0323] Interestingly, when comparing Examples 1 and 2 with Examples 3 and 4, Examples 1 and 2, which lowered the temperatures near the locations of IR1 and IR2, further improved the Charpy impact values. This is consistent with analytical results showing that IR1 and IR2 have high importance calculated using an algorithm using random forest.

[0324] In Examples 5-8 and Comparative Examples 3 and 4, which were evaluated with different types of elastomers, the same tendency as in Examples 1-4 and Comparative Examples 1 and 2 was also confirmed.

[0325] In addition, in Example 9 and Comparative Example 5, the same evaluation was performed using an elastic body C that was not used for machine learning, but like the other results, results that can be applied to machine learning are shown.

[0326] If we refer to the melt viscosity value, it tends that the smaller the average dispersion diameter of the elastomer, the greater the dispersibility and the better the melt viscosity. Furthermore, it is believed that reducing the set temperature of the barrel, thereby suppressing the thermal degradation of the elastomer, also leads to an improvement in melt viscosity.

[0327] This demonstrates that the improved conditions obtained through machine learning are highly effective in manufacturing resin compositions with high blending ratios of thermoplastic elastomers, where dispersibility has been difficult to improve in the past. It is speculated that by melt-blending the blending components under high-speed shear conditions, the average dispersion diameter of the thermoplastic elastomer is reduced, improving dispersibility. Furthermore, by further reducing the measured temperature of the blend during melt-blending, the dispersion efficiency may be improved due to the reduced flowability of the polyarylene sulfide resin, further enhancing the dispersibility of the thermoplastic elastomer and suppressing the thermal decomposition of the polyarylene sulfide resin and the thermoplastic elastomer. This results in resin compositions and their molded products with extremely high Charpy impact values.

[0328] Each embodiment and its combination is an example, and features may be added, omitted, substituted, and other changes may be made without departing from the spirit of the invention. Furthermore, the invention is not limited to the embodiments, but only to the scope of the claims.

[0329] Explanation of reference numerals in the attached figures

[0330] 10…Twin-screw extruder, 11…Drive unit, 12…Feeder, 13…Barrel, 14…Screw, 19…Die, 20…Machine learning algorithm, 30…Storage device, DS…Dataset, CD…Manufacturing condition data, MD…Physical property measurement data, HC…High importance items, IR…Infrared temperature sensor, IR1…First infrared temperature sensor, IR2…Second infrared temperature sensor, IR3…Third infrared temperature sensor, IR4…Fourth infrared temperature sensor.

Claims

1. A method for determining manufacturing conditions of a polyarylene sulfide resin composition, which executes a machine learning algorithm using a data set containing manufacturing condition data and physical property measurement data, the manufacturing condition data containing at least, as manufacturing condition items, a compounding component of a polyarylene sulfide resin composition, a mixing condition, and a temperature of a compound during melt kneading, the physical property measurement data containing at least, as a property value item, an impact resistance of a polyarylene sulfide resin composition manufactured according to the manufacturing condition shown by the manufacturing condition data, thereby determining, as items in which an importance degree of a change in a property value for a property improvement target item is high, among a plurality of items contained in the manufacturing condition data and the physical property measurement data.

2. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 1, wherein the machine learning algorithm is an algorithm using a random forest, and an importance degree of each of a plurality of items contained in the manufacturing condition data and the physical property measurement data is calculated, thereby determining items in which the importance degree of the change in the property value for the property improvement target item is high.

3. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 2, wherein the manufacturing condition data contains a first manufacturing condition item and a second manufacturing condition item as the manufacturing condition items, the first manufacturing condition item being a control object of a manufacturing apparatus of a polyarylene sulfide resin composition, the second manufacturing condition item not being a control object of the manufacturing apparatus.

4. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 3, wherein the second manufacturing condition item contains an internal temperature of each of a plurality of portions of a kneading portion of the manufacturing apparatus that kneads a polyarylene sulfide resin.

5. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 4, wherein the internal temperature of each of the plurality of portions of the kneading portion contained in the second manufacturing condition item is higher in the importance degree on an upstream side where a raw material of a polyarylene sulfide resin composition is fed than on a downstream side where a polyarylene sulfide resin composition after kneading is extruded in the kneading portion.

6. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 2, wherein the machine learning algorithm is executed using, as new target variables, items in which the calculated importance degree is higher, thereby determining items in which the importance degree of a change in a property value for the new target variables is high, and a method for determining the items in which the importance degree is higher has a configuration in which the first three of the calculated importance degrees are determined as items in which the importance degree is higher.

7. The method for determining manufacturing conditions of a polyarylene sulfide resin composition according to claim 2, wherein ​ ​ ​ ​ ​ ​ ​ performing a regression operation using the data set with the calculated items of higher importance as an analysis axis, thereby estimating a correspondence between a change in a characteristic value of the items of higher importance and a change in a characteristic value of the target variable, The method for determining the items of higher importance is configured to determine the three highest calculated importance values as the items of higher importance.

8. A method for producing a polyarylene sulfide resin composition, which is a method for producing a polyarylene sulfide resin composition containing a polyarylene sulfide resin (A) and a thermoplastic elastomer (B), The compounding ratio of the thermoplastic elastomer (B) is 5 to 30% by mass relative to the total mass of the polyarylene sulfide resin composition, The production method includes a step of melt-kneading a raw polyarylene sulfide resin (A) and a raw thermoplastic elastomer (B) using an extruder, The shear rate generated by the screw rotation relative to the inner wall of the barrel of the extruder is 1000 to 6500 s -1 , The set temperature of the cylinder is lower than 300°C in a region of a length of 3 / 15 or more of the total length of the cylinder.

9. The method of producing a resin composition according to claim 8, wherein The thermoplastic elastomer (B) is a polyolefin-based thermoplastic elastomer.

10. The method of producing a resin composition according to claim 9, wherein The thermoplastic elastomer (B) is a glycidyl-modified polyolefin-based thermoplastic elastomer.

11. The method for producing a resin composition according to claim 9, wherein the thermoplastic elastomer (B) is a polyolefin-based thermoplastic elastomer. The thermoplastic elastomer (B) is a polyolefin-based thermoplastic elastomer. contains 0.1 to 30% by mass of a structural unit derived from glycidyl (meth)acrylate, contains 0.1 to 50% by mass of a structural unit derived from methyl acrylate.

12. The method for producing a resin composition according to claim 8, which has a dispersed structure in which the average dispersed diameter of the thermoplastic elastomer (B) is 0.20 μm or less.

13. The method for producing a resin composition according to claim 8, wherein the thermoplastic elastomer (B) is a polyolefin-based thermoplastic elastomer. The notched Charpy impact value at 23°C of a molded article of the resin composition, measured according to ISO 179-1, is 40 kJ / m 2 The above.

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