Material for injection molding and selection method of resin thereof

Abstract

To provide a material having high impact resistance.SOLUTION: A material for injection molding includes cellulose and resin, in which an affinity parameter Ln (gamma) of the resin and water at 25°C is -5 or less that is calculated using COSMO-RS method.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a material for injection molding and a method for selecting a resin therefor. [Background technology]

[0002] Fiber-reinforced resins, in which reinforcing fibers such as glass fibers, carbon fibers, and cellulose fibers are blended into resins to improve the mechanical properties of resin products, have been known. For example, Patent Document 1 discloses a resin molded product having a thickness of 0.1 mm or more, which is made of a cellulose fiber-dispersed resin composite material obtained by dispersing cellulose fibers in a resin, in which the cellulose fiber content is 1% by mass or more and less than 70% by mass, and in which LL and LN, which are length-weighted average fiber length and number-average fiber length of the cellulose fibers measured under the following measurement conditions, satisfy predetermined conditions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-193263 Summary of the Invention [Problem to be solved by the invention]

[0004] However, mixing cellulose does not necessarily improve the mechanical properties of the composite material, and depending on the type of resin combined with cellulose, the mechanical properties of the composite material may not improve. [Means for solving the problem]

[0005] The injection molding material of the present invention contains cellulose and a resin, and has an affinity parameter Ln(gamma) between the resin and water at 25°C, calculated using the COSMO-RS method, of -5 or less.

[0006] The method for selecting a resin for injection molding material of the present invention comprises the following steps: a first step of setting a target value for the Charpy impact strength of the injection molding material; a second step of obtaining the water content of the cellulose to be included in the injection molding material; and a third step of outputting a resin that satisfies the target value for Charpy impact strength based on the water content of the cellulose and an affinity parameter Ln(gamma), where the affinity parameter Ln(gamma) is the affinity parameter Ln(gamma) at 25°C calculated using the COSMO-RS method between the resin and water. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram showing the activity coefficient of H2O in each resin. [Figure 2] An example of data is shown below in which the resin composition and its Ln(gamma), the water content and mixing ratio of cellulose, and the Charpy strength are recorded in correspondence with each other. [Figure 3] 1 is a block diagram showing a selection device according to an embodiment of the present invention; [Figure 4A] 10 is a flowchart illustrating an example of processing according to the present embodiment. [Figure 4B] 10 is a flowchart illustrating an example of processing according to the present embodiment. [Figure 4C] 10 is a flowchart illustrating an example of processing according to the present embodiment. [Figure 5] 1 is a table showing data from an example. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, an embodiment of the present invention (hereinafter referred to as "the present embodiment") will be described in detail with reference to the drawings as necessary, but the present invention is not limited to this, and various modifications are possible without departing from the spirit of the present invention. In the drawings, the same elements are given the same reference numerals, and redundant explanations will be omitted. Furthermore, positional relationships such as up, down, left, and right will be based on the positional relationships shown in the drawings unless otherwise specified. Furthermore, the dimensional ratios of the drawings are not limited to those shown in the drawings.

[0009] 1. How to select resin for injection molding The method for selecting a resin for injection molding material in this embodiment includes a first step of setting a target value for the Charpy impact strength of the injection molding material, a second step of obtaining the water content of the cellulose to be included in the injection molding material, and a third step of outputting a resin that satisfies the target value for Charpy impact strength based on the water content of the cellulose and an affinity parameter Ln(gamma), where the affinity parameter Ln(gamma) is the affinity parameter Ln(gamma) at 25°C calculated using the COSMO-RS method between the resin and water.

[0010] In recent years, there has been a demand for environmentally friendly injection molding materials, and cellulose has been considered as a filler to be added to resins. It is generally said that environmentally friendly resin materials have inferior mechanical strength and impact resistance compared to petroleum-derived resin materials. In impact tests, the interface between the resin and filler is the starting point of fracture, so high adhesive strength is required at the contact surface between the resin and filler. To develop environmentally friendly resin materials with excellent impact resistance, it is desirable to select an appropriate resin for the filler and increase this adhesive strength.

[0011] Therefore, in the method for selecting a resin for injection molding material of this embodiment, the surface condition of the cellulose filler and the affinity of the cellulose surface condition with various resins are evaluated, and a resin that meets the target value of Charpy impact strength is output.

[0012] 1.1. First Step The first step is to set a target value for the Charpy impact strength of the injection molding material. The target value for Charpy impact strength may be specified as an absolute value. In this case, a resin that satisfies a Charpy impact strength equal to or greater than a specific strength is output in the third step. The target value for Charpy impact strength may also be specified as a relative value. In this case, the Charpy impact strength of the resin before mixing with the cellulose filler may be set to, for example, 100%, and the Charpy impact strength of the composite material after mixing with cellulose may be specified. In this case, a resin that satisfies a specific rate of increase in Charpy impact strength is output in the third step.

[0013] 1.2. Second Step The second step is to obtain the moisture content of the cellulose to be included in the injection molding material. In this embodiment, this moisture content is a value related to the surface state of the cellulose. Cellulose has hydroxyl groups and may be partially hydrated even when in a dry state. This hydrated moisture is included in the definition of the moisture content.

[0014] That is, even for the same cellulose, the balance of hydrophilicity and hydrophobicity on the surface can differ depending on the degree of hydration. The balance of hydrophilicity and hydrophobicity on the surface affects whether the resin mixed into the composite can approach the cellulose surface. When the resin can approach the cellulose surface, van der Waals forces act between the cellulose and resin, resulting in high adhesive strength and high Charpy impact strength. On the other hand, when the resin cannot approach the cellulose surface, adhesive strength between the cellulose and resin is not exerted, resulting in low Charpy impact strength.

[0015] The method for measuring the water content is not particularly limited, but may be defined as the weight loss rate when cellulose is kept at 150° C. for 48 hours, for example.

[0016] 1.3. Third Step The third step is to output a resin that satisfies the target value of Charpy impact strength based on the water content of the cellulose and the affinity parameter Ln(gamma), which is the affinity parameter Ln(gamma) between the resin and water at 25°C calculated using the COSMO-RS method.

[0017] The affinity parameter Ln(gamma) represents the natural logarithm of the activity coefficient γ. In this embodiment, the first step to obtain Ln(gamma) is to calculate the screening charge σ on the surface of the molecule by quantum chemical calculation based on the density functional theory. Next, the frequency distribution function of the screening charge σ, σ profile, p x From (σ), the activity coefficient γ of molecule X is calculated based on statistical mechanics using the COSMO-RS method to obtain Ln(gamma). The software used in each step is not particularly limited, but preferably, TURBOMOLE (COSMOlogic) is used in the first step, and COSMOtherm (COSMOlogic) is used in the next step.

[0018] For example, Figure 1 shows the activity coefficient of HO in each resin. Here, PBS stands for polybutylene succinate, PET stands for polyethylene terephthalate, PLA stands for polylactic acid, PS stands for polystyrene, and PP stands for polypropylene. The lower the Ln(gamma) value, the better the affinity with water.

[0019] Molecular simulations were performed to examine the affinity of polylactic acid (PLA) or polypropylene (PP) with cellulose of a given water content. The results showed that when polypropylene has low water affinity, water remains between the cellulose and polypropylene, preventing the polypropylene from approaching the cellulose sufficiently. On the other hand, when polylactic acid has high water affinity, even if water molecules are present near the cellulose surface, the water molecules penetrate the polylactic acid, bringing the polylactic acid and cellulose into sufficient proximity. This suggests that using a low water affinity parameter Ln(gamma) rather than a high one allows cellulose to approach sufficiently, allowing van der Waals forces to act.

[0020] Model The output of a resin that meets the target value of Charpy impact strength based on the affinity parameter Ln(gamma) can be realized by a learning model. Such a model can be created using training data such as that shown in Figure 2. Figure 2 records the resin composition, its Ln(gamma), the water content and mixing ratio of cellulose, and the Charpy impact strength in correspondence with each other.

[0021] By training a model based on data on the Charpy impact strength when a specific resin is mixed with a specific cellulose, it is possible to output a resin that meets the target value for Charpy impact strength based on the water content of the cellulose and the affinity parameter Ln(gamma) of each resin.

[0022] The model can also take a specific Charpy impact strength as input and output a combination of a specific resin and a specific cellulose that can achieve that Charpy impact strength.

[0023] Additionally, the model can take a specific resin and a specific cellulose as input and output an estimate of Charpy impact strength.

[0024] As shown in Figure 2, the resin may be a single type such as PLA, or may contain two or more types consisting of 50% PLA and 50% PP. By including the Charpy impact strength when multiple resins are mixed in any ratio in the training data, the method for selecting a resin for injection molding material can output not only a single resin but also a combination of multiple resins.

[0025] Furthermore, when the affinity parameters Ln(gamma) deviate to a certain degree, such as PLA and PP, the resins themselves may not be compatible. Therefore, in the third step, combinations of resins with affinity parameters Ln(gamma) that deviate to a certain degree may be excluded from the output results.

[0026] Furthermore, in the third step, the melting point of the resin may be further taken into consideration. When mixing the resin and cellulose, the resin is heated to near its melting point, but if the heating temperature is too high, the cellulose may deteriorate and its function as a filler may be reduced. Therefore, it is preferable to prioritize resins with low melting points in the third step. From this perspective, the resin with the lowest melting point may be output in the third step. Note that when two or more resins are included, the melting point may be the melting point of the resin with the lowest melting point, or the melting point of a resin alloy in which two or more resins are mixed.

[0027] 1.3.2.COSMO-RS Method

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[0028]

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[0029] In the above calculations, the following literature can be referenced for calculating activity coefficients. The software used in the COSMO-RS calculations is BIOVIA COSMOtherm 2023 (parameter set: BP_TZVPD_FINE_23.ctd). ·Klamt, AJ Phys. Chem. 99, 2224 (1995). ·Klamt, A.; Jonas, V.; Burger, T.; Lohrenz, JCJ Phys. Chem. A 102, 5074 (1998). ·Eckert, F. and A. Klamt, AIChE Journal, 48, 369 (2002). Furthermore, the combinatorial term due to the free volume in the resin can be incorporated into the chemical potential by the method of Elbro described in the following document: ·Elbro, HS; Fredenslund, A.; Rasmussen, PA Macromolecules 23, 4707 (1990).

[0030] 2.Selection device The selection device 100 is an information processing device implemented by a selection program, and may execute processing in response to a processing request from a user via the communication interface 120 and the network N. For example, the selection device 100 acquires information on the target Charpy impact strength value and the water content of cellulose from user input, etc. Then, based on this information, the selection device 100 may estimate and output a resin that can achieve the target Charpy impact strength value in combination with the cellulose.

[0031] The selection device 100 is an information processing device used by a user who executes the selection process, and may be, for example, a computer, a smartphone, a tablet terminal, a personal computer, or the like.

[0032] Hereinafter, the hardware configuration and functional configuration of the selection device 100 will be described with reference to FIG. 3, and then each control will be described in detail in association with the functional configuration of the selection device 100.

[0033] As shown in FIG. 1B, the selection device 100 includes, for example, a processor 110, a communication interface 120, an input / output interface 130, a memory 140, a storage 150, and one or more communication buses 160 for interconnecting these components.

[0034] The processor 110 executes processes, functions, or methods implemented by code or instructions included in a program stored in the storage 150. The processor 110 may include, for example and without limitation, one or more central processing units (CPUs), MPUs, GPUs, etc., and may implement each process, function, or method disclosed in each embodiment by a logic circuit (hardware) formed in an integrated circuit or the like, or a dedicated circuit.

[0035] As shown in FIG. 1B, the processor 110 of this embodiment may be configured to function as an acquisition unit 111 and an output unit 112.

[0036] The communication interface 120 transmits and receives various data to and from other devices via the network N. The communication may be performed either wired or wirelessly, and any communication protocol may be used as long as mutual communication is possible. For example, the communication interface 120 may be implemented as hardware such as a network adapter, various communication software, or a combination of these.

[0037] Network N may be, by way of example and not limitation, an ad-hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a cellular network, Integrated Service Digital Networks (ISDNs), wireless LANs, Long Term Evolution (LTE), Code Division Multiple Access (CDMA), Bluetooth, satellite communications, etc., or any combination thereof. A network may include one or more networks.

[0038] The input / output interface 130 includes an input device for inputting various operations to the selection device 100, and an output device for outputting processing results processed by the selection device 100. For example, the input / output interface 130 includes information input devices such as a keyboard, a mouse, and a touch panel, and information output devices such as a display. Note that the selection device 100 may accept a predetermined input or execute a predetermined output by connecting an external input / output interface 130.

[0039] Memory 140 temporarily stores programs loaded from storage 150 and provides a working area for processor 110. Memory 140 also temporarily stores various data generated while processor 110 is executing the programs. Memory 140 may be, for example, a high-speed random access memory such as a DRAM, an SRAM, a DDR RAM, or another random access solid-state storage device, or a combination of these.

[0040] The storage 150 stores programs, each functional unit, and various data. The storage 150 may be, for example, one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or nonvolatile memories such as other nonvolatile solid-state storage devices, or a combination of these. Another example of the storage 150 is one or more storage devices installed remotely from the processor 110. The storage 150 may also store the training data 151.

[0041] The communication bus 160 is not particularly limited as long as it is a known dedicated communication path for exchanging data, control information, and the like between hardware configurations.

[0042] 4A, the acquisition unit 111 acquires the target value of the Charpy impact strength of the injection molding material and the water content of the cellulose contained in the injection molding material from the user (S11, S12). Then, the output unit 112 outputs a resin that satisfies the target value of the Charpy impact strength based on the water content of the cellulose and the affinity parameter Ln(gamma) (S13).

[0043] In this case, the output unit 112 may output multiple Charpy impact strengths of a mixture of cellulose and a resin having the affinity parameter Ln(gamma) in a regular or irregular manner based on the moisture content of the cellulose and the affinity parameter Ln(gamma), and output a resin that satisfies the target value from among the multiple Charpy impact strengths.

[0044] As described above, the present embodiment is not limited to outputting resins that satisfy a target value for Charpy impact strength. For example, as shown in FIG. 4B , the acquisition unit 111 may acquire a specific Charpy impact strength (S21), and the output unit 112 may output a combination of a specific resin and a specific cellulose that can achieve that Charpy impact strength (S22). In this case, the output unit 112 may output the water content of the cellulose and the blend ratio of the cellulose that can achieve that Charpy impact strength.

[0045] Furthermore, for example, as shown in FIG. 4C, the acquisition unit 111 may acquire a specific resin and a specific cellulose (S31), and the output unit 112 may output an estimated value of the Charpy impact strength thereof (S32).

[0046] 3. Materials for injection molding The injection molding material of this embodiment contains cellulose and a resin, and has an affinity parameter Ln(gamma) between the resin and water at 25°C, calculated using the COSMO-RS method, of -5 or less.

[0047] There are no particular limitations on the cellulose, so long as it is a conventionally known cellulose, and cellulose with any water content can be used.

[0048] The resin is not particularly limited, but examples thereof include polybutylene succinate, polyethylene terephthalate, polylactic acid, polystyrene, and polypropylene.

[0049] The affinity parameter Ln(gamma) at 25° C. calculated using the COSMO-RS method between the resin and water is −5 or less, preferably −30 to −7.5, and more preferably −20 to −10.

[0050] Furthermore, when multiple types of resins are included, the difference between the maximum affinity parameter Ln(gamma) and the minimum affinity parameter Ln(gamma) among those multiple resins is preferably 0 to 10, 0 to 7.5, 0 to 5.0, or 0 to 2.5.

[0051] The content of the resin is preferably 15 to 60 parts by mass, 20 to 55 parts by mass, or 25 to 50 parts by mass relative to 100 parts by mass of cellulose. [Example]

[0052] The present invention will be described in more detail below using examples and comparative examples, but the present invention is not limited to the following examples.

[0053] 1. Mixture Preparation Resin and cellulose were mixed according to the composition shown in Figure 5. The affinity parameter Ln(gamma) at 25°C, calculated using the COSMO-RS method for the resin and water, was estimated with reference to the data in Figure 1. In this case, when 56 parts by mass of PBS and 24 parts by mass of PLA were included as in Example 1, the affinity parameter Ln(gamma) was calculated as the weighted average.

[0054] 2. Judgment process The target value of Charpy impact strength was set to 50% relative to the resin-only state, which was set to 100%, and the selection method of this embodiment was executed on a general-purpose PC. The general-purpose PC had a model trained using training data such as that shown in Figure 2 pre-recorded on it.

[0055] The results of the evaluation are shown in Figure 5. In Figure 5, the cases where the Charpy impact strength was 50% or more are marked with a circle, and the cases where it was less than 50% are marked with an x.

[0056] 3. Verification Furthermore, the Charpy impact strength was measured for a mixture obtained by mixing resin and cellulose in the composition shown in Figure 5. The Charpy impact strength of the resin alone was also measured. Figure 5 shows the ratio of the Charpy impact strength of the mixture when the Charpy impact strength of the resin alone is taken as 100%. The Charpy impact strength ratios shown in Figure 5 are actual measured values.

[0057] The results of the evaluation process obtained as described above were compared with the actually measured Charpy impact strength ratios to determine whether the evaluation results matched. As a result, it was confirmed that the selection method of the present invention can accurately estimate the target value of Charpy impact strength.

[0058] Furthermore, the above results show that when the affinity parameter Ln(gamma) at 25°C, calculated using the COSMO-RS method between resin and water, is -5 or less, the resin has excellent Charpy impact strength.

[0059] 4. Summary Water is present on the surface of cellulose. The contact area between the resin and cellulose is affected by the influence of this water. When a resin with high affinity for water is used, water molecules are absorbed into the resin, increasing the contact area between the resin and cellulose. On the other hand, when a resin with low affinity for water is selected, water molecules remain between the resin and cellulose, reducing the contact area. Since the larger the contact area, the greater the adhesive strength. Therefore, it has been shown that by selecting a resin with high affinity for water using the method of this embodiment, a material with high impact resistance can be provided. [Explanation of symbols]

[0060] 100... selection device, 110... processor, 111... acquisition unit, 112... output unit, 120... communication interface, 130... input / output interface, 140... memory, 150... storage, 151... learning data, 160... communication bus

Claims

1. cellulose and a resin, The affinity parameter Ln(gamma) between the resin and water at 25°C calculated using the COSMO-RS method is -5 or less. Material for injection molding.

2. The content of the resin is 15 to 60 parts by mass relative to 100 parts by mass of the cellulose. The injection molding material according to claim 1.

3. A first step of setting a target value for the Charpy impact strength of an injection molding material; a second step of obtaining the moisture content of the cellulose contained in the injection molding material; a third step of outputting a resin that satisfies the target value of the Charpy impact strength based on the water content of the cellulose and an affinity parameter Ln(gamma), The affinity parameter Ln(gamma) is the affinity parameter Ln(gamma) at 25°C calculated using the COSMO-RS method between the resin and water. How to select resin for injection molding.

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