CN100405376C - Method for Determining Process Parameters of Plastic Injection and Injection Molding Machine - Google Patents

Abstract

The setting method of plastic injection process parameters is to establish an instance database based on a large number of injection molding instances, and according to the plastic injection conditions of the target instance, use the method of instance reasoning to search the instance database for the instance that is most similar to the target instance, and obtain similar plastics. The process parameters are injected, and the process parameters are absorbed and corrected by the instance, so as to determine the process parameters of the target instance. Also provided is an injection molding machine, which includes an injection molding machine host, a memory, a process parameter processor and a controller, the memory stores an example library, a plastic physical property library and an injection molding machine library, and the process parameter processor passes The above method determines the process parameters and transmits the process parameters to the controller. The invention has the advantages of: using the case-by-case reasoning method, the process parameters of plastic injection under specific injection molding conditions can be quickly and accurately obtained, thereby shortening the production cycle of plastic products, improving product quality, and maximizing the production capacity of injection machines.

Description

Method for Determining Process Parameters of Plastic Injection and Injection Molding Machine

technical field

The invention relates to the field of plastic injection molding, in particular to a method for determining process parameters of plastic injection. Specifically, the process parameters of plastic injection are determined by collecting and arranging production examples in plastic injection production and reasoning based on examples.

Background technique

Plastic has the characteristics of low density, light weight, good insulation performance, low dielectric loss, high chemical stability and good wear resistance. It is widely used in various fields such as industry, agriculture, construction, packaging, national defense and daily life. The growth rate has jumped to the top of the four major industrial materials (plastics, steel, wood and cement), and it is one of the fastest growing industries in the world. The consumption in terms of volume and weight has exceeded that of steel in the world.

Plastic injection molding has the characteristics of fast production cycle, high production efficiency, complex shape, precise size or products with inserts, and easy automation. Therefore, injection molding has become one of the main processing methods for plastics. Plastics account for 32% of the total weight of plastic processing, which occupies a very important position in the plastic products production industry.

Injection molding process parameters are very important in plastic production, directly affecting product quality, cost and production cycle. However, the determination of injection molding process parameters is very complicated, which is closely related to factors such as injection molding machines, plastic properties, and molds. The traditional method of determining the injection molding process parameters is mainly the trial method (also known as the trial and error method), that is, the process parameters are determined based on the limited experience of relevant personnel, and then the evaluation is carried out after the mold trial, and the appropriate injection molding process parameters are finally determined. However, in actual production, due to the wide variety of properties of plastic materials, the ever-changing structure of products and molds, and the complex process parameters of injection molding machines, it is difficult to fully consider and deal with these factors only with limited experience; and the experience of relevant personnel The accumulation cannot keep up with the development of plastic materials and the improvement of product complexity and precision requirements. Therefore, this method requires repeated mold trials, resulting in long time for process parameter determination, large production gap, long molding cycle, high reject rate, and low product quality. Unsatisfactory quality, unstable production, and low utilization rate of injection machines. At the same time, this determination method requires experienced personnel, and the serious shortage of such personnel restricts the development of the plastic injection industry.

With the increasingly fierce competition in the global market, it is required to reduce the setting time of the molding process as much as possible, shorten the production cycle of plastic products, improve the quality of products, and maximize the production capacity of injection machines. Therefore, there is an urgent need to quickly and accurately determine the process parameters of plastic injection, and replace the traditional "trial and error method" with a new method for determining the process parameters of injection molding.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the above-mentioned defects of the prior art and provide a method for determining plastic injection process parameters and an injection molding machine, so as to achieve the purpose of quickly and correctly determining the plastic injection process parameters.

The technical solution adopted by the present invention to solve the problems of the technologies described above is:

A method for determining the process parameters of plastic injection, which establishes an instance database based on a large number of injection molding instances, uses the method of instance reasoning to search for the instance closest to the target instance in the instance database according to the plastic injection conditions of the target instance, and obtains a similar The plastic injection process parameters of the target instance are absorbed and corrected by the example, so as to determine the process parameters of the target instance.

In order to solve a new problem, people usually find an old problem similar to the new problem from memory first, and then use the relevant information and knowledge in the old problem to solve the new problem, that is, case-based reasoning. method. Similarly, in injection molding production, each production example itself condenses the production experience of the craftsman. Therefore, reasonable injection molding process parameters can be determined through reasoning through the collated production examples, which is the basis of the present invention.

Specifically, the overall flow diagram of the method for determining plastic injection process parameters in the present invention is shown in FIG. 1 . First import the CAD modeling file (STL file) of the product, and input the grade of plastic used for injection and the model of the injection molding machine. According to the location of the gate, the geometric data of the cavity is calculated through the CAD file, including flow length, average thickness, cavity complexity, cavity volume, etc. According to the geometric characteristics of the cavity and the physical properties of the plastic, search for the instance with the highest similarity to the current injection process (similar instance) in the instance library, and take out each process parameter of the instance, correct the process parameters of the similar instance, and then pass the injection molding machine controller The communication interface uploads various process parameters to the controller of the injection molding machine. Then carry out the mold trial, if the mold trial is successful, the current injection molding process will be saved as a production example in the example library on the computer memory, so that the example library will be continuously enriched and expanded as the basis for future example reasoning.

The examples in the above example library are described by the structure of their plastic injection conditions and corresponding process parameters. This is because there is a certain inherent relationship between the injection conditions of the example and the process parameters of injection molding, and the process parameters required for injection molding production can be determined by the injection conditions of the example.

The plastic injection conditions of the above example are described by the following three aspects: plastic type, mold cavity geometric characteristics, injection molding machine model, wherein, the mold cavity geometric characteristics include: flow length, average thickness, cavity complexity , Cavity volume. This is because the injection molding process parameters are closely related to these factors. Different plastics have different rheological properties, and the characteristics of the mold cavity also have a decisive influence on the process parameters. Different types of injection molding machines start from the process parameter machine value (machine parameter ) to real process parameters conversion (theoretical parameters) coefficients are not the same.

The process parameters of the above examples include the following aspects: injection parameters, pressure holding parameters, cooling parameters and plasticizing parameters, nozzle heating temperature, nozzle heat preservation temperature, etc. This is because during the plastic injection process, these parameters have the greatest impact on the quality of injection molded parts, are the most important process parameters, and are also the most difficult and most necessary process parameters for craftsmen to determine during the mold adjustment process.

The organizational form of the example data is shown in Table 1.

Table 1. Instance database organization form

The above example library is placed on the computer memory, so that it can be called at any time.

As an example of the target, the shape of the injection molded product, the type of plastic and the model of the injection molding machine can be determined. According to the modeling file of the injection molded product, calculate the required geometric characteristic data of the cavity, that is, flow length, average thickness, cavity volume, and determine the cavity complexity based on experience.

Then determine the similarity between the current target instance and each instance in the instance library. The similarity is calculated using the following formula:

$\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 4</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 4</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, f(i) is the influence factor of each feature data, including material factor, thickness factor, flow length factor, cavity volume factor and cavity complexity factor, etc. W _i is the weight of each influence factor; each influence factor Calculated using the following formulas:

Material factor f(0):

(i)(i＝1、2、3)分别为材料的流动相似度因子、保压相似度因子和冷却相似度因子，

v_i(i＝1、2、3)分别为各个因子的权值；η_(obj)，η_(src)分别为目标实例和相似实例塑料材料的粘度，可采用Cross粘度模型计算： $\mathrm{\&eta;}=\frac{{\mathrm{\&eta;}}_0(T,P)}{1+{({\mathrm{\&eta;}}_0\mathrm{\&gamma;}/{\mathrm{\&tau;}}^{*})}^{1-n}},$ ${\mathrm{\&eta;}}_0(T,P)={\mathrm{Be}}^{{\text{T}}_b/T}{e}^{\mathrm{\&beta;P}},$ τ为材料常数，n为非牛顿指数，η₀为零剪切粘度，B，T_b，β均为材料常数；S_(obj)，S_(src)，分别为目标实例和相似实例材料的收缩率；α_(obi)，α_(src)分别为目标实例和相似实例材料的热扩散系数，上述材料的物性参数从塑料材料库中读取。Among them, the material factor is the similarity of plastic materials, including the similarity of viscosity, similarity of shrinkage and similarity of thermal diffusivity,

(i) (i=1, 2, 3) are respectively the flow similarity factor, packing similarity factor and cooling similarity factor of the material,

v _i (i=1, 2, 3) are the weights of each factor respectively; η _(obj) , η _(src) are respectively the viscosity of the target instance and the similar instance plastic material, which can be calculated by using the Cross viscosity model: $\mathrm{\&eta;}=\frac{{\mathrm{\&eta;}}_0(T,P)}{1+{({\mathrm{\&eta;}}_0\mathrm{\&gamma;}/{\mathrm{\&tau;}}^{*})}^{1-\mathrm{no}}},$ ${\mathrm{\&eta;}}_0(T,P)={\mathrm{be}}^{{\text{T}}_b/T}{e}^{\mathrm{\&beta;P}},$ τ is a material constant, n is a non-Newtonian exponent, η ₀ is a zero shear viscosity, B, T _b , and β are all material constants; S _(obj) , S _(src) , are the shrinkage of the target instance and similar instance materials, respectively α _(obi) and α _(src) are the thermal diffusivity of the target instance and similar instance materials respectively, and the physical parameters of the above materials are read from the plastic material library.

Thickness factor f(1): $f\left(1\right)=1-\frac{|{\mathrm{thick}}_{\left(\mathrm{obj}\right)}-{\mathrm{thick}}_{\left(\mathrm{src}\right)}|}{{\mathrm{thick}}_{\left(\mathrm{obj}\right)}}---\left(3\right)$

Flow length factor f(2): $f\left(2\right)=1-\frac{|{\mathrm{flowLen}}_{\left(\mathrm{obj}\right)}-{\mathrm{flowLen}}_{\left(\mathrm{src}\right)}|}{{\mathrm{flowLen}}_{\left(\mathrm{obj}\right)}}---\left(4\right)$

Cavity volume factor f(3): $f\left(3\right)=1-\frac{|{\mathrm{volume}}_{\left(\mathrm{obj}\right)}-{\mathrm{volume}}_{\left(\mathrm{src}\right)}|}{{\mathrm{volume}}_{\left(\mathrm{obj}\right)}}---\left(5\right)$

Cavity complexity factor f(4): $f\left(4\right)=1-\frac{|c_{\left(\mathrm{obj}\right)}-c_{\left(\mathrm{src}\right)}|}{c_{\left(\mathrm{obj}\right)}}---\left(6\right)$

Among them, thick _(obj) , flowlen _(obj) , volume _(obj) , c _(obj) are the average thickness, flow length, cavity volume, cavity complexity of the target instance respectively, thick _(src) , flowlen _(src) , volume _(src) and c _(src ) are feature data corresponding to similar instances.

Instance retrieval: According to the similarity value, obtain the instance that meets the specified condition value, and determine the instance that is most similar to the target instance from the instance library. The larger the similarity value, the better the effect of process parameter determination, and the lower the similarity value, the worse the effect of process parameter determination. Therefore, the similarity of the most similar instance must meet a certain size, generally a value of 0.85 is reasonable Yes, at this time part of the process parameters of the similar instances in the database can be used as the process parameters of the target instance, otherwise, it is unreasonable, and none of the process parameters in the database can be used as the process parameters of the target instance.

After determining the similar examples, take out the corresponding process parameters from the examples. Since the process parameters saved in the example are the machine parameter values (referred to as machine parameters) set on the controller panel of the injection molding machine, the various pressures and speeds are the pressure and speed percentage values in the hydraulic oil circuit, not the front end of the screw. The real pressure and speed values (called theoretical parameters), that is to say, some process parameters are directly controlled by machine parameters, and some are indirectly controlled by machine parameters. For directly controlled process parameters, the machine setting value is the theoretical value. The value has nothing to do with the model of the injection molding machine. For the indirectly controlled process parameters, there is a conversion relationship between the machine setting value and the theoretical value. Different types of injection molding machines have different conversion coefficients. Therefore, when determining the pressure and speed parameter values in the target instance according to the similar instance, it is necessary to take into account the difference in the model of the injection molding machine used in the similar instance and the target instance.

The process parameters directly controlled by machine parameters include: barrel heating temperature, barrel holding temperature, pressure holding time, injection time, cooling time, cushion length, rear loosening displacement, etc. The process parameters indirectly controlled by machine parameters include: Injection pressure, injection speed, holding pressure, back pressure, screw rotation speed, etc.

For the process parameters controlled indirectly, it is necessary to convert the machine parameters into theoretical parameters in order to borrow the process parameters of the target instance. The conversion formulas of the process parameters of each indirect control are as follows:

Injection pressure: $p_{\mathrm{inj}\left(\mathrm{src}\right)}=p_{\max \left(\mathrm{src}\right)}\&times;\frac{p_{\mathrm{inj}-\mathrm{the\; oil}\left(\mathrm{src}\right)}}{P_{\mathrm{panel}\left(\mathrm{src}\right)}^{\max }}$ or

Injection speed: $v_{\mathrm{inj}\left(\mathrm{src}\right)}=\frac{v_{\max \left(\mathrm{src}\right)}}{0.25\mathrm{\&pi;}{{\mathrm{D.}}_{\left(\mathrm{src}\right)}}^2}\&times;\frac{v_{\mathrm{inj}-\mathrm{the\; oil}\left(\mathrm{src}\right)}}{v_{\mathrm{panel}\left(\mathrm{src}\right)}^{\max }}---\left(8\right)$

Holding pressure: $p_{\mathrm{hld}\left(\mathrm{src}\right)}=p_{\max \left(\mathrm{src}\right)}\&times;\frac{p_{\mathrm{hld}-\mathrm{the\; oil}\left(\mathrm{src}\right)}}{p_{\mathrm{panel}\left(\mathrm{src}\right)}^{\max }}$ or

Back pressure: $p_{\mathrm{back}\left(\mathrm{src}\right)}=p_{\max \left(\mathrm{src}\right)}\&times;\frac{p_{\mathrm{back}-\mathrm{the\; oil}\left(\mathrm{src}\right)}}{P_{\mathrm{panel}\left(\mathrm{src}\right)}^{\max }}$ or

Screw rotation speed: $v_{\mathrm{screw}\left(\mathrm{src}\right)}={\mathrm{\&omega;}}_{\mathrm{src}}\&times;\frac{{\mathrm{D.}}_{\left(\mathrm{src}\right)}}{2}---\left(11\right)$

Among them, p _inj(src) , v _inj(src) , p _hld(src) and p _back(src) represent theoretical injection pressure, holding pressure and back pressure respectively, P _inj-oil(src) and p _{hld -oil(src)} and p _{back-oil(src)} represent the machine parameter values of injection pressure, holding pressure and back pressure respectively, that is, the pressure value in the oil circuit; p _max(src) is the maximum injection pressure of the injection molding machine , p ^max _panel(src) and v ^max _panel(src) are the maximum pressure and speed values of the oil circuit that can be set on the controller panel of the injection molding machine respectively, A _{injection cylinder (src)} is the total cross-sectional area of the injection cylinder, A _{screw( src)} is the cross-sectional area of the screw, D _(src) is the diameter of the screw, v _{screw (src)} is the linear velocity of the screw rotation, and the above variables are the corresponding values of the machines used in similar examples (the subscripts are unified as: _(src) ).

Instance parameter extraction: After searching for the most similar instance, it is necessary to determine the process parameters of the target instance from this instance. The principle of parameter extraction is: barrel heating temperature, barrel holding temperature, injection speed, injection pressure, holding pressure, holding time, cooling time, cushion length, rear loosening and retreating displacement, and screw rotation linear speed reference of the target instance The numerical value of the corresponding parameter in the similar instance. Among them, some process parameters directly controlled by machine parameters: heating temperature, holding temperature, pressure holding time, cooling time, length of cushion material, and rear loosening displacement directly take the corresponding values in similar examples, and process parameters indirectly controlled by machine parameters : Injection speed, injection pressure, holding pressure, and screw rotation linear speed take the theoretical values converted by the above formula, and other unmentioned process parameters are determined by correction in the subsequent process parameter correction. The extraction of parameters can be explicitly expressed by the following formula:

T _(obj) = T _(src) ; T' _(obj) = T'_(src); t _{hld (obj)} = t _{hld (src)} ; t _{cool (obj)} = t _{cool (src)} ; ΔL _{spare (obj )} = ΔL _spare(src) ;

Δx _(obj) = Δx _(src) ; p _inj(obj) = p _inj(src) ; v _inj(obj) = v _inj(src) ; p _hld(obj) = p _hld(src) ; p _{back(obj )} = p _back(src) ;

v _screw(obj) = v _screw(src) ; (12)

Among them, T _(obj) , T′ _(obj) , t _hld(obj) , t _cool(obj) , ΔL _spare(obj) , Δx _(obj) , p _inj(obj) , v _inj(obj) , p _{hld (obj)} , p _back(obj) and v _screw(obj) are the barrel heating temperature, holding temperature, pressure holding time, cooling time, cushion length, back loosening displacement, injection pressure, injection speed, Theoretical values of holding pressure, back pressure, screw speed (line speed), T _(src) , T′ _(src) , t _hld(src) , t _cool(src) , ΔL _spare(src) , Δx _(src) , p _inj(src) , v _inj(src) , p _hld(src) , p _back(src) and v _screw(src) are the corresponding theoretical values of process parameters in similar examples.

Example correction: determine the undetermined process parameters in parameter extraction, including injection time, injection start position, injection end position, and plasticization end position. Using the difference between the injected volumes of the target instance and similar instances, the correction is done as follows:

Injection time: $t_{\mathrm{inj}\left(\mathrm{obj}\right)}=\frac{V_{\left(\mathrm{obj}\right)}}{v_{\mathrm{inj}\left(\mathrm{obj}\right)}}---\left(13\right)$

Plasticizing end position: $Y_{\left(\mathrm{obj}\right)}=\mathrm{\&Delta;}{L}_{\mathrm{spare}\left(\mathrm{src}\right)}+\frac{V_{\left(\mathrm{obj}\right)}}{{{0.25\mathrm{\&pi;D}}_{\left(\mathrm{obj}\right)}}^2}---\left(14\right)$

Injection start location: $x_{0\left(\mathrm{obj}\right)}=\mathrm{\&Delta;}{L}_{\mathrm{spare}\left(\mathrm{src}\right)}+\frac{V_{\left(\mathrm{obj}\right)}}{{{0.25\mathrm{\&pi;D}}_{\left(\mathrm{obj}\right)}}^2}+\mathrm{\&Delta;}{x}_{\left(\mathrm{src}\right)}---\left(15\right)$

Injection endpoint position: $x_{1\left(\mathrm{obj}\right)}=x_{0\left(\mathrm{obj}\right)}-0.95\frac{V_{\left(\mathrm{obj}\right)}}{{{0.25\mathrm{\&pi;D}}_{\left(\mathrm{obj}\right)}}^2}---\left(16\right)$

Among them, V _(obj) , t _inj(obj) , Y _(obj) , x _0(obj) , x _1(obj) are respectively the target instance injection volume, injection time, plasticization end position, injection start position, injection end point Location. The definitions of other variable symbols are the same as above.

Target instance process conversion: convert the following theoretical values obtained on the injection molding machine used in the target instance into machine parameter values, that is, the values set on the controller panel. The conversion formula is as follows:

Injection pressure: $p_{\mathrm{inj}-\mathrm{the\; oil}\left(\mathrm{obj}\right)}=p_{\mathrm{panel}\left(\mathrm{obj}\right)}^{\max }\&times;\frac{p_{\mathrm{inj}\left(\mathrm{obj}\right)}}{p_{\max \left(\mathrm{obj}\right)}}$ or

Injection speed: $v_{\mathrm{inj}-\mathrm{the\; oil}\left(\mathrm{obj}\right)}=\frac{{{0.25\mathrm{\&pi;D}}_{\left(\mathrm{obj}\right)}}^2{v}_{\mathrm{panel}\left(\mathrm{obj}\right)}^{\max }}{v_{\max \left(\mathrm{obj}\right)}{v}_{\mathrm{inj}\left(\mathrm{obj}\right)}}---\left(18\right)$

Holding pressure: $p_{\mathrm{hld}-\mathrm{the\; oil}\left(\mathrm{obj}\right)}=p_{\mathrm{panel}\left(\mathrm{obj}\right)}^{\max }\&times;\frac{p_{\mathrm{hld}\left(\mathrm{obj}\right)}}{p_{\max \left(\mathrm{obj}\right)}}$ or

Back pressure: $p_{\mathrm{back}-\mathrm{the\; oil}\left(\mathrm{obj}\right)}=P_{\mathrm{panel}\left(\mathrm{obj}\right)}^{\max }\&times;\frac{p_{\mathrm{back}\left(\mathrm{obj}\right)}}{p_{\max \left(\mathrm{obj}\right)}}$ or

Screw speed: ${\mathrm{\&omega;}}_{\left(\mathrm{obj}\right)}=\frac{2\&times;v_{\mathrm{screw}\left(\mathrm{obj}\right)}}{{\mathrm{D.}}_{\left(\mathrm{obj}\right)}}---\left(\mathrm{twenty\; one}\right)$

Wherein, the meaning of each variable is the same as the meaning of each corresponding variable in the foregoing formula, and each variable is a value corresponding to the injection molding machine used in the target instance.

In this way, the above-mentioned production process parameters of the target instance on the injection molding machine controller panel are finally determined, and then the process parameters are uploaded to the controller through the standard TCP/IP communication interface of the injection molding machine controller for injection molding.

If the quality of the injection molded part of the above-mentioned target instance is not defective, the target instance will be saved in the instance library to enrich the content of the instance library and serve as the basis for inference in the future.

The present invention also provides an injection molding machine, which includes an injection molding machine host, a memory, a process parameter processor and a controller. The memory stores an example library, a plastic physical property library, and an injection molding machine library. The controller determines the process parameters through the above method, and transmits the process parameters to the controller.

Compared with the prior art, the present invention has the advantages of: using the case-by-case reasoning method, the process parameters of plastic injection under specific injection molding conditions can be quickly and accurately obtained, thereby shortening the production cycle of plastic products, improving product quality, and promoting injection machine production. capacity maximization.

Description of drawings

Figure 1 is the overall block diagram of the method for determining the parameters of the plastic injection process

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

As shown in Figure 1, three databases are established first: instance database, plastic database, and injection molding machine database, as the basis of this method.

The example library collects and arranges the plastic injection production examples according to the organizational structure shown in Table 1, and saves them on the computer memory.

The plastic library contains physical parameters such as viscosity, shrinkage rate, and thermal diffusivity of different grades of plastics, which are stored in the computer memory and used as the basis for the calculation of material similarity in the example similarity calculation.

The injection molding machine library organizes and stores the performance parameters of the injection molding machine used in the injection examples in the database and saves them on the computer memory, as the basis for converting the process parameters of similar examples into the process parameters of target examples.

Then calculate the geometric characteristics of the cavity according to the CAD modeling file of the injection molded part of the target example: the CAD modeling file of the injection molded part is an STL file, and the average wall thickness of the injection molded part (that is, the average wall thickness of the cavity) and the injection volume are determined according to the experience of the craftsman According to the complexity of the cavity and the position of the gate, the flow length in the cavity is determined, so as to determine the characteristic data of the target instance.

Then instance retrieval: traverse each instance in the instance library, calculate the similarity between the instance in the instance library and the target instance, and find out the similar instance most similar to the target instance. The calculation of the similarity is carried out by formula (1), which first reads the physical parameters of the target instance material and the physical parameters of the plastic material used in the similar examples from the plastic material library, calculates the material similarity f(0), and then uses the formula (2)-(5) Calculate the thickness factor, flow length factor, volume factor, and complexity factor, and then use formula (1) to calculate the total similarity.

Then the instance parameters are extracted: the most similar instance is determined by the similarity value, the process parameters of the most similar instance are taken out from the instance database, and the performance data of the injection molding machine used by the instance is read from the injection molding machine database, using formula (7)-(11 ) to convert process parameters (machine parameters) of similar examples into theoretical parameters. Through a group of equations shown in formula (12), the theoretical values of some process parameters of the target instance are determined.

Carry out instance correction: determine the injection time, plasticizing end point, injection start point, and injection end point of the target instance through formulas (13)(14)(15)(16).

Target instance process parameter conversion: read the performance parameters of the injection molding machine used in the target instance from the injection molding machine database, and use formula (17)-(21) to convert the theoretical value of the determined target instance process parameters into the injection molding machine controller The setting value on the panel. Thus, the entire calculation process is completed, and the process parameters of the injection molding machine used in the target instance are determined.

Then upload the above process parameters to the controller through the standard TCP/IP network communication interface provided by the controller of the injection molding machine.

The injection molding machine performs mold trial, if the obtained product meets the requirements, the mold trial is successful, and the characteristics and process parameters of this injection are saved in the instance inventory.

Claims (10)

1. The method of determining the process parameters of plastic injection is to establish an instance database based on a large number of injection molding instances, and according to the plastic injection conditions of the target instance, use the method of instance reasoning to search for the instance closest to the target instance in the instance database, and obtain a similar The plastic injection process parameters of the example, and the process parameters of the similar example are corrected by example drawing and example, thereby determining the process parameters of the target example, which is characterized in that: The injection molding example of described example database comprises two parts, and a part is example feature, and another part is process parameter; Described example feature is described by following three aspects: plastic type, mold cavity geometric feature, injection molding machine model, wherein , the geometric characteristics of the mold cavity include: flow length, average thickness, cavity complexity, and cavity volume; the process parameters described are barrel heating temperature, barrel holding temperature, injection parameters, pressure holding parameters, cooling parameters and molding parameters. parameters; The plastic injection conditions of the target example are example characteristics, and are determined by the following method: import the CAD modeling file of the target example product, and calculate the cavity geometric data, including the flow length, according to the position of the gate setting through the CAD modeling file , average thickness, cavity volume, determine cavity complexity based on experience; The closest instance to the target instance refers to the instance whose similarity is greater than or equal to the specified value, and the similarity is given by the formula $\mathrm{the\; s}=\frac{f\left(0\right)\&times;\underset{i=1}{\overset{4}{\mathrm{\&Sigma;}}}(W_i\&times;f\left(i\right))}{\underset{i=1}{\overset{4}{\mathrm{\&Sigma;}}}{W}_i}$ Calculation, where f(0) is the material factor, f(i) is the influence factor of each characteristic data, including thickness factor, flow length factor, cavity volume factor and cavity complexity factor, W _i is the influence factor of each influence factor Weight; the relevant data for calculating the plastic similarity is obtained from the plastic physical property library; The examples described are as follows: process parameters directly controlled by machine parameters: heating temperature, holding temperature, pressure holding time, and post-loosening displacement directly take the corresponding values in the most similar example, and other process parameters indirectly controlled by machine parameters: Injection pressure, injection speed, holding pressure, back pressure, screw rotation speed, convert machine parameters into theoretical values, and the required injection molding machine parameters are obtained from the injection molding machine library; The example correction refers to determining the undetermined process parameters in parameter absorption, including injection time, injection start position, injection end position, and plasticization end position. 2. The method for determining plastic injection process parameters according to claim 1, wherein the CAD modeling file is an STL file. 3. The method for determining the process parameters of plastic injection according to claim 1 or 2, characterized in that: the process parameters of the target instance are converted into process parameters of the injection molding machine through the process of the target instance. 4. The method for determining plastic injection process parameters according to claim 1 or 2, characterized in that: the determined value of the similarity value is 0.85. 5. The method for determining plastic injection process parameters according to claim 3, characterized in that: the determined value of the similarity value is 0.85. 6. The method for determining plastic injection process parameters according to claim 1 or 2, characterized in that: said calculation and reasoning are all processed on a computer, and said plastic physical property library, injection molding machine library, and example library are stored in on the computer's memory. 7. The method for determining the process parameters of plastic injection according to claim 3, characterized in that: the determined process parameters are uploaded to the controller through the communication interface of the controller of the injection molding machine. 8. The method for determining plastic injection process parameters according to claim 4, characterized in that: the determined process parameters are uploaded to the controller through the communication interface of the injection molding machine controller. 9. The method for determining the process parameters of plastic injection according to claim 7, characterized in that: the determined process parameters are uploaded to the controller through the communication interface of the controller of the injection molding machine. 10. An injection molding machine, characterized in that: it includes an injection molding machine host, a memory, a process parameter processor and a controller, the memory stores an example library, a plastic physical property library and an injection molding machine library, and the described The process parameter processor determines the process parameters through the method for determining the process parameters of plastic injection according to any one of claims 1-9, and transmits the process parameters to the controller.

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