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The use of numerical analysis of the injection process to select the material for the injection molding

  • Tomasz Jachowicz EMAIL logo , Ivan Gajdoš , Vlastimil Cech and Volodymyr Krasinskyi
Published/Copyright: October 7, 2021
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Open Engineering
From the journal Open Engineering Volume 11 Issue 1

Abstract

The article presents the methodology of using the results of computer simulation of the injection process to assess the suitability of the material for the injection molding. Computer simulation of the basic phenomena occurring during the filling phase, packing, and cooling phase of the injection molding provides a number of different results, containing typical information both on the suggested technological parameters of the process and on the dimensional accuracy of the molded part, but also allows obtaining data on the production efficiency and energy demand of the processing machine. On the basis of this information, it is possible to assess the suitability of the polymer materials used in the simulation, intended for the production of products from a specific industry, taking into account various criteria, mainly of an economic or qualitative nature.

Keywords: injection molding; numerical modeling; computer simulation; material selection; characteristics of the injection molding.

1 Introduction

Injection molding is currently the basic method of processing polymer materials, due to the complexity of the structure and high accuracy of the moldings obtained, as well as the range of materials used. As a result of injection, plastic parts are obtained with complex shapes, various wall thicknesses, and a large mass span. When constructing an injection molding, many factors are of fundamental importance; the most important of which are: wall thickness, surface inclination, edge rounding radius, shape and dimensions of cross-sections, openings, undercuts, and reinforcing ribs, which should be selected according to appropriate recommendations ensuring proper filling of the mold cavity with plastic, proper cooling conditions for the molded part, and the desired geometric accuracy. These recommendations limit the freedom of designing the molded part at the stage of determining its functional shape and are related to the concept of mold technological correctness, understood as the compliance of the design of the designed product with the conditions of a specific production process, which in this case is injection [1,2,3,4].

Nowadays, numerical modeling is irreplaceable in many fields of technology. Thanks to computer simulation and analysis of the obtained results, many design and technological errors can be eliminated already at the design stage, therefore significantly reducing costs and shortening the production preparation time [5,6,7,8,9]. Obtaining a molding with high utility values takes the correct selection of process parameters, the type of material, a tool of appropriate design, and a processing machine with specific technological capabilities. A properly designed molding is a source of information on the basis of which the type of material is selected, the injection mold is constructed, and the technological conditions of the process are determined [10,11,12]. The final determination of the technological parameters of injection molding is a compromise between the expected maximum injection efficiency and the acceptable level of quality of the molded part and its functional features. Due to the very high costs of manufacturing injection molds, it is necessary to minimize the risk of errors at each stage of the preparation of the production process [12,13,14].

2 The essence of numerical modeling of the injection process

Injection of polymer materials, due to the complexity of the phenomena occurring during the implementation of this processing method, is the subject of a comprehensive analysis using specialized engineering software CAD/CAM/CAE [15,16]. The CAE software for numerical modeling of injection allows simulation of the phenomena occurring during filling the mold cavity with plastic and replenishing the material in the packing phase, as well as for following analysis of the cooling of the molded part, processing shrinkage, warpage, and deformation of the finished product. For the material forming the injection molding part, it is possible to analyze the primary shrinkage occurring during the process and the secondary shrinkage occurring during cooling. The simulation of the material flow and filling of the molding cavity and the resulting numerical description of the injection process facilitate the optimization of the mold structure, determination of process parameters, and the selection of the injection molding machine with appropriate technological capabilities in order to get the molded part of the appropriate quality. The simulation of the cooling of the injection molding is to optimize the design of the mold, in which the cooling system is designed in such a way as to achieve the most even possible cooling intensity while keeping the cycle time of the injection process as short as possible. The results of the analysis of the cooling of the molded part allow the injection cycle time to be shortened and the production costs to be reduced without loss of product quality.

Preparation of simulations of the phenomena occurring during injection demands the introduction of general characteristics concerning the shape and dimensions of the molded part, properties of the processed material, and process conditions [1,4,17,18]. In the case of using computer simulation of injection to select the most appropriate material for a specific injection molding, both the functional and processing properties of the material as well as the relevant simulation results should be taken into account. Usually, the first attention is paid to the results describing the dimensional accuracy of the molded part (shrinkage, warpage, and deformation) [10,18,19], while it is equally important to refer to the technological parameters of the process determined by numerical modeling (time of the injection phase, pressure and cooling phases, and appropriate pressures) [19,20,21]. Also, the results related to the energy load of the machine (mold clamping force and injection power) and the consumption of the raw material, which in the case of injection, where millions of the same parts are produced, is particularly important. Based on the simulation results, it is possible to meet the qualitative and economic requirements related to getting a molded part made of appropriately selected material, with high dimensional stability, good optical properties of the surface, and the required accuracy of fit with other cooperating parts [4,13,22].

3 Computer simulation of the cap manufacturing

3.1 Purpose of computer simulation

The purpose of computer simulation and numerical modeling of the injection process is to assess the suitability of selected thermoplastics for the production of a lid for packaging of food products. For selected five thermoplastics dedicated by manufacturers to be used in the packaging industry, numerical simulations of the injection phase, packing phase, and cooling phase of the molded part were performed. Selected parameters of the injection process were compared, like injection phase time, packing phase time, and cooling time, affecting the length of the injection cycle and thus the process efficiency. The processing temperature range, the required process pressures, the forces acting on the injection mold, and the injection power as factors influencing the energy demand of the machine were further analyzed. The dimensional accuracy of the obtained moldings was also assessed by means of shrinkage, distortion, and deformation analysis. On the basis of the obtained results, a material was selected which was assessed in three respects: economic, technological, and quality.

3.2 Solid model of the molded part

Before simulating the injection molding process with Cadmould 3D-F, it is necessary to import a solid model representing the mold cavity of the injection mold. Most often, the design is made in a CAD software and then converted to one of the formats read in Cadmould 3D-F (usually STL, STEP, IGES). The solid model of the cap used to simulate the injection process was made using Solid Edge software and saved in the STL format. The appearance of the solid model is shown in Figure 1. The surface area of the model is 7,459 mm2, and its volume is 4,295 mm3. Figure 2 shows the model introduced to the Cadmould 3D-F program with the generated FEM mesh. The relative size of the FEM mesh elements was 1.647%, while the absolute size of the FEM mesh elements was 0.87 mm. Four mold cavities were modelled with the material supply channels arranged in a star shape. The total volume of the elements forming the FEM mesh was 18,221 mm3, of which the volume of cylindrical elements was 1,579 mm3 and tetrahedral elements 16,642 mm3. Accordingly, the total actual volume of the solid models was 18,759 mm3, of which the volume of all four molding cavities was 17,180 mm3 and the material supply channels were 1,579 mm3. The FEM mesh fit factor, which is the ratio of the finite element volume to the real volume, is 0.971 and is within the recommended range of 1.0 ± 5% (Figure 3).

Figure 1 The view of the solid model of the cap.
Figure 1

The view of the solid model of the cap.

Figure 2 A solid model of a cap with a FEM mesh applied in the Cadmould 3D-F program.
Figure 2

A solid model of a cap with a FEM mesh applied in the Cadmould 3D-F program.

Figure 3 A four-cavity mold insert with a runner system used to perform the simulation.
Figure 3

A four-cavity mold insert with a runner system used to perform the simulation.

3.3 Numerical analysis program

The computer simulation of the injection process was carried out with the Cadmould 3D-F software, version 13.0.5.0.

The constant factors during the computer simulations were the shape and geometrical dimensions of the solid model of the cap as well as the shape and dimensions of the material supply system. A single-point material supply system was used, simplified for one four-cavity mold insert. Due to the adopted goal of the computer simulations, the complete gating system for the entire multi-cavity injection mold was not built, and the design of the possible cooling system for the injection mold was not analyzed (if such would be needed). The same material was adopted for the injection mold – tool steel 1.2083 with a thermal conductivity coefficient of 20 W/m K, an ambient temperature of 20°C, and an ambient heat conductivity coefficient of 0,025 W/m K.

The variable factor is the generally understood characteristics of the material used to perform the computer simulation, including the physical quantities corresponding to individual plastics identifying their processing properties: among others, the melt flow rate, melt and solid density, and heat conductivity coefficient. The simulation was performed for five types of plastics, four of which are polypropylene varieties, and one is high-density polyethylene. These materials are presented in more detail later in the article.

The results of the computer simulation, treated as the basis for further analysis, were divided into factors related to the course of the injection process, factors related to the operation of the injection molding machine, and factors describing the properties of the obtained injection molding. Among other things, the influence of the type of material on the length of the injection phase, the packing and cooling phase, the risk of failure to fill the mold cavity, the risk of the sink marks, pressure loss in the mold, material flow rate, injection force, mold clamping force, molded part weight, shrinkage, warpage and deformation of the molded part, and change of curvature is important.

3.4 Materials used in the computer simulation

The following materials were used: PP HG313MO homopolymer, PP RE420MO random copolymer, PP RF365MO random copolymer, PP BorPure RJ377MO random copolymer, and HDPE MB5568. Only one manufacturer’s materials, Borealis, have been specially selected to highlight how differently similar raw materials offered by one manufacturer may behave when processing. Table 1 presents selected values characterizing individual materials, taken from the CMDB – material database of the Cadmould 3D-F software [23].

Table 1

Selected properties of polymers used in simulations [23]

Simulation number Simulation No. 1 Simulation No. 2 Simulation No. 3 Simulation No. 4 Simulation No. 5
Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Type of material Homopolymer PP Random copolymer PP Random copolymer PP Random copolymer PP High-density PE
Density at room temperature (kg/m3) 910 928 905 905 956
Density at processing temperature (kg/m3) 741 775 702.7 687 699
Young’s modulus (MPa) 1,500 1,671 1,150 1,100 1,000
Specific heat (J/kg °C) 2,459 2,947 2,776 2,859 2,038
Thermal conductivity W/(m K) 0.159 0.110 0.185 0.189 0.248
Thermal diffusivity (mm2/s) 0.087 0.048 0.095 0.096 0.174
Mass melt flow rate (g/10 min) 30 (230°C; 2.16 kg) 10 (230°C; 2.16 kg) 20 (230°C; 2.16 kg) 42 (230°C; 2.16 kg) 0.8 (190°C; 2.16 kg)
Injection temperature range (°C) 210 ÷ 250 235 210 ÷ 260 210 ÷ 260 190 ÷ 250
Mold temperature range (°C) 10 ÷ 30 35 30 ÷ 40 15 ÷ 40 10 ÷ 40
No flow temperature (°C) 154 175 129 129 120
Ejection temperature (°C) 110 110 86 104 70

Figure 4 presents a graph of the relationship between the viscosity of the material and the shear rate (Carreau diagram). It shows that the viscosity of the polymer decreases with increasing shear rate and also decreases with increasing processing temperature. Figure 5 shows graphically the dependence of the specific volume of the polymer on the process temperature and pressure (pvT diagram). Both charts were generated for polypropylene HG313MO from the Cadmould Material Database (CMDB) as examples. Similar charts are also available for the other materials used in the simulation.

Figure 4 Graph of the dependence of the viscosity of the PP HG313MO on the shear rate [23].
Figure 4

Graph of the dependence of the viscosity of the PP HG313MO on the shear rate [23].

Figure 5 Graph of the dependence of the specific volume of PP HG313MO on the process temperature and pressure [23].
Figure 5

Graph of the dependence of the specific volume of PP HG313MO on the process temperature and pressure [23].

4 Results

Cadmould 3D-F software presents the simulation results in graphic form and in text files. Selected simulation results related to the subject of this article were written down from text files and generated graphic files and placed in the appropriate tables. In the following figures, one sample bitmap illustrating a particular group of results is shown to limit the number of illustrations and maintain an appropriate volume of the article.

Figures 6 and 7 show graphically the places where problems with filling the mold cavity (Filing Problems module) and the places where surface sunken areas (Sink Marks module) can be expected. The probability of the occurrence of these defects of the molded part has been divided into three groups – the area where the risk of collapse of the mold surface or failure to fill the mold cavity is negligible is marked in green, the areas of the molding are marked in yellow where the probability of occurrence of the previously mentioned defects is medium, while the areas marked in red are where the risk of these defects is very high. In the case of simulations No. 1, 2, 3, and 4, the red areas were absent at all, and the yellow areas were minimal at the outer edge of the cap. Figure 6 shows the result of simulation No. 5, where the yellow area took the most space compared to the results of the other simulations, but in the overall summary it can be stated that the risk of failure to fill the mold cavity for all five materials is negligible. Figure 7 shows a small area on the inside of the cap, occurring only in the case of simulation No. 4, where the average risk of sink marks is marked in yellow. In the case of other simulations, there were no yellow or red areas at all, so it can be concluded that the cap model was designed correctly, with the appropriate selection of wall thickness.

Figure 6 Visualization of places where slight problems with filling the mold may occur, simulation No. 5, polymer PP MB5568.
Figure 6

Visualization of places where slight problems with filling the mold may occur, simulation No. 5, polymer PP MB5568.

Figure 7 Visualization of places where minor problems with the sink marks may occur, simulation No. 4, polymer PP RJ377MO.
Figure 7

Visualization of places where minor problems with the sink marks may occur, simulation No. 4, polymer PP RJ377MO.

Figure 8 shows the result of the simulation related to the analysis of the filling phase and the time of filling the molding cavities, on the example of simulation No. 2 concerning the RE420MO material. Figure 9 presents the simulation result illustrating the pressure loss distribution in the molding seat, based on the example of simulation No. 1, for the HG313MO material. Packing phase time, cooling time, and flow rate were read from simulation results saved in text files.

Figure 8 Example of results showing injection phase and mold filling time, simulation No. 2, polymer PP RE420MO.
Figure 8

Example of results showing injection phase and mold filling time, simulation No. 2, polymer PP RE420MO.

Figure 9 Example of results showing pressure loss in a cavity, simulation No. 1, polymer PP HG313MO.
Figure 9

Example of results showing pressure loss in a cavity, simulation No. 1, polymer PP HG313MO.

Table 2 lists selected factors describing the course of the injection molding process from all five simulations. The HG313MO material had the shortest filling phase time (0.582 s), and the longest for MB5568 material, equal to 0.741 s. The packing phase time was the shortest for MB5568 (1.239 s), and the longest for RE420MO, amounting to 2.996 s. The shortest cooling time was achieved by the HG313MO material (4.302 s), and the longest by the RF365MO material (7.635 s). The comparison of the total time of all phases is shown in Figure 10. The most favorable, i.e., the lowest value, was achieved for the HG313MO material.

Table 2

List of factors characterizing the course of the injection process

Simulation number Simulation No. 1 Simulation No. 2 Simulation No. 3 Simulation No. 4 Simulation No. 5
Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Filling time (s) 0.582 0.610 0.583 0.587 0.741
Packing time (s) 1.430 2.996 2.101 1.527 1.239
Cooling time (s) 4.302 6.839 7.635 4.852 5.331
Flow rate (cm3/s) 33.441 31.267 33.441 33.441 26.686
Pressure loss in the mold cavity (MPa) 20.22 23.71 16.76 12.60 48.15
Figure 10 Comparison of the total times: injection phase, packing phase, and cooling for individual polymers.
Figure 10

Comparison of the total times: injection phase, packing phase, and cooling for individual polymers.

The flow rate of the material in the filling phase in the case of the four types of polypropylene differed slightly, while an explicit decrease, about 20%, occurred for high-density polyethylene. Figure 11 shows a comparison of the simulation results for the flow rate for each of the five materials. Lower flow rate of the material reduces the shear rate and smoother filling the mold cavity with the material, reducing the risk of burns and mechanical and thermal degradation of the material.

Figure 11 Comparison of the flow rate of the material when filling the mold cavities in individual simulations.
Figure 11

Comparison of the flow rate of the material when filling the mold cavities in individual simulations.

Figure 12 shows a comparison of the pressure loss in the mold cavity during its filling with plastic. The most favorable value, equal to 12.60 MPa, was achieved for the MB5568 material.

Figure 12 Comparison of pressure loss in the mold cavity for individual simulations.
Figure 12

Comparison of pressure loss in the mold cavity for individual simulations.

Table 3 presents a list of technological factors selected from the simulation results related to the operation of the injection molding machine. The values of individual results were received from text files, only the injection power values were read using the “Result Selection” module and the “Diagrams” option. The maximum temperature of the material, resulting from the settings of the heaters in the plasticizing system, slightly differed in the case of all five materials, therefore its influence on the selection of the material most suitable for the cap was ignored.

Table 3

Summary of factors related to the operation of the injection molding machine

Simulation number Simulation No. 1 Simulation No. 2 Simulation No. 3 Simulation No. 4 Simulation No. 5
Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Maximum temperature of the polymer (°C) 239.1 246.1 240.6 238.6 237.2
X-axis clamping force (kN) 92.858 90.891 93.594 96.620 55.490
Y-axis clamping force (kN) 140.522 137.390 141.430 145.959 86.662
Z-axis clamping force (kN) 93.884 91.732 93.916 97.001 56.014
Injection power (kW) 2.780 2.501 3.784 6.470 1.281

Figure 13 presents charts prepared on the basis of data from the Cadmould software, showing a comparison of the mold clamping force, distributed along the X, Y, and Z axes for individual simulations. The diagrams of forces on the X and Z axes are similar, which results from the axial symmetry of the analyzed part. The Y-axis component, consistent with the main axis of the injection molding machine and the injection direction, had the greatest influence on the resultant value of the force. The most favorable result, i.e., the lowest force, was acquired for high-density polyethylene MB5568.

Figure 13 Comparison of the injection mold clamping force for individual materials: (a) force in the X axis, (b) force in the Y axis, (c) force in the Z axis.
Figure 13

Comparison of the injection mold clamping force for individual materials: (a) force in the X axis, (b) force in the Y axis, (c) force in the Z axis.

Figure 14 shows a comparison of the injection power demand for individual materials. The most favorable, i.e., the lowest, result was obtained for the MB5568 material, which is consistent with the previously discussed results regarding the injection mold clamping force.

Figure 14 Comparison of injection power demand for each simulation.
Figure 14

Comparison of injection power demand for each simulation.

Table 4 presents a list of factors related to the geometry of the injection molding selected from the simulation results, generally understood as pro-quality factors. The mass of the part, changing depending on the type of material, was read from the results saved in text files, while the maximum values of warpage, shrinkage, deformation, and curvature change were read from saved graphic files, examples of which are shown in Figures 15–18. The difference between the contour of the mold cavity and the deformed shape of the molded part, visible in Figure 18, is specially enlarged in order to better visualize the shape and directions of the warpage; in fact, the degree of warpage ranges from −5.860 to 6.6872% and in real scale it would be difficult to show.

Table 4

List of factors related to the geometry of the injection molding

Simulation number Simulation No. 1 Simulation No. 2 Simulation No. 3 Simulation No. 4 Simulation No. 5
Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Part mass (g) 3.9 4.0 3.8 3.8 4.2
Maximum warpage (mm) 0.151 0.052 0.178 0.159 0.289
Maximum linear shrinkage (%) 2.194 1.682 2.355 3.304 2.419
Maximum deformation (mm) 0.300 0.283 0.304 0.419 0.344
Curvature change (%) −5.860 to 6.687 −2.267 to 1.906 −6.896 to 7.944 −7.827 to 8.863 −8.505 to 9.985
Figure 15 Example of results showing part warpage, simulation No. 3, polymer RF365MO.
Figure 15

Example of results showing part warpage, simulation No. 3, polymer RF365MO.

Figure 16 Example of results showing part shrinkage, simulation No. 4, polymer RJ377MO.
Figure 16

Example of results showing part shrinkage, simulation No. 4, polymer RJ377MO.

Figure 17 Example of results showing molding deformation (as sum of warpage and shrinkage), simulation No. 5, polymer MB5568.
Figure 17

Example of results showing molding deformation (as sum of warpage and shrinkage), simulation No. 5, polymer MB5568.

Figure 18 Example of results showing a curvature change of a molded part, simulation No. 1, polymer HG313MO.
Figure 18

Example of results showing a curvature change of a molded part, simulation No. 1, polymer HG313MO.

Figure 19 presents a list of mass of caps made of specific types of materials. The most significant mass equal to 4.2 g (with the same volume) was the molding made of MB5568, while the most favorable mass, i.e., the smallest, received the caps made of RF365MO and RJ377MO. In their case, the value of the cap mass obtains 3.8 g, which gives a raw material saving of 9%.

Figure 19 Comparison of the mass of moldings in each simulation.
Figure 19

Comparison of the mass of moldings in each simulation.

Figure 20 shows a summary of the deformation results obtained in individual simulations. Since the deformation is the geometric sum of the warpage and the shrinkage, no separate comparative plots for the warpage and the shrinkage were generated, showing only the corresponding data in Table 4. The most accurate molding, with the smallest deformation equal to 0.283 mm, was the cap made of RE420MO material. The largest difference between the dimension of the solid model and the real shape of the molded part occurred in the case of RJ377MO material and amounted to 0.419 mm.

Figure 20 Summary of the molding deformation results obtained in simulations for individual materials.
Figure 20

Summary of the molding deformation results obtained in simulations for individual materials.

Figure 21 presents a graphical summary of the curvature change values of injection moldings made of specific materials. A negative value of the curvature represents a concave surface, while a positive value means its convexity. The best result, i.e., the lowest value of the curvature change, was achieved for the RE420MO material.

Figure 21 Comparison of curvature changes in moldings made of each polymer analyzed by numerical method.
Figure 21

Comparison of curvature changes in moldings made of each polymer analyzed by numerical method.

5 Discussion

On the basis of the obtained simulation results, it is possible to conclude on the selection of the most appropriate material for the analyzed injection molding. The individual factors describing the course of the injection molding process, describing the operation of the injection molding machine and the properties of the injection molded part, can be assigned an appropriate role related to the economic or quality criteria. Depending on which of these criteria is more important in a given case, it can be assigned a higher criterion weight. For example, in the case of precision moldings, the qualitative criterion will be more important, while in the case of general-purpose moldings, the economic criterion will be more important. The choice of authors, on the basis of which the analyzed factors have been defined as technological, economic, and qualitative, is a subjective choice, because it can be discussed whether the clamping force of the injection mold is more of a technological factor. From the point of view of the energy demand of the injection molding machine and the energy costs needed to generate one kN of force clamping the mold, considering this force as an economic factor seems justified.

Table 5 summarizes the separate factors analyzed in the computer simulations, the numerical results of which are presented in Tables 2–4 as well as on the relevant charts. The individual values of separate factors were assigned point values from 4 to 0. Value 4 meant the most favorable impact, while the value 0 – the least favorable. In the event that the indicated results did not differ significantly, they were assigned the identical number of criteria points without grading the lowest value into the highest value. The material with the highest total point value will be considered to be the best material for making the cap by injection.

Table 5

List of scores for the analyzed quantities characterizing the injection process of the cap

Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Technological factors
  Flow rate (cm3/s) 0 3 0 0 4
  Pressure loss in the mold cavity (MPa) 2 1 3 4 0
Economic factors
  Filling time (s) 4 1 0 3 2
  Packing time (s)
  Cooling time (s)
  Maximum temperature of the polymer (°C) 0 0 0 0 0
  X-axis clamping force (kN) 0 0 0 0 4
  Y-axis clamping force (kN)
  Z-axis clamping force (kN)
  Injection power (kW) 2 3 1 0 4
  Part mass (g) 2 1 4 4 0
Qualitative factors
  Maximum warpage (mm) 2 4 3 0 1
  Maximum linear shrinkage (%)
  Maximum deformation (mm)
  Curvature change (%) 3 4 2 1 0

Table 6 presents a summary of the points, when each of the analyzed values was assigned the same importance. As a result, the polypropylene random copolymer RE420MO received the most points and it should be considered as the most suitable material for the cap.

Table 6

Summary points with equal weighting of all criteria

Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Technological factors 2 4 3 4 4
Economic factors 8 5 5 7 10
Qualitative factors 5 8 5 1 1
15 17 13 12 15

Bold numbers indicate the best polymer for making the part.

The situation in which the basic selection criterion represents the economic factor is presented in Table 7. Factors influencing in various ways the cost of production of the molded part were assigned a double weight, therefore the highest result was achieved by BorPure MB5568 high-density polyethylene.

Table 7

Summary points according to the criterion of the economic

Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Technological factors 2 4 3 4 4
Economic factors (double) 16 10 10 14 20
Qualitative factors 5 8 5 1 1
23 22 18 19 25

Bold numbers indicate the best polymer for making the part.

In another approach, when the qualitative factor was considered the selection criterion, the results of the analysis are shown in Table 8. Factors influencing the dimensional accuracy of the molded part were assigned a double value, which resulted in the fact that the RE420MO material scored the most points.

Table 8

Summary points according to the qualitative criterion

Polymer trade name HG313MO RE420MO RF365MO RJ377MO MB5568
Technological factors 2 4 3 4 4
Economic factors 8 5 5 7 10
Qualitative factors (double) 10 16 10 2 2
20 25 18 13 16

Bold numbers indicate the best polymer for making the part.

6 Conclusion

The injection process, as a method of producing plastic parts with complex shapes and very high dimensional accuracy, is becoming an increasingly popular and leading process nowadays – not only in the group of plastic processing methods, but also compared to other manufacturing technologies. Its main advantage is the ability to get products with complex shapes in a very short time, in one technological operation, on one machine, using one tool, while maintaining a very high accuracy of performance and repeatability of the product quality. The unit cost of making a plastic part, resulting from the cost of material, tool, and processing machine in the case of mass production and mass production in the case of injection, is many times lower than the cost of making the same product by combined methods of machining, plastic, or other processing. This resulted in the displacement of metal parts with plastic parts wherever the plastic part meets the strength expectations.

The main task of each CAD/CAM/CAE software is to support and shorten the time of work related to the construction of the product, to facilitate analyses in terms of correctness of performance, and to simplify the creation of technical documentation and the manufacturing process. Injection simulation programs, even if they do not detect all errors that may appear during the realization of the manufacturing process, certainly significantly reduce the risk of their occurrence and help choose the right course of action at the design stage, not just at the manufacturing stage.

On the basis of computer simulations, the process of injection of a popular object made of plastic, which is a screw cap, into the packaging in the form of a bottle was analyzed. Billions of pieces of these items are produced and even a slight alteration in the manufacturing conditions of a single cap has significant effects on mass production. Five plastics were selected for simulations, intended (as indicated by the information provided by manufacturers) for use in the packaging industry. The obtained simulation results were analyzed in terms of economic, technological, and qualitative factors. Individual factors were assigned scores in points, and depending on the significance of a given criterion (economic or qualitative), an additional modifier in the form of a weight was applied, multiplying the points from the evaluation, respectively. Based on the evaluation method used, without emphasizing a specific criterion, the polypropylene random copolymer RE420MO was considered to be the best material for the cap, which was identified by better properties than other materials. When choosing the economic criterion, the best to make a cap turned out to be high-density polyethylene under the name of BorPure MB5568. The indication of this type of raw material is interesting because both the packaging and the cap can be made of the same material, which improves waste separation. When choosing the quality criterion related to getting the dimensional accuracy of the compact, the best material was again polypropylene random copolymer RE420MO.

On the basis of the obtained results, conclusions of an analytical and utilitarian nature were drawn, indicating that by means of numerical simulation of the injection process, it is possible to determine the right choice of material for the production of a specific injection molding, which will meet the appropriate quality criteria. Currently, it is possible to predict the effect of the injection molding machine more and more precisely with the help of simulation programs, and the continuous development of this type of software means that the simulation results are closer to experimental measurements.

  1. Conflict of interest: Authors state no conflict of interest.

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Received: 2021-07-11
Revised: 2021-08-17
Accepted: 2021-08-23
Published Online: 2021-10-07

© 2021 Tomasz Jachowicz et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2021-0094
Keywords for this article
injection molding; numerical modeling; computer simulation; material selection; characteristics of the injection molding.
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Electrochemical studies of the synergistic combination effect of thymus mastichina and illicium verum essential oil extracts on the corrosion inhibition of low carbon steel in dilute acid solution
  3. Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems — Case Study of XYZ Company
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  121. Design of production lines and logistic flows in production
  122. The planning process of transport tasks for autonomous vans
  123. Modeling of the two shuttle box system within the internal logistics system using simulation software
  124. Implementation of the logistics train in the intralogistics system: A case study
  125. Assessment of investment in electric buses: A case study of a public transport company
  126. Assessment of a robot base production using CAM programming for the FANUC control system
  127. Proposal for the flow of material and adjustments to the storage system of an external service provider
  128. The use of numerical analysis of the injection process to select the material for the injection molding
  129. Economic aspect of combined transport
  130. Solution of a production process with the application of simulation: A case study
  131. Speedometer reliability in regard to road traffic sustainability
  132. Design and construction of a scanning stand for the PU mini-acoustic sensor
  133. Utilization of intelligent vehicle units for train set dispatching
  134. Special Issue: ICRTEEC - 2021 - Part I
  135. LVRT enhancement of DFIG-driven wind system using feed-forward neuro-sliding mode control
  136. Special Issue: Automation in Finland 2021 - Part I
  137. Prediction of future paths of mobile objects using path library
  138. Model predictive control for a multiple injection combustion model
  139. Model-based on-board post-injection control development for marine diesel engine
  140. Intelligent temporal analysis of coronavirus statistical data
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