Published at : 04 Apr 2023
Volume : IJtech
Vol 14, No 2 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i2.5573
Chiwapon Nitnara | Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, 1518 Pracharat I, Bangsue, Bangkok 10800, Thailand |
Kumpon Tragangoon | Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, 1518 Pracharat I, Bangsue, Bangkok 10800, Thailand |
Plastic injection molding is one of the most used
methods for producing plastic products because it can be produced at a high
production rate, low cost, and ease in manufacturing. However, one defect that
affects product quality is namely warpage. To reduce plastic product warpage,
the injection molding process is required optimal process control to
increase plastic product quality. The objective of this paper is to optimize
injection molding process parameters for minimizing the warpage of plastic
glass. The optimization process is divided into two phases.
The Finite Element Method (FEM) was employed in the first phase to
simulate 32 experiments under various parameters. The parameters of this
process consist of melt temperature ranging from 180 to 230 °C, mold
temperature in the range of 20 – 45 °C, filling time from 0.82 to 0.92 s,
packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the
second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA)
was developed to predict the warpage and solve the optimization process to find
optimal parameters. Combining the intelligent method shows that ANN and GA
effectively find the optimal process parameters that can reduce the warpage of
the product by 35.73% from the maximum value.
Artificial Neural Network (ANN); Finite Element Method (FEM); Genetic Algorithm (GA); Optimization; Plastic injection molding
Plastic
injection molding is the most popular process for producing plastic packaging,
medical equipment, automotive parts, and electronics. In the plastic injection
molding process, the plastic pellets are melted at high temperatures by a
heater of the injection machine. Subsequently, melted plastic is injected into
the mold cavity and core with specific injection pressure and packed by packing
pressure. Finally, the melted plastic is cooled down to transform into a
plastic product (Kitayama et al., 2020; Cheng
and Liu, 2018; Hasnan et al., 2017).
However, the process parameters, such as the melt temperature of the plastic,
injection pressure, packing pressure, packing time, and cooling time, affect
the resulting product's properties. The properties of plastic products from the
injection molding process are required a high strength-to-weight ratio and
durability. Controlling the process parameters is necessary for obtaining the
best plastic product property.
Traditionally, the conventional practice of determining injection molding
process parameters is adjusted through trial and error by experienced engineers
(Guo et al., 2019). However, this
method cannot precisely determine the optimal process parameters, resulting in
time-consuming, repetitive testing and easy occurring defect. The most common
type of defect that occurs in the plastic injection molding process is called
warpage which affects the quality of the product (Huang
et al., 2021; Gao
and Wang, 2008; Kurtaran and Erzurumlu,
2006; Hakimian and Sulong, 2012). The warpage of the product continues
occurring because several related process parameters and independent process
parameters intervene with the plastic injection molding process.
Computer-aided engineering
(CAE) was a technology for numerical simulation of the plastic injection
molding process (Hentati et al., 2019).
The advantage of CAE was less cost and faster experimenting virtually.
Additionally, CAE serves as a tool for predicting the behavior of defects that
may impact the product's quality, as well as for the validation and
optimization of the product's design. At present, the intelligent method is
widely used in combination with CAE to optimize the plastic injection molding
process for reducing defects such as artificial neural networks, genetic
algorithms, support vector machine, Etc (Zhao et
al., 2020).
Several studies have
investigated the optimization of the injection molding process by different
techniques. Shi, Xie, and Wang (2013) optimized plastic injection process parameters to reduce warpage by using
the Kriging surrogate model. Erzurumlu and Ozcelik
(2006) minimized warpage and sink marks of plastic parts under different
design rib cross-section types and rib layout angles by using Taguchi
optimization. Oliaei et al. (2016) optimized
plastic injection process parameters by Taguchi's orthogonal array, and ANN was
selected as the optimal parameter. Zhang et al.
(2016) presented particle swarm optimization on the oil cooler cover cooling
and a cooling channel to reduce warpage. Zhou, Turng,
and Kramschuster (2006) used SVR and GA to optimize the process
parameters. Dang (2014) used direct and
metamodel-based methods as optimization injection molding process parameters. Farshi, Gheshmi, and Miandoabchi (2011) presented
the Evolutionary Operation (EVOP) method used to minimize the warpage and
shrinkage defects of plastic parts. Lockner and
Hopmann (2021) used network-based transfer learning to reduce data of
artificial neural network training for optimizing injection machine parameters.
The objective of this paper
is to find the optimal injection molding process parameters for reducing
warpage by determining the injection molding process parameters of plastic
glass. we conducted experiments using a simulation method to assess warpage
under various process parameters, including melt temperature, mold temperature,
filling time, packing time, and cooling time. To further refine our results, we
used artificial neural networks (ANN) to predict warpage based on simulation
data and developed a Fitness Function Equation. Finally, we utilized the GA
method to identify the optimal injection molding process parameters that will
reduce warpage in plastic glass.
2.1. Sample Part
In this experiment, the part is a plastic glass used for an experimental simulation. The dimensions have a diameter of 97 mm, height of 70 mm, and thickness of 2 mm. Plastic glass is made of polystyrene (PS), which is widely used in consumer goods and commercial packaging. The general view of the part is presented in (Figure 1).
Figure 1 Plastic Glass
2.2. Experiment
The
Finite Element Method (FEM) was developed to simulate the behavior of plastic material
(Hashash, Jung, and Ghaboussi, 2004; Hung, Chen, and Lin, 2002). In addition, the FEM capability can
help improve the defects that may occur before actual production (Irsyad et al., 2020). A MOLDEX 3D software
was used to simulate the plastic injection molding process to determine the
defect of the sample part. This software uses the Finite Element Method to
analyze the plastic behavior of the injection molding process by a mathematical
function. In this simulation, various parameters are adjusted to determine the
minimum warpage value.
The experiment
simulation was created by the design of the experiment (DOE) method with 32
experiments. The process parameters of injection molding experiments consist of
the melt temperature, mold temperature, filling time, packing time, and cooling
time were chosen as input parameters as shown in Table 1.
Table
1 Process parameters
Process parameters |
Level | |
Low |
High | |
Melt temperature (degree) |
180 |
230 |
Mold Temperature (degree) |
20 |
45 |
Filling Time (mm/s) |
0.82 |
0.94 |
Packing Time (s) |
5.88 |
7 |
Cooling Time (s) |
14 |
18 |
2.3. Warpage
The warpage is a distortion of the dimension part on 3 axes consisting
of x, y, and z from the actual dimension of the part. Adjusting suitable
injection molding process parameters is essential to decrease the warpage of
parts, which is the main purpose of this paper. The warpage was measured as the
total displacements on 3 axes of the product. An equation for the warpage is (Mukras, Omar, and al-Mufadi, 2019):
TWsum =
? ymax i (1)
i = 1, 2, 3
where ymaxi is the
displacement on one axis of the product.
This paper consists of two phases: First, a simulation of injection molding process experiments. It has 32 experiments under different parameter setting values to determine the warpage value. Second, ANN has conducted predicted warpage from the simulation result of the experiment. The result from predicting the warpage value of ANN has created a mathematical model by coefficient value for fitness function of GA. Optimization by the GA method was performed to find the optimal injection molding parameter using a mathematical model from the ANN method, which results in the lowest warpage. (Figure 2) shows the process of this paper.
Figure 2 Process of paper
3.1. Simulation
Moldex3D software was used
in this experiment to simulate the injection molding process (Sun et al., 2021; Quintana and Frontini, 2020; Tseng,
Chang, and Hsu, 2017) with 32 experiments in order to find out the
warpage value. Table 2 shows results gained from the simulation, and mold base
for analysis were used for the ANN method. (Figure 3) depicts the maximum
warpage result that occurs on the red color area of the example plastic glass
in experiment No. 1, which is 0.809 mm after the experiment simulation was
conducted, and the mold base for analysis has 8 cooling channels used to heat
transfer.
Table 2 Results of experiments simulation
No. |
Melt Temperature |
Mold Temperature |
Filling Time |
Packing Time |
Cooling Time |
Warpage | |||||||
1 |
180 |
20 |
0.82 |
5.88 |
14 |
0.809 | |||||||
2 |
230 |
20 |
0.82 |
5.88 |
14 |
1.119 | |||||||
3 |
180 |
45 |
0.82 |
5.88 |
14 |
0.848 | |||||||
4 |
230 |
45 |
0.82 |
5.88 |
14 |
1.205 | |||||||
5 |
180 |
20 |
0.94 |
5.88 |
14 |
0.823 | |||||||
6 |
230 |
20 |
0.94 |
5.88 |
14 |
1.122 | |||||||
7 |
180 |
45 |
0.94 |
5.88 |
14 |
0.854 | |||||||
8 |
230 |
45 |
0.94 |
5.88 |
14 |
1.198 | |||||||
9 |
180 |
20 |
0.82 |
7 |
14 |
0.801 | |||||||
10 |
230 |
20 |
0.82 |
7 |
14 |
1.09 | |||||||
11 |
180 |
45 |
0.82 |
7 |
14 |
0.842 | |||||||
12 |
230 |
45 |
0.82 |
7 |
14 |
1.154 | |||||||
13 |
180 |
20 |
0.94 |
7 |
14 |
0.828 | |||||||
14 |
230 |
20 |
0.94 |
7 |
14 |
1.124 | |||||||
15 |
180 |
45 |
0.94 |
7 |
14 |
0.84 | |||||||
16 |
230 |
45 |
0.94 |
7 |
14 |
1.165 | |||||||
17 |
180 |
20 |
0.82 |
5.88 |
18 |
0.8 | |||||||
18 |
230 |
20 |
0.82 |
5.88 |
18 |
1.096 | |||||||
19 |
180 |
45 |
0.82 |
5.88 |
18 |
0.843 | |||||||
20 |
230 |
45 |
0.82 |
5.88 |
18 |
1.176 | |||||||
21 |
180 |
20 |
0.94 |
5.88 |
18 |
0.815 | |||||||
22 |
230 |
20 |
0.94 |
5.88 |
18 |
1.09 | |||||||
No. |
Melt Temperature |
Mold Temperature |
Filling Time |
Packing Time |
Cooling Time |
Warpage |
| ||||||
23 |
180 |
45 |
0.94 |
5.88 |
18 |
0.845 |
| ||||||
24 |
230 |
45 |
0.94 |
5.88 |
18 |
1.162 |
| ||||||
25 |
180 |
20 |
0.82 |
7 |
18 |
0.795 |
| ||||||
26 |
230 |
20 |
0.82 |
7 |
18 |
1.076 |
| ||||||
27 |
180 |
45 |
0.82 |
7 |
18 |
0.834 |
| ||||||
28 |
230 |
45 |
0.82 |
7 |
18 |
1.115 |
| ||||||
29 |
180 |
20 |
0.94 |
7 |
18 |
0.812 |
| ||||||
30 |
230 |
20 |
0.94 |
7 |
18 |
1.097 |
| ||||||
31 |
180 |
45 |
0.94 |
7 |
18 |
0.835 |
| ||||||
32 |
230 |
45 |
0.94 |
7 |
18 |
1.129 |
| ||||||
Figure 3
Simulation Analysis: (a)
Warpage; and (b) Mold base
3.2. Artificial Neural Network (ANN)
ANN is widely used in engineering because ANN ability can analyze
information by detecting data patterns and relationships through learning. It
is easier to analyze and improve engineering processes (Hemmati
et al., 2020; Alas and Ali, 2019;
Rafiq, Bugmann, and Easterbrook, 2001).
This section uses the ANN to predict the warpage results from the experiment simulation by MATLAB software. The experimental parameters are an input of ANN, consisting of melt temperature, mold temperature, filling time, packing time, and cooling time. During the network training, the weight (w) of the network is calculated by minimizing the error value between the predicted warpage value, which is called the output of ANN, and the actual warpage value (Chen et al., 2008; Lee and Lin, 2006; Sadeghi, 2000). (Figure 4) shows the ANN model consists of five inputs, the transfer function is sigmoid, ten hidden layers, and one output. A Backpropagation network (BPN) has been adopted because it has the ability of fast responsiveness and high accuracy (Asmael et al., 2022).
Figure 4
ANN Model
Using the warpage results
obtained by the simulation experiment to predict by ANN, Table 3 shows the
resulting warpage of ANN compared with the finite element method analysis of
the experiment.
Table
3 Results of ANN prediction
No. |
Melt Temperature |
Mold Temperature |
Filling Time |
Packing Time |
Cooling Time |
Warpage |
ANN Warpage |
1 |
180 |
20 |
0.82 |
5.88 |
14 |
0.809 |
0.804 |
2 |
230 |
20 |
0.82 |
5.88 |
14 |
1.119 |
1.028 |
3 |
180 |
45 |
0.82 |
5.88 |
14 |
0.848 |
0.847 |
4 |
230 |
45 |
0.82 |
5.88 |
14 |
1.205 |
1.223 |
5 |
180 |
20 |
0.94 |
5.88 |
14 |
0.823 |
0.846 |
6 |
230 |
20 |
0.94 |
5.88 |
14 |
1.122 |
1.022 |
7 |
180 |
45 |
0.94 |
5.88 |
14 |
0.854 |
0.87 |
8 |
230 |
45 |
0.94 |
5.88 |
14 |
1.198 |
1.248 |
9 |
180 |
20 |
0.82 |
7 |
14 |
0.801 |
0.81 |
10 |
230 |
20 |
0.82 |
7 |
14 |
1.09 |
1.102 |
11 |
180 |
45 |
0.82 |
7 |
14 |
0.842 |
0.847 |
12 |
230 |
45 |
0.82 |
7 |
14 |
1.154 |
1.148 |
13 |
180 |
20 |
0.94 |
7 |
14 |
0.828 |
0.813 |
14 |
230 |
20 |
0.94 |
7 |
14 |
1.124 |
1.118 |
15 |
180 |
45 |
0.94 |
7 |
14 |
0.84 |
0.838 |
16 |
230 |
45 |
0.94 |
7 |
14 |
1.165 |
1.216 |
17 |
180 |
20 |
0.82 |
5.88 |
18 |
0.8 |
0.809 |
18 |
230 |
20 |
0.82 |
5.88 |
18 |
1.096 |
1.1 |
19 |
180 |
45 |
0.82 |
5.88 |
18 |
0.843 |
0.846 |
20 |
230 |
45 |
0.82 |
5.88 |
18 |
1.176 |
1.137 |
21 |
180 |
20 |
0.94 |
5.88 |
18 |
0.815 |
0.821 |
22 |
230 |
20 |
0.94 |
5.88 |
18 |
1.09 |
1.084 |
23 |
180 |
45 |
0.94 |
5.88 |
18 |
0.845 |
0.86 |
24 |
230 |
45 |
0.94 |
5.88 |
18 |
1.162 |
1.126 |
25 |
180 |
20 |
0.82 |
7 |
18 |
0.795 |
0.789 |
26 |
230 |
20 |
0.82 |
7 |
18 |
1.076 |
1.063 |
27 |
180 |
45 |
0.82 |
7 |
18 |
0.834 |
0.827 |
28 |
230 |
45 |
0.82 |
7 |
18 |
1.115 |
1.105 |
29 |
180 |
20 |
0.94 |
7 |
18 |
0.812 |
0.819 |
30 |
230 |
20 |
0.94 |
7 |
18 |
1.097 |
1.066 |
31 |
180 |
45 |
0.94 |
7 |
18 |
0.835 |
0.818 |
32 |
230 |
45 |
0.94 |
7 |
18 |
1.129 |
1.168 |
The results showed that the mean square
error (MSE) of validation is 0.004, and the overall R-square value is 0.97985.
The mean square error (MSE) of ANN has a value of close to 0, and the R-square
is near 1. The average prediction error % of the ANN model was 1.97%. It
clearly shows that the ANN has a high performance in predicting the result of
the warpage as shown in (Figure
5).