Quality Improvement of Recycled Plastic Products Using Mixture Experiment

Charnnarong Saikaew Department of Industrial Engineering

Khon Kaen University Khon Kaen, Thailand

e-mail: charn_sa@kku.ac.th

Panita Sripaya Technology Management Center

National Science and Technology Development Agency Pathumtani, Thailand

e-mail: koong_king19@hotmail.com

Abstract— Recycling plastic has several advantages such as reducing consumption of energy, non-renewable fossil fuels use, and global emissions of carbon dioxide. In this study, the manufacturer would like to improve product quality and decrease cost of the products by recycling polycarbonates. The optimal amounts of mixture components to produce recycled plastic products should be determined. Thus, this research aims to study the effect of recycled polycarbonates with different colors and virgin polycarbonates on tensile strength and heat distortion temperature. By performing systematic experimentation, using mixture experiment, response surface methodology (RSM), and propagation of error (POE), the quality of the recycled plastic products can be improved and becomes more robust to variations at the optimal amounts of mixture components. Hence the manufacturer can use these settings of recycled plastics and virgin polycarbonate to produce quality products with low cost and environmental impact reduction.

Keywords- recycled plastics; plastic properties; quality; mixture experiment; response surface methodology

I. INTRODUCTION Producing plastic has an environmental impact because it

uses many resources and fossil fuels. There is approximately 3 million tons of plastic waste each year [1]. Environmental agency reports that around 80% of plastic waste is reaching landfill sites [1]. This is a major problem cause for land fill sites increasing and environmental impact. The practical solution is to recycle or reuse the plastic that has already been produced. In fact, recycling plastic has many advantages such as reducing consumption of energy, non-renewable fossil fuels use, as well as global emissions of carbon dioxide. Ref. [2] proposes a model of plastics recycling to prove whether it can reduce the amount of waste. Ref. [3] proves that recycling plastic can reduce the amount of waste to landfill and the overall environmental burden. Plastics that can be recycled are called thermoplastic polymers. Thermoplastic polymers become soft and deformable upon heating [4]. Some typical examples of thermoplastic polymers are polyethylene, polypropylene, polyvinyl chloride, polycarbonate, etc. These plastics include the various films, fabric, and packaging materials.

A plastic manufacturer produces various commodity plastic parts assembly for use in electric devices. A variety of

the plastic products illustrates in Fig. 1. The manufacturer would like to improve product quality and decrease cost of the products by recycling polycarbonates. Certainly, polycarbonate is one of thermoplastic polymers that is easily recycled and molded. Some typical properties of these thermoplastics are stronger, usable in a larger temperature range, highly transparent to visible light, temperature resistance, impact resistance, and optical properties [4]. However, these plastics are more expensive. This is a good opportunity to recycle. A major issue is that mixture of different types and colors of recycled polycarbonates has high impact on plastic properties such as tensile strength, tensile modulus, impact strength, fatigue, heat resistance, etc. This can make low quality of the recycled plastic products. Even though producing recycled plastic products can reduce environmental impact and cost of the product, quality of the product should be also considered. Ref. [5] states that if amount of recycled plastics is mixed too much to produce a new product, this can decrease mechanical properties of the product such as tensile strength and impact strength. This statement can be confirmed by [6]. Thus, optimal amounts of mixture components to produce recycled plastic products should be concerned.

This research aims to study the effect of recycled polycarbonates with different colors (i.e., transparent color, white color, muddy color) and virgin polycarbonate on tensile strength and heat distortion temperature. The optimal amounts of mixture components to produce recycled plastic products will be determined. Mixture experimental design is one of the most wildly used techniques for studying the effect of mixture components on quality characteristics. This design is applied to various engineering materials such as ceramic [7], [8], [9], metals and alloys [10], [11], polymers [12], [13]. To determine the optimal amounts of mixture components to produce recycled plastic products, response surface methodology coupled with mixture experiment is employed [12], [13]. However, the method of propagation of error (POE) has not been used for determining the optimal amounts of mixture components to produce recycled plastic products. The concept of this method is to minimize variation in the quality characteristics. Thus, this study will apply mixture experiment, response surface methodology, and propagation of error to recycled plastic parts assembly for use in electric devices.

2009 Second International Conference on Environmental and Computer Science

978-0-7695-3937-9/09 $26.00 © 2009 IEEE

DOI 10.1109/ICECS.2009.13

312

Figure 1. Plastic parts assembly for use in electric devices.

II. RESEARCH METHODOLOGY

A. Materials The tensile strength test is performed on plastics tensile

test machine according to ASTM D638 [14]. Fig. 2 illustrates a specimen for tensile test. A specimen is produced by injection molding machine. The test method consists of applying a load to a specimen at a rate which is within a prescribed range until failure occurs. The typical test includes three replicates per run for tensile strength test. To investigate the effect of heat distortion temperature on the quality of the product, specimen is tested on a machine according to ASTM D648 [14]. The test specimen is loaded in three-point bending in the edgewise direction. The temperature is increased at 2 °C/min until the specimen deflects 0.25 mm. The typical test includes three replicates per run for heat distortion temperature test. Fig. 3 illustrates a specimen for heat distortion temperature test.

Figure 2. A specimen for tensile test.

Figure 3. A specimen for heat distortion temperature test.

B. Methodology In this research work, a seven-step approach involved

planning and conducting the experiment is described as follows:

1) Identifying mixture components to produce recycled plastic products (i.e., recycled polycarbonate--transparent color, recycled polycarbonate--white color, recycled polycarbonate--muddy color, and virgin polycarbonate) and identify the responses of the problem (i.e., tensile strength and heat distortion temperature);

2) Select appropriate amounts of mixture components (i.e., varying the amounts of mixture of four kinds of components based on mixture experiment design);

3) Choose the matrix of experimental design (This problem involves a set of constrained plastic mixtures of recycled and virgin polycarbonate (mixture of four kinds of components). D-optimal design with mixture experiment is selected for solving this problem due to the concept of minimizing the variance of the regression coefficients.);

4) Conducting the experiment as per the design matrix and collect experimental data;

5) Analyzing experimental data by using one of the most widely used techniques of statistical analysis (i.e., analysis of variance, ANOVA).

6) Determine optimal amounts of mixture components by using response surface methodology (RSM) with desirability function and propagation of error (POE);

7) Verify the validation of the optimal amounts of mixture components by conducting some experiments at the optimal amounts of mixture components (i.e., the optimal operating settings) and constructing 95% confidence interval of each of responses.

The seven-step approach involved planning and conducting the experiment is illustrated in Fig. 4. In the sixth step, RSM and POE are used to determine the optimal amounts of mixture of four kinds of components for product quality improvement. Response surface methodology, by definition, is “a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several factors and the objective is to optimize this response” [15]. The steps in RSM are as follows: 1) designing of a set of experiments for adequate and reliable measurement of the true mean response of interest, 2) determining the mathematical model with best fits, 3) obtaining the optimum amounts of mixture of four kinds of components that produces maximum or minimum value of response, and 4) depicting the effects of varying the amounts of mixture of components on the two responses through two dimensional graph (i.e., contour plot) and the three dimensional graph (i.e., response surface plot).

313

Figure 4. Seven-step approach involved planning and conducting the experiment.

To make the product more robust to variations in the amounts of mixture of four kinds of components, the method of propagation of error (POE) is a useful technique for product quality improvement. POE is the amount of error that is transmitted to the response from variation in the amounts of mixture of four kinds of components. The POE method finds settings that minimize variation in the responses. It involves application of partial derivatives to locate flat areas on the response surface, preferably high plateaus. The POE method provides more accurate results when the amounts of mixture of four kinds of components have lower standard deviation than when they have higher standard deviations. By using the optimization routines (i.e., direct search and downhill simplex methods) to minimize the POE value, this method will obtain the optimal operating settings that produce a response that is robust to fluctuations in the amounts of mixture of four kinds of components. For more detailed information about POE, refer to [13], [15].

This research problem involves the analysis of multi-response variables (i.e., tensile strength and heat distortion temperature). Simultaneous consideration of multi-response variables is to build an appropriate response surface model for each response variable and try to obtain a set of optimal amounts of mixture that optimizes all response variables or keeps them in desired ranges [13]. To solve such the problem, a useful technique to simultaneous optimization of multi-response variables is the use of desirability functions presented by [16]. More detailed information about desirability function can be found in [13], [15], [16], [17].

III. RESULTS AND DISSCUSSION Table I illustrates mixture experiment D-optimal design

for recycled plastic manufacturing experiment of the amounts of mixture of four kinds of components affecting tensile strength and heat distortion temperature. Since this problem involves constrains of mixture components, the total of components is equal to 1 or 100 percent [18]. Based on the constrains of each of polycarbonates, the optimization

models for obtaining the optimal amounts of mixture components are as follows

1004321 =+++ xxxx . (1)

20040204020

3010

4

3

2

1

≤≤≤≤≤≤≤≤

xxxx

. (2)

where 4321 ,,, xxxx represent percentages of recycled polycarbonate--transparent color, recycled polycarbonate--transparent color, recycled polycarbonate--white color, recycled polycarbonate--muddy color, and virgin polycarbonate, respectively.

TABLE I. EXPERIMENTAL MATRIX FOR THE RECYCLED PLASTIC MANUFACTURING EXPERIMENT

mixture 1x 2x 3x 4x Tensile strength Heat distortion

temp1 2 3 1 2 3

1 22.5 32.5 32.5 12.5 43.32 45.51 44.64 129 131 130 2 20 20 40 20 43.58 45.01 43.38 133 134 131 3 30 25 25 20 46.85 43.12 42.66 129 126 127 4 25 35 40 0 43.35 43.37 43.59 126 128 128 5 30 40 30 0 43.65 42.84 43.88 126 125 127 6 30 20 40 10 43.89 44.48 44.59 128 130 129 7 10 40 40 10 42.86 43.39 44.38 127 128 126 8 20 40 20 20 47.85 45.95 48.94 131 129 128 9 10 30 40 20 44.30 43.88 45.05 130 127 128

10 30 40 20 10 45.63 43.34 44.21 130 128 131 11 10 40 30 20 42.81 43.91 44.18 130 127 127 12 20 30 40 10 45.33 44.23 43.49 130 128 127 13 30 30 30 10 41.95 42.83 43.36 131 128 128

TABLE II. RESULTS OF FITTING LINEAR MODEL TO THE DATA OBTAINED IN THE TENSILE STRENGTH TESTING EXPERIMENT

Source of variation

Sum of squares

Degree of freedom

Mean squares

F-value

p-value

Model 37.06 9 4.12 3.11 0.009 Linear

mixture 11.68 3 3.89 2.94 0.050

AB 0.29 1 0.29 0.22 0.641AC 5.18 1 5.18 3.91 0.058AD 5.34 1 5.34 4.03 0.054BC 2.74 1 2.74 2.07 0.161BD 14.57 1 14.57 10.99 0.003CD 2.71 1 2.71 2.04 0.164Residual 38.44 29 1.33 Lack of fit 10.21 3 3.40 3.14 0.043 Pure error 28.23 26 1.09

Cor total 75.50 38

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TABLE III. RESULTS OF FITTING LINEAR MODEL TO THE DATA OBTAINED IN THE HEAT DISTORTION TEMPERATURE TESTING EXPERIMENT

Source of variation

Sum of squares

Degree of freedom

Mean squares

F-value

p-value

Model 105.64 9 11.74 4.65 0.001 Linear mixture

42.20 3 14.07 5.57 0.004

AB 11.47 1 11.47 4.54 0.042AC 9.42 1 9.42 3.73 0.063AD 19.83 1 19.83 7.85 0.009BC 10.97 1 10.97 4.34 0.046BD 0.18 1 0.18 0.07 0.792CD 3.19 1 3.19 1.26 0.270Residual 73.28 29 2.53 Lack of fit 14.61 3 4.87 2.16 0.117 Pure error 58.67 26 2.26 Cor total 178.92 38

Tables II and III present the results of fitting linear model to the data obtained in the tensile strength and heat distortion temperature testing experiments, respectively. The sequential F-tests in the tables indicate that the contributions of the linear mixture model are significant. The diagnostic information is generally satisfactory. The fitted models are highly significant based on the low p-value. Equations (3) and (4) illustrate the two fitted models for tensile strength and heat distortion temperature based on Scheffé model [19]. According to regression model in (3), component of recycled polycarbonate--white color )( 2x exhibits the highest coefficient for the main effects for tensile test. This means that the best pure plastic product is recycled polycarbonate--white color. Component of recycled polycarbonate--transparent color )( 1x is least preferred. If one looks at the coefficients for the second-order terms, the recycled polycarbonate--transparent color and the recycled polycarbonate--muddy color combination )( 31xx , the recycled polycarbonate--transparent color and the virgin polycarbonate combination )( 41xx , and the recycled polycarbonate--muddy color and the virgin polycarbonate combination )( 43xx are positive coefficients. These mean that the mixture components of the two kinds of recycled plastics are preferred. Similarly, component of recycled polycarbonate--white color )( 2x exhibits the highest coefficient for the main effects for heat distortion temperature test according to regression model in (4). Once again, this means that the best pure plastic product is recycled polycarbonate--white color. Component of recycled polycarbonate--transparent color )( 1x is least preferred. However, the coefficient signs for the second-order terms of the heat distortion temperature model are not the same as those of the tensile strength model.

43

42324131

2143211

43.929.1993.667.1231.13

46.233.4279.4189.5002.41

xxxxxxxxxx

xxxxxxy

+−−++

−+++= (3)

43

42324131

2143212

23.1014.287.1343.2495.17

38.1549.12123.13013217.121

xxxxxxxxxx

xxxxxxy

+−−++

++++= (4)

Consequently, the results from analysis of variance for tensile strength and heat distortion temperature indicate that the problem of conflicting responses arises. To overcome the problem of conflicting responses, simultaneous optimization of multi-response variables is used. Since there are a number of operating conditions for the mixture components that can be used to maintain all responses within acceptable values, the optimal operating conditions of the mixture components should be determined using desirability function. Furthermore, RSM and POE are used to determine the optimal amounts of mixture of four kinds of components for product quality improvement.

A: x13.67654

B: x213.6765

C: x313.6765

40 40

30

POE(Tensile strength)

1.46972

1.77797

2.086212.39445

2.7027

Prediction 1.38

Figure 5. A contour plot of tensile strength after performing RSM and

POE.

A: x13.67654

B: x213.6765

C: x313.6765

40 40

30

POE(HDT)

1.92346

2.25053

2.25053

2.5776

2.5776

2.90468

2.90468

3.23175 3.23175

3.23175

Prediction 2

Figure 6. A contour plot of heat distortion temperature after performing

RSM and POE.

315

According to Fig. 5 and Fig. 6 after performing RSM and POE, the surface reaches a minimum where the least amount of error is transmitted or propagated to the tensile strength and heat distortion temperature responses. These minima occur at the flat areas on the model graphs where plastic mixtures of recycled and virgin polycarbonate will be most robust to varying amounts of mixture components. The results from Design Expert® software [20] (not shown), have proven that the optimal operating settings of plastic mixtures consist of 19.15% component of recycled polycarbonate--transparent color, 36.99% component of recycled polycarbonate--white color, 27.54% component of recycled polycarbonate--muddy color and 16.33% component of virgin polycarbonate producing minimum propagation of error. At the optimal operating settings, tensile strength and heat distortion temperature are 44.58 MPa and 129.94 oC, respectively.

Practically, it is recommended to confirm the optimal operating settings by conducting a confirmation experiment at the optimal operating settings. The confirmation experiment is used to verify whether the predicted response based on the regression model lies within the confidence interval or not. Ten samples are conducted at the settings. From the confirmation runs and the confidence intervals of the two responses, both predicted responses of tensile strength and heat distortion temperature lie within the 95% confidence intervals. These illustrate that the predicted models for the tensile strength and heat distortion temperature are valid and sound.

IV. CONCLUSION Producing plastic has an environmental impact because it

uses many resources and fossil fuels. Recycling plastic can reduce consumption of energy, non-renewable fossil fuels use, as well as global emissions of carbon dioxide. The effect of recycled polycarbonates and virgin polycarbonates on tensile strength and heat distortion temperature is studied. The optimal amounts of mixture components to produce recycled plastic products are determined. As the results of doing systematic experimentation, using mixture experiment, RSM, and POE, the quality of the recycled plastic products can be improved and becomes more robust to variations at the optimal operating settings. The results have proven that the manufacturer can use these settings of recycled plastics and virgin polycarbonate to produce quality products with low cost and environmental impact reduction.

ACKNOWLEDGMENT The authors would like to thank Faculty of Engineering

at Khon Kaen University for supporting the research connected with this paper. In addition, the authors would like to thank Thailand Research Fund (TRF) for Master Research Grants (TRF-MAG) for providing financial support for this research. A special thanks is due to manager of Saptawee Electric Company Limited for supporting the authors data set collection for the recycled plastic manufacturing experiments.

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