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1 Research Paper OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS FOR PLASTIC INJECTION MOLDING FOR ENHANCE PRODUCTIVITY AND REDUCED TIME FOR DEVELOPMENT 1 Sanjay N.Lahoti, 2 Prof.M.D.Nadar, 3 Swapnil S. Kulkarni Address for Correspondence 1 ME-Mech/CAD-CAM appearing, 2 Professor, Mechanical Engineering Department, Pillai s Institute of Information & Technology, New Panvel 3 Director-Able Technologies India Pvt. Ltd., Pune INTRODUCTION Injection Molding is an extensive global manufacturing process for making simple to intricate plastic, ceramic and metal parts. Injection Molding converts wax, thermoplastics, thermo sets as well as powdered metals and magnesium into thousands of products. Injection Molding is the most commonly used manufacturing process for the fabrication of plastic parts. A wide variety of products are manufactured using injection molding, which vary greatly in their size, complexity, and application. The injection molding process requires the use of an injection Molding machine, raw plastic material, and a Mold. Injection Molding is used to produce thin-walled plastic parts for a wide variety of plastic housings. It often requires many ribs and bosses on the interior. These housings are used in a variety of products including household appliances, consumer electronics, power tools, types of open containers, buckets, automotive dashboards. Injection Molding is a manufacturing process which is producing parts from both thermoplastic and thermosetting plastic materials. Material is fed into a heated barrel, mixed, and forced into a Mold cavity where it cools and hardens to the configuration of the Mold cavity. After a product is designed, usually by an industrial designer or an engineer, Molds are made by a Mold maker (or toolmaker) from metal, usually either steel or aluminum, and precision-machined to form the features of the desired part. Injection Molding is widely used for manufacturing a variety of parts, from the smallest component to entire body panels of cars. The machine must be accurate in giving correct injection pressure, molded temperature control system, proper alignment between the two plates etc. A good injection Molding machine will definitely give consistent good quality products. The different types of plastics materials used for producing various products must be of good graded quality. If standard plastic raw materials are used good quality plastic components are also assured. OBJECTIVES OF THE PROJECT The main objectives of the process are to reduce cycle time by process parameters optimization to ensure high quality parts. The aim of this project work is to identify the factors affecting cycle time and to reduce cycle time to optimize process. Hence the objectives of the present experimental work are: To review the literature on injection molding process parameters. To design the experiment for assessment of injection molding process parameters. To Select appropriate injection molding machine and suitable material To select the major process parameters that will affect the cycle time and quality of the product. To determine the effect of injection molding process parameters such as mold temp, melt temp, injection pressure, holding pressure, cool time, on the quality of molded parts. To optimize selected injection molding process parameters. Experimental design consists of the following stages: Define the problem. Determine the objective of the experiment. Generate a prior model. Create the experimental design and analyze the data. Taguchi methods provide a systematic approach to a better understanding of the process and assist industrial engineers to discover the key process variables which affect the critical process or product characteristics. Taguchi s philosophy is more relevant in terms of working towards a target performance, which essentially reflects the continuous improvement attitude. The objective of the Taguchi methods is to obtain more robust processes/products under varying environmental variables. Unlike the full factorial design method that investigates every possible combination of processes parameters, the Taguchi method studies the entire parameter space with a minimum number of experiments. Accordingly, the studied process should be characterized by a number of parameters which are signal factors, control factors and noise factors Problem definition To define the problem, we must first identify all the inputs and outputs of the system. We also select the range, or settings of the input factors. The inputs can be either qualitative or quantitative. A qualitative variable has discrete settings, such as the type of cutting tool. A quantitative variable has a numerical value and is usually continuous, such as the surface velocity. We must also take a first guess at the factors that we expect to interact. Interactions occur when the response caused by one factor is affected by the settings of another factor. Intelligent assumptions should be made based on understanding of the system. These assumptions will affect the experimental design. However, during the data analysis, we will test these assumptions and possibly add additional experiments based on our results. If we choose to include the effects of many interactions, this will greatly increase the number of required experiments. Also, the number of interactions we choose to study will also be influenced by the objective of the experiment. "The production of injection molded parts is a complex process where, without the right combination of material, part and mold design and processing parameters, a multitude of manufacturing defects can occur, thus incurring high costs. The injection molding process itself is a complex mix of time, temperature and pressure variables with a multitude of

2 manufacturing defects that can occur without the right combination of processing parameters and design components. Determining optimal initial process parameter settings critically influences productivity, quality, and costs of production in the plastic injection molding (PIM) industry. The problem for the proposed work is to discern the optimal values for the injection molding parameters such that any new component to be introduced for production could be taken up with ease through the trial and testing and later through the pilot production phase once we could establish the category that it belongs to - i.e. Material type and the Size. The reference values so evolved during the dissertation work would prove handy while deliberating over the actual process/ production plan for any newly introduced component" Experimental objectives The experimental objective can range from simply identifying which factors affect a process to optimizing performance or formulating a predictive model. In order to develop a sound predictive model, often many experiments are required. Usually we begin the experimental design process by selecting the significant factors during a screening experiment. Then more in depth experiments are performed with only the significant factors to develop a predictive model or optimize the performance. Screening The objective of screening experiments is to reduce a large number of potentially important factors to those that are most significant. Often it is not economically practical to perform every possible combination of factor settings. In these situations screening is a very useful tool. In order to reduce the number of experiments, usually we study only two states (a high and a low) for each factor. With only two states, it is only possible to observe linear effects of the factor on the response. Since our objective during screening is to determine significant factors rather than to develop predictive capabilities, this is usually sufficient. Later, during response surface methodology, additional settings for each factor are added. To further reduce the number of experiments, often only first order effects are studied. However, it is not possible to assess the effects of the interactions. Furthermore, the interaction effects will be coupled to other first order effects and may cause erroneous results. SOLUTION PROCEDURE (METHODOLOGY) Analytical Method Analytical methods refer to techniques and procedures for analyzing data collected while conducting an evaluation. Two main types of analytical methods include qualitative and quantitative procedures. Quantitative methods include statistical techniques for analyzing data, and qualitative methods analyze information, such as notes from interviews and observations, that cannot easily be summarized in numerical terms. Popular quantitative methods used in evaluation include, but are not limited to, analysis of variance, factor analysis and linear regression. Quantitative methods make data easier to analyze and summarize, and qualitative approaches are subject to varying interpretations. Many evaluations, especially in education and the social sciences, combine qualitative and quantitative techniques. Computational approach This presents a computational approach for the assessment of the given problem. One of the main features of the work is the search for simplicity and robustness in all steps of the modeling, in order to match the proposed method with industrial practices and constraints. The proposed method utilizes software in the domain of FEA (Finite Element Analysis) for analyzing the effects of the variation in the values of the design parameters influencing the response parameter. In our case, MINITAB 14 software is proposed to be deployed. Experimental set up With the use of experimental set-up we can analyze the data in a real time environment or verify the actual results obtained by other methods. This method is simpler to visualize and understand but is more challenging in terms of manipulation of the input data for finding the sensitivity associated with the output. Also, it is time consuming and expensive to build a prototype and later engage the testing equipments for this purpose. Steps Involved in Taguchi method The use of the parameter design of the Taguchi method to optimize a process with multiple performance characteristics includes the following steps: 1. Define the problem. 2. Selection of factors and number of levels. 3. Selection of appropriate Orthogonal Array (OA). 4. Performing the experiments 5. Statistical analysis and interpretation of experimental results. 6. Determination of optimal condition. 7. Confirmation run or experiment. EXPERIMENTAL WORK Introduction Based on the literature of injection molding process discussed in the second chapter and the objectives of the investigation the experiments are planned. This experimental work throws light on the influence of molding process parameters on cycle time, which is not adequately studied till date. It helps to understand relationship between the various parameters and cycle time as response variables. The experiments are planned on two different material ABS and PP with one verity each. Experimental procedure Selection of orthogonal array The selection of appropriate orthogonal array (OA) depends on the total degree of freedom of the parameters. Degrees of freedom are defined as the number of comparisons between process parameters that need to be made to determine which level is better and specify how much it is. The first step in constructing an orthogonal array to fit a specific study is to count the total degree of freedom that tells the minimum number of experiments that must be performed to study all the chosen control factors. To begin with, one degree of freedom is associated with the overall mean regardless of the number of control factors to be studied. The degrees of freedom associated with interaction between two factors, called A and B, are given by the product of the degree of freedom for each of the two factors. Degrees of freedom for interaction A x B = (degrees of freedom for A) x (degrees of freedom for B)

3 In our experiment, three 3-level factors (A, B, C). The degrees of freedom for this experiment are then computed as follows: Factor/ interaction Degrees of freedom Hence minimum numbers of experiments needed are nineteen. Considering the further need of data collection with respect to interactions and as once set the resources needed are not too costly, L27 was opted for experimentation. DATA ANALYSIS Analysis of variance (ANOVA) The main aim of ANOVA is to investigate the design parameters and to indicate which parameters are significantly affecting the output parameters. In the analysis, the sum of squares and variance are calculated. F-test value at 95% confidence level is used to decide the significant factors affecting the process and percentage contribution is calculated. Larger F value indicates that the variation of the process parameter makes a big change on the performance. The analysis of variance (ANOVA) is applied in order to test the equality of several means, resulting in what process parameters (factors) are statistically significant. The results of ANOVA are presented in a table that displays for each factor (or interaction) the values of: SS: sum of squared deviations from the mean. For n values of y i and the mean value = ( ) d.f : degree of freedom which is the number of levels for each factor minus 1. MS: mean of squares. MS= SS d.f. F: F is the ratio between the mean of squares effect and the mean of squares error. = M.S.effect M.S.error F- Test is used to see the significance of each factor (or interaction) on the response variable or signal-to-noise ratio. The analysis of variance (ANOVA) of the effects of factors on the response variable by using MINITAB software is given as below. The ANOVA analysis with general linear model for calculating F- test value and P Value for cycle time of ABS material is shown in Table 1. The response table for mean cycle time and mean S/N ratio for each level is summarized and shown in Table 2 and Table 3. Table 1 ANOVA results for ABS parts indicates that the variation of the process parameter makes a big change on the performance. According to this analysis, the most effective parameter with respect to cycle time is melt temp. and then cooling time. The P values test the statistical significance of each of the factors. It is observed from the above ANOVA table, there are three P-values are less than 0.05, but out of these two factors i.e. melt temp. And cool time has a statistically significant effect on cycle time at the 95.0% confidence level. According to table 1, melt temp. is found to be the major factor affecting the cycle time, whereas cool time was found to be second ranking factor affecting cycle time. Table 2: Response Table for Means for ABS material Table 3: The response table of S/N ratios for ABS (Smaller is better) Analysis of response table In Table 2, mean for each level is summarized and called the mean response table for cycle time. In Table 3, mean S/N ratio for each level is summarized and called the response table for S/N ratio. According to Table 2 and 3, as the difference between level 1 and level 3 are much higher (3.67 for mean cycle time and 0.87 for S/N ratio) for melt temp. Hence the slight increase of melt temps. will increase the cycle time significantly. Hence melt temp. is most influencing parameter in injection molding process. The difference between level 1 and level 3 for cool time is 2.78 for mean cycle time and 0.62 for S/N ratio. Hence cool time is second influencing parameter in injection molding process. The third injection molding parameter hold pressure is not much influencing according to the response table, hence ranked third. The ANOVA analysis with general linear model for calculating F- test value and P-value of process parameters for cycle time of PP is shown in Table 4. The response table for mean cycle time and mean S/N ratio for each level is summarized and shown in Table 5 and Table 6. Table 4 ANOVA results for PP materials All F-ratios are based on the residual mean square error. Analysis of ANOVA result The result of ANOVA table for cycle time is presented in Table 1. Statistically, larger F value All F-ratios are based on the residual mean square error Analysis of ANOVA result The result of ANOVA table for cycle time for PP is presented in Table 4. Statistically, larger F value indicates that the variation of the process parameter makes a big change on the performance. According to this analysis, the most effective parameter with respect to cycle time is injection pressure and mold temperature. The P-values test the statistical

4 significance of each of the factors. It is observed from the above ANOVA table, there is one P-values which is less than 0.05, this factors is injection pressure have a statistically significant effect on cycle time at the 95.0% confidence level. According to Table 4, injection pressure was found to be the major factor affecting the cycle time. Table 5. Response Table for Means for PP materials Graphs of main effects for PP Table 6. The response table for means of S/N ratios for PP materials Figure 3-Main effects plots of mean for PP Analysis of response table In Table 5, mean for each level is summarised and called the mean response table for cycle time. In Table 6, mean S/N ratio for each level is summarised and called the response table for S/N ratio. According to Table 5 and 6, as the difference between level 1 and level 3 are much higher (3.33 for mean cycle time and 1.15 for S/N ratio) for injection pressure. Hence the slight increase of injection pressure will increase the cycle time significantly. Hence injection pressure is most influencing parameter in injection molding process. The difference between level 1 and level 3 for mold temperature is 1.78 for mean burr height and 0.64 for S/N ratio. Hence mold temperature is second influencing parameter in molding process. The third molding process parameter hold pressure is not much influencing according to the response table, hence ranked third. Graphs of main effects for PP Figure 1-Main effects plots of mean for PP Figure 4-Main effects plots of S/N ratios for PP Analysis of graphs Figure 1 shows main effect plot (data means) for mean cycle time for PP. The grand mean reference line is drawn at sec. For mold temperature at level 1 (80), the mean value is sec, at level 2 (85), the mean value is sec and at level 3 (90), the mean value is For Injection pressure, the mean cycle time is increases from to from level 1(65) to level 3 (75). For hold pressure at level 1(30) it is observed mean value is it is increases to sec at level 3 (40) through mean value While for cool time at level 1 (9) mean value is at level2 it is and at level 3 (15) mean value is Figure 3 shows main effect plot (data means) for SN ratios for PP. The grand mean of SN ratios reference line is drawn at For mold temperature at level 1 (80), the mean S/N ratio value is sec, at level 2 (85) the mean S/N ratio value is sec and at level 3 (90), the value is For Injection pressure, the mean S/N ratio value is increases from to from level 1(65) to level 3 (75). For hold pressure at level 1(30) it is observed S/N ratio value is it is increases to sec at level 3 (40) through S/N ratio value While for cool time at level 1 (9) S/N value is at level 2 it is and at level3 (15) mean S/N ratio value is The above graphs show that, increase in melt temp from level 1 will increases the cycle time. And as injection pressure increases, the mean cycle time also increases significantly. These results are analyzed by ANOVA for the purpose of indentifying the significant factors which influence the cycle time. Table 7: ABS material best working parameters Table 8: PP material best working parameters Figure 2- Main effects plots of S/N ratios for PP

5 Confirmation test The confirmation experiment is the final step in the design of the experiment process. The purpose of the confirmation experiment is to validate the conclusions drawn during the analysis phase. The confirmation experiment is performed by conducting a test with a specific combination of the factors and levels previously evaluated. In this study, after determining the optimum conditions and predicting the response under these conditions, a new experiment was designed and conducted with the optimum levels of the injection molding process parameters. The experiment was conducted with optimum level as melt temp 220 C, Injection pressure 75 Mpa, Hold pressure 60 Mpa and cool time 15 sec for ABS and mold temp 80 C, Injection pressure 65 Mpa, Hold pressure30 Mpa and cool time 12 sec for PP. Table 9 and Table 10 shows the comparison between the estimated value of cycle time and the confirmation experiment value. A small difference (2 sec for ABS and 2 sec for PP) can be observed between these values. This indicates that the experimental value is close to the estimated value. Therefore, this verifies that the experimental result is highly correlated with the previous results Table 6.9: Result of confirmation experiment for ABS materials Table 6.10: Result of confirmation experiment for PP materials RESULTS AND DISCUSSIONS Based on the responses received from the experimentation and their analysis we arrived on following results. The conformation experiments have proved that the model is valid. As the interactions are not significant hence, model is linear and suitable to explain Behavior with Taguchi method. According to ANOVA analysis as shown in Table 1 and 3, the most effective parameters with respect to cycle time are melt temp. and injection pressure.a small variation will have a great influence on the performance. According to this, melt temp. was found to be the major factor affecting the cycle time, whereas cool time was found to be the second factor. The percent contribution of other factors are much lower. Figure 2 and 3 shows the S/N ratio graph where the horizontal line is the value of the total mean of the S/N ratio. Basically, larger the S/N ratio, the better is the quality characteristics for the molded parts. As per the S/N ratio analysis from graph the levels of parameters to be set for getting optimum value of cycle time for ABS and PP are as follows. Expected S/N ratio at these optimum factors combination are CONCLUSION AND FUTURE SCOPE OF THE WORK: Study the injection molding process parameters for two different thermoplastic materials with at least one variety in each. Develop a methodology to produce defects free parts by controlling the initial process parameters settings (like melt temperature, injection pressure, injection velocity, injection time, packing pressure, packing time, cooling temperature, cooling time, etc). Identify the critical parameters that need the longest time for iteration for study. The objective is to help to provide a common ground for recommendation of processing parameters settings according to geometrical and the material characteristics of the injection molded component. Optimize the setting time as a result. The approach of design of experiment was successfully applied for the study which explained the linear model of injection molding process parameter for selected range of parameters of melt temperature, mold temperature, injection pressure, hold pressure and cool time for ABS and PP. The experimental work resulted into finding of the influence of molding process parameters. According to ANOVA analysis, the most effective parameters with respect to cycle time are melt temperature and injection pressure. The confirmation experiment was conducted and finds that there is no much difference in optimum condition. The estimated results are closed correlated with the previous results. REFERENCES 1. Optimization of Weld Line Quality in Injection Molding Using an Experimental Design Approach Tao c. Chang and Ernest Faison, Journal of Injection Molding Technology, JUNE 1999, Vol. 3, No. 2 PP Setting the Processing Parameters in Injection Molding Through Multiple-Criteria Optimization: A Case Study Velia Garc ıa Loera, José M. Castro, Jesus Mireles Diaz, O scar L. Chaco n Mondragon,, IEEE 2008 PP Processing Parameter Optimization For Injection Molding Products In Agricultural Equipment Based On Orthogonal Experiment And Analysis Yanwei1 Huyong IEEE 2011 PP Warpage Factors Effectiveness of a Thin Shallow Injection-Molded Part using Taguchi Method N.A.Shuaib, M.F. 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