PROCESS PARAMETERS OPTIMIZATION FOR DEVELOPMENT OF DEFECT FREE INJECTION MOLDED COMPONENT

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1 PROCESS PARAMETERS OPTIMIZATION FOR DEVELOPMENT OF DEFECT FREE INJECTION MOLDED COMPONENT Mr.G.V.Pujari 1, Prof. Dr.V.R.Naik 2 1 PG Student, Dept. of Mechanical Engineering, D.K.T.E. s Ichalkaranji, Maharashtra,(India) 2 Professor, Mechanical Engineering, Dept, D.K.T.E. s Ichalkaranji, Maharashtra, (India) ABSTRACT Product quality for plastic injection molding process is highly related with the settings for its process parameters. During producing a product using injection molding process, various defects such as warpage, weld lines, shrinkage and sink mark can be occurred, optimal setting up of injection molding process is very important to reduce the defect and controlling the quality of the injection molded product. The purpose of this paper is to minimizing defect in nylon thermoplastic material by moldflow simulation software & Taguchi method. Taguchi s orthogonal array Method has been implemented to analyze and optimize the processing parameters such as melt temperature, injection pressure, holding pressure & cooling time. Keywords: Injection Molding; Moldflow; Taguchi s Orthogonal Array. I.INTRODUCTION Injection molding is the most commonly used manufacturing process for the producing 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 takes place by use of injection molding machine, raw plastic material, and a mold. There are so many parameters in the injection molding process that must be controlled to ensure the proper manufacture of a product.[1] All fall into four major categories. These are temperature, pressure, time and distance. Material property values can be altered significantly by adjusting molding parameters.[2] Most polymers may be injection molded, including thermoplastics fiber reinforces thermoplastics, thermosetting plastics and elastomers. The another advantage of injection molding are high production rates, high tolerances are repeatable, wide range of materials can be used, low labor costs, minimal scrap losses and little need to finish parts after molding [3] II. SCOPE The scope consist of: i). Defects will be observed for the optimized parameters. ii). Parameters to be studied include melt temperature, injection pressure, holding pressure, cooling time are considered 56 P a g e

2 iii.moldflow plastics insight software and Design of experiment will be employed to analysis and optimization. [4] III. TOOL AND MATERIAL Moldflow Plastic Insight software (MPI) is used in simulation for the parameters guidelines. The simulation processes start with the development of the model by using Computer Aided Design (CAD) solid work software. [5] Nylon thermoplastic material has used for this experiment. Figure.1:Nylon Material Model I V. TAGUCHI'S EXPERIMENTAL METHOD Taguchi method is an effective method targeted at quality improvement for product development and it has used in designing of our experiments. There are Four factors identified to control the injection process; melt temperature, injection pressure, holding pressure, cooling time, Each factor consists of four levels where an orthogonal array L16 has used to suit the inputs and all parameters have been identified. [6] Taguchi uses the S/N ratio to measure quality characteristic deviating from the desired value. The S/N ratio characteristics can be divided into three categories: the-nominal-the-best, the smaller-the-better, and the-larger-the better. Selection of which orthogonal array(oa) to use mostly depends on these items in order of numbers of factors and interactions of interest, number of levels for the factors of interest, and the desired experimental resolutions or cost limitations. The results that have considered in Taguchi method is observed on the basis of defect such as Short shot/unfilled region ; Burn mark, Warpage, Sink mark, Weld line, Flash.[7] V. INJECTION MOLDING EXPERIMENT Parts acceptance criteria on the basis of defects percentage-:according to the experience and simulation analysis, the melt temperature, injection pressure, holding pressure, and cooling time are considered as the main factors of effecting defects such as. Short shot/unfilled region ; Burn mark, Warpage, Sink mark, Weld line, Flash. In Table:1 we have assigned the defect percentage for each defect. On the basis of this defect percentage we have assigned the rank to each injection molding trial in Table.2. Defect level of Rank 1.= 0 % 20 % is part acceptable level & further other levels are rejected. 57 P a g e

3 Table.1: Defect Percentage Table.2: Defect Rank Table.3:Nylon part Mold flow simulation result 58 P a g e

4 Range of process parameters for injection molding machine setting for development of nylon part have selected by the help of mold flow software suggestion (From Table:3) & shop floor engineer experience Table.4. Range of Process Parameters Table.5: 4 Levels Value of 4 Factors Taguchi L16 screening experiments were conducted on injection molding machine to identify the most significant input variables by ranking with respect to their relative impact on the defects. 59 P a g e

5 Table.6: Taguchi L16 array The S/N ratio η is given by: η = -10log (MSD) (1) Where MSD is the mean-square deviation for the output characteristic. MSD for the smaller-the-better quality characteristic is calculated by the following equation, (2) Where Yi is the defect rank value for the ith test, n denotes the number of tests and N is the total number of data points. The function -log is a monotonically decreasing one, it means that we should maximize the S/N value. The S/N values are calculated using equations (1) and (2). Table -7 shows the response table for S/N ratios using smaller-the-better approach. 60 P a g e

6 Table.7. Response Table for S/N Ratios Using Smaller-the-Better Create graph of the S/N ratio values of individual parameters Melt temperature(mt), Injection pressure(ip), Holding pressure(hp), Cooling time(ct) by the help of Mini-Tab software Fig.2:Main Effect Plot SN Ratios VI. TEST OPTIMIZATION RESULTS Observed from Fig.2 Main effect plot SN ratios Table.8. Best Setting Combination In these Fig.2 highest level value indicates the best level setting for each factor. Higher significant factors can be observed by the higher difference values of S/N ratio in Table.7 and based on Table.7 melt temperature is the most significant factors that affects the product followed by holding pressure, injection pressure & cooling time. 61 P a g e

7 VII. CONCLUSION The optimization of plastics injection molding processing parameters Based on the minimization of defects have been performed by Taguchi method & Moldflow software. The conclusions of the study are as follows: 1) The sequence of effects degree of different molding process parameters for defects; ranked in the melt temperature; holding pressure; injection pressure and cooling time. The melt temperature is the most important effect. 2) The optimized parameters combinations of different factors are considered as melt temp 235 c; holding pressure 35 bar; injection pressure 65 bar & cooling time 12 sec. REFERENCES [1]. Kamaruddin, Zahid A. Khan,S. H. Foong. Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic BlendS. IACSIT International Journal of Engineering and Technology, Vol.2, No.6, December 2010 PP [2]. N. A.Shuaib, M.F. Ghazali, Z. Shayfull, M.Z.M. Zain, S.M. Nasir.Warpage Factors Effectiveness of a Thin Shallow Injection-Molded Part using Taguchi Method International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01 PP [3]. K. R. Jamaludina, N. Muhamadb, M. N. Ab. Rahmanb, S. Y. M. Aminb, Murtadhahadib, M. H. Ismail Injection molding parameter optimization using the taguchi method for highest green strength for bimodal powder mixture with ss316l in peg and pmma IEEE 2006 PP 1-8. [4]. Wen-Chin Chen, Gong-Loung Fu Pei-Hao Tai Wei-Jaw deng Yang-chih Ann and ga-based process parameter optimization for MIMO plastic injection molding IEEE 2007 PP [5]. Wen-ChinChen Tung-Tsan Lai, Gong-Loung Fu A Systematic Optimization Approach in the MISO Plastic Injection Molding Process IEEE 2008 PP [6] Tao c. Chang, Ernest Faison Optimization of Weld Line Quality in Injection Molding Using an Experimental Design Approach Journal of Injection Moulding Technology, JUNE 1999, Vol. 3, No. 2 PP [7] Velia Garc ıa Loera, Jos e M. Castro, Jes us Mireles Diaz, O scar L. Setting the Processing Parameters in Injection Molding Through Multiple-Criteria Optimization: A Case Study, IEEE 2008 PP P a g e