Measurements of cycle time in injection molding of filled thermoplastics

At present plastic becomes inseparable from human life especially in the food and beverage industry. One of the methods used in the manufacturing process of plastic products is injection molding. Injection molding is one of manufacturing technique that consists of a series of cyclical processes and is used to produce thermoplastic materials. The effect of the combination of process parameters impact on the product results such as the quantity and quality of the product, the non-conformity of the parameters causes the production to be not optimal. One method that can be used for optimization is the taguchi method. The taguchi method is a set of special matrices called orthoghonal arrays that are used as reference in the determination combination of parameters and level values. The purpose of this research is to determine the optimal cycle time and net of the product on the process of making 180 ml bottle cap but by minimizing flash defects. The method used in this phase is ANOVA, and the calculation of taguchi method by using minitab 16 software. From the result of the research, the result of optimal condition is combination injection pressure 1320 bar, injection speed 50 mm/s, holding pressure 300 bar, and nozzle temperature 255oC produces a cycle time value of 15.72 seconds and netto 3.56 grams. This result is better than the setting of the company that produces 16.66 seconds cycle time and entered in the net range of 4 ± 0.5 grams resulting in an increase in production of 5.97%. While with combination of injection pressure 1280 bar, injection speed 50 mm / s, holding pressure 300 bar, and nozzle temperature 245oC resulted in fewer number of flash defects compared to company setting that is 12 units from 80 units of sample. Keywords: flash deffect, injection molding, taguchi method, cycle time

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OPTIMASI PRODUKSI TUTUP BOTOL 500 ml PADA PROSES INJECTION MOULDING MENGGUNAKAN METODE RESPONSE SURFACE

ROTOR ◽

10.19184/rotor.v10i1.5146 ◽

2017 ◽

Vol 10 (1) ◽

pp. 36

Author(s):

Kurniawan Purnama Putra ◽

R. Koekoeh R. W.

Keyword(s):

Injection Molding ◽

Response Surface ◽

Cycle Time ◽

Injection Moulding ◽

Plastic Material ◽

Cooling Time ◽

Mathematical Methods ◽

Nozzle Temperature ◽

Pressure Nozzle ◽

Plastic Pellets

Injection molding is one of the most common operations and versatile for mass production of complex plastic components. Injection molding is a process of plastic forming into a desired shape by means of pressing molten plastic into a space (cavity). Injection molding has a multi-step process, starting from inserting plastic pellets into the hopper, then plastic pellets into barrels / heating which makes the plastic material is melted. Melting plastic material is driven by the rotation of the screw, so that the flow to the nozzle, and then toward the sprue, runner, gate and into the cavity. Then the material present in the cavity will be retained within the mold under a certain pressure (holding pressure) to keep no shringkage when the product cooling process (cooling).The purpose of this study was to obtain the optimization of the cycle time and the percentage of production reject the bottle cap 500 ml in PT Berlina Tbk. The method used for this research is the method of response surface analysis, method surface respone is statistical and mathematical methods used to examine the relationship between one or more variables with the qualitative form of the response variables that aims to optimize and develop the response in an experiment. The data is optimized using Minitab software 16 produces an output response parameter setting optimization to inject pressure, nozzle temperature and cooling time. From the data analysis using the software Minitab 16 is obtained from the optimum situation results generated under conditions injection amounting to 1420 bar pressure; nozzle temperature at 264.7912 ° C; and the cooling time of 14.08 seconds. In this state of production can be increased by 10.836%. From the state of the optimum cycle time generated is 27.9161 seconds, net produced according to the standard that is 33.5820 grams and reject percentage the resulting is 11.11%. With a cycle time 27.9161 seconds with a net according to the standard and the percentage of rejects decreased 3.89%, the resulting product amounts to ± 15,517 /shift or up approximately 10.836%. The amount is based on the number of products produced in the cycle time previous of 30 seconds to produce ± 14,000 /shift. Keywords: Injection moulding, box-benhken design, Response surface methodology

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Review on the Reduction of Defects and Optimization of Cycle Time for Polymer Injection Molding

SSRN Electronic Journal ◽

10.2139/ssrn.3790356 ◽

2020 ◽

Author(s):

Arun Raj K. ◽

Roshin Das M. P.

Keyword(s):

Injection Molding ◽

Cycle Time ◽

Polymer Injection ◽

Polymer Injection Molding

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Partial Solidification Ejection Criteria for Injection Molding Production to Reduce Cycle Time

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2021.102263 ◽

2022 ◽

Vol 74 ◽

pp. 102263

Author(s):

Junyu Fu ◽

Jikai Liu ◽

Long Quan ◽

Yongsheng Ma

Keyword(s):

Injection Molding ◽

Cycle Time

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Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming

Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization ◽

10.4018/978-1-4666-1870-1.ch015 ◽

2013 ◽

pp. 218-229

Author(s):

Abbas Al-Refaie ◽

Ming-Hsien Li

Keyword(s):

Injection Molding ◽

Goal Programming ◽

Cycle Time ◽

Programming Model ◽

Additive Model ◽

Injection Molding Process ◽

Molding Process ◽

Goal Programming Model ◽

Process Factors ◽

Short Product

Injection molding process is increasingly more significant in today’s plastic production industries because it provides high-quality product, short product cycles, and light weight. This research optimizes the performance of this process with three main quality responses: defect count, cycle time, and spoon weight, using the weighted additive goal programming model. The three quality responses and process factors are described by appropriate membership functions. The Taguchi’s orthogonal array is then utilized to provide experimental layout. A linear optimization based on the weighted additive model in goal programming model is built to minimize the deviations of the product/process targets from their corresponding imprecise fuzzy values specified by the process engineer’s preferences. The results show that the average defect count is reduced from an average of 0.75 to 0.16. Moreover, the average cycle time becomes 13.06 seconds, which is significantly smaller than that obtained at initial factor settings (= 15.10 seconds). Finally, the average spoon weight is exactly on its target value of 2.0 gm.

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