A Novel Method for Online Control in Equipment Parameters of Blow Molding Process

2022 ◽  
pp. 53-62
Author(s):  
Zhisong Zhu ◽  
Xu Zhao ◽  
Yu Liu
Keyword(s):  
Online Control ◽  
Molding Process ◽  
Blow Molding ◽  
Novel Method

scholarly journals STUDY OF DEFECT ON 180 ML HDPE BOTTLES YOGURT PRODUCTS WITH SMC B11 EXTRUSION BLOW MACHINE AT PT X

KREATOR ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tommy Prasetya Kana ◽  
Handika Dany Rahmayanti ◽  
HM Didik
Keyword(s):  
High Density Polyethylene ◽  
Plastic Material ◽  
Production Cycle ◽  
Molding Process ◽  
Plastic Packaging ◽  
Cycle Times ◽  
Blow Molding ◽  
Plastic Bottle ◽  
Extrusion Blow Molding ◽  
Stretch Blow Molding

The type of plastic packaging that is popular in the community is bottle packaging. The plastic material that is generally used to make plastic bottles is High Density Polyethylene (HDPE). The plastic bottle industry in Indonesia usually uses a blow molding process in its production process, where the blow molding process consists of injection blow molding, extrusion blow molding and stretch blow molding. The SMC B11 machine is one of the extrusion blow molding machines used to produce plastic bottle packaging. In producing workpieces, this machine still produces several products that are not in accordance with company standards, including in terms of production cycle times and product defects. Defects or defects that are often encountered include the appearance of spots, bent parison which causes the bottle to bend (the bottle body is thin one side) and blow pin which causes the thread to not fit.Keywords— Bottle, Plastic, Defect, Extrussion Blow Molding

Development of Conductive Packaging for Beverage Processing by Ohmic Heating

2020 ◽  
Vol 861 ◽  
pp. 213-217
Author(s):  
Sumuncharee Suyraksa ◽  
Pitiya Kamonpatana ◽  
Noppadon Kerddonfag ◽  
Amporn Sane ◽  
Vanee Chonhenchob
Keyword(s):  
Electrical Conductivity ◽  
Orange Juice ◽  
Sodium Sulfate ◽  
Ohmic Heating ◽  
Uniform Heating ◽  
Molding Process ◽  
Conductive Material ◽  
Blow Molding ◽  
Extrusion Blow Molding

This study was aimed to develop conductive packaging for ohmic heating. Polypropylene (PP) was mixed with conductive material (CM) in the ratios of 70:30 (CM30), 75:25 (CM25), and 80:20 (CM20) (w/w), then the conductive bottles were developed using extrusion blow molding process. The bottles were suspended in different sodium sulfate (Na2SO4) solutions (0.2, 0.3, and 0.5% w/w) as a transmitting current medium for ohmic heating and heated for 8 min. The CM30 and CM 25 had the highest electrical conductivity compared to the CM20, however the CM20 exhibited best processability, hence it was selected to be used for ohmic heating of orange juice. Different concentrations of Na2SO4 solutions had the effects on ohmic heating. The CM20 bottle suspended in 0.2% Na2SO4 solution resulted in the most uniform heating and suitable for ohmic processing of orange juice. The new conductive bottles developed could potentially be used for beverage processing by ohmic heating.

scholarly journals Finite Element Analysis of PMMA Stretch Blow Molding

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Afef Bougharriou ◽  
Mohieddine Jeridi ◽  
Mohamed Hdiji ◽  
Anoir Boughrira ◽  
Kacem Saï
Keyword(s):  
Finite Element ◽  
Thickness Distribution ◽  
Geometrical Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Blow Molding ◽  
Stretch Blow Molding ◽  
Crucial Parameter

The electric bubbles are a useful product made of PMMA material. They are produced by the stretch blow molding process. Thickness, which reflects the quality of the electric bubble, is a crucial parameter that deserves special attention for the molding process. In this work, finite element simulations of the stretch blow molding process are performed aiming at the determination of the preform geometry to ensure homogeneous thickness of the finished product. The geometrical parameters of the preform are optimized allowing a better homogeneity thickness compared to existing data. The predicted parameters allow the improvement of the thickness distribution. The standard deviation of the thickness is reduced to about 95% compared to the existing bubble.

Performance Optimization of Extrusion Blow Molded Parts Using Fuzzy Neural-Taguchi Method and Genetic Algorithm

2002 ◽  
Author(s):  
Jyh-Cheng Yu ◽  
Tsung-Ren Hung ◽  
Francis Thibault
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Fitness Function ◽  
Back Propagation ◽  
Stress Constraints ◽  
Molding Process ◽  
Finite Element Software ◽  
Blow Molding ◽  
Fuzzy Neural ◽  
Propagation Network

This paper presents a soft computing strategy to determine the optimal die gap parison programming of extrusion blow molding process. The design objective is to minimize part weight subject to stress constraints. The finite-element software, BlowSim, is used to simulate the parison extrusion and the blow molding processes. However, the simulations are time consuming, and minimizing the number of simulation becomes an important issue. The proposed strategy, Fuzzy Neural-Taguchi and Genetic Algorithm (FUNTGA), first establishes a back propagation network using Taguchi’s experimental array to predict the relationship between design variables and response. Genetic algorithm is then applied to search for the optimum design of parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The Reliability Distance is proposed and introduced to genetic algorithm using fuzzy rules to modify the fitness function and thus improve search efficiency. This study uses ANSYS to find the stress distribution of blown parts under loads. The comparison of results demonstrates the effectiveness of the proposed strategy.

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