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

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

Design and Analysis of Parison Blow Molding Using Adaptive Mesh

Blow Molding is one of the most versatile and economical process available for molding hollow materials. When polyethylene is stretched, it exhibits strain-hardening properties, which are temperature, pressure, velocity and strain-rate dependent. In this paper, preform is made by extrusion and forced between two halves by pressurization. This process includes isothermal and transient flow of Newtonian fluid in complex geometries simultaneous with structuring and solidification. A time dependent problem is defined and setting material properties and boundaries condition for bottle blow molding. Numerical data available in POLYDATA for a time dependent problem using ANSYS POLYFLOW were applied. Results display in form contours associated with different variables at different time steps and good agreement with the bottle thickness profile is observed. In this paper, the analysis of the stretch-blow molding (SBM) process of polyethylene terephthalate (PET), parison plastic bottles is studied by the finite element method (FEM). A hyper elastic constitutive behavior was calibrated using material data available in literature in variant high temperatures and strain rates and was used in the numerical simulation. Hydrostatic pressure with convention heat transfer has been used instead of a blowing process. Comparisons of numerical results with experimental observations demonstrate that the model can predict an overall trend of thickness distribution. Through the study, it becomes clear that the proposed model is applicable for simulating the stretch-blow molding process of PET bottles, and is capable of offering helpful knowledge in the production of bottles and the design of an optimum preform.

A Model Developed for Predicting Uniformity of Kyphoplasty Balloon Wall Thickness Based on the Orthogonal Test

In order to optimize the wall thickness distribution of medical balloon, kyphoplasty balloon was chosen as the research object, the uniformity of wall thickness distribution was taken as the evaluation index, and the influence of stretch blow molding process on the uniformity of kyphoplasty balloon was investigated. In this paper, 16 sets of orthogonal test schemes were studied by selecting four main parameters such as forming temperature, forming pressure, stretching distance, and holding time of stretch blow molding process based on the L16(44) Taguchi method orthogonal table. The statistical analysis showed that the forming temperature was an utmost parameter on the uniformity, while an optimal scheme was obtained and an optimal balloon with the uniformity of 95.86% was formed under the scheme. To further quantify the relationship between the uniformity and the parameters, artificial neural network (ANN) and nonlinear regression (NLR) models were developed to predict the uniformity of the balloon based on orthogonal test results. A feed-forward neural network based on backpropagation (BP) was made up of 4 input neurons, 11 hidden neurons, and one output neuron, an objective function of the NLR model was developed using second-order polynomial, and the BFGS method was used to solve the function. Adequacy of models was tested using hypothesis tests, and their performances were evaluated using the R2 value. Results show that both predictive models can be used for predicting the uniformity of the balloon with a higher reliability. However, the NLR model showed a slightly better performance than the ANN model.