scholarly journals Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application

Materials ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 1740 ◽  
Author(s):  
Ana Elduque ◽  
Daniel Elduque ◽  
Carmelo Pina ◽  
Isabel Clavería ◽  
Carlos Javierre
Keyword(s):  
Environmental Impact ◽  
Injection Molding ◽  
Empirical Model ◽  
Electricity Consumption ◽  
Injection Molding Process ◽  
Molding Process ◽  
Polymer Injection ◽  
Wide Range ◽  
Molded Parts ◽  
Polymer Injection Molding

Polymer injection-molding is one of the most used manufacturing processes for the production of plastic products. Its electricity consumption highly influences its cost as well as its environmental impact. Reducing these factors is one of the challenges that material science and production engineering face today. However, there is currently a lack of data regarding electricity consumption values for injection-molding, which leads to significant errors due to the inherent high variability of injection-molding and its configurations. In this paper, an empirical model is proposed to better estimate the electricity consumption and the environmental impact of the injection-molding process. This empirical model was created after measuring the electricity consumption of a wide range of parts. It provides a method to estimate both electricity consumption and environmental impact, taking into account characteristics of both the molded parts and the molding machine. A case study of an induction cooktop housing is presented, showing adequate accuracy of the empirical model and the importance of proper machine selection to reduce cost, electricity consumption, and environmental impact.

Computer Simulation of Warpage Formation in Polymer Injection Molding of a Step Pad

2006 ◽  
Author(s):  
Sridhar P. Ramamurthy ◽  
Lyle Steenson ◽  
Zhong Hu
Keyword(s):  
Injection Molding ◽  
Cooling System ◽  
Three Dimensional ◽  
Nonlinear Material ◽  
Injection Molding Process ◽  
Molding Process ◽  
Element Analysis ◽  
Polymer Injection ◽  
Fea Model ◽  
Polymer Injection Molding

Warpage is one of the most common defects of a plastic product in the polymer injection molding process. It is attributed to the differential shrinkage after the part is ejected from the die cavity due to the nonlinear material property of the polymer, improper design of the cooling system, geometry of the part and the related process parameters. In this paper, the warpage formation of a plastic part, Step Pad of polypropylene copolymer, in the cooling stage of the polymer injection molding process was simulated by finite element analysis (FEA). A three-dimensional FEA model, taking into account the nonlinear material (polypropylene copolymer) properties, with a thermal-structural sequential coupled approach of higher computing efficiency was developed. The effects of mold closed time and layout of cooling system on the dimension and shape of the part were investigated. Industrial experiments for the different mold closed times (25s, 30s, 35s, 40s, 45s, 50s, and 55s) were conducted. The simulation results were compared with the experimental results. The approach is effective in predicting warpage in the polymer injection molding processes.

Optimal Sensor Locations for Polymer Injection Molding Process

2014 ◽  
Vol 611-612 ◽  
pp. 1724-1733
Author(s):  
David Garcia ◽  
Ronan Le Goff ◽  
Maxime Gasse ◽  
Alexandre Aussem
Keyword(s):  
Injection Molding ◽  
Physical Phenomenon ◽  
Sensor Data ◽  
Injection Molding Process ◽  
Molding Process ◽  
Virtual Sensor ◽  
Polymer Injection ◽  
Sensor Configuration ◽  
Data Driven Modeling ◽  
Polymer Injection Molding

The subject discussed in this article concerns the determination of optimal sensor (pressure & temperature) configurations for polymer injection moulds. A sensor configuration is considered optimal when it is able to predict the product quality (dimension, warpage, etc.) with a good accuracy (from experimental data provided by these sensors). Initially, plastic engineers integrated sensors in moulds to acquire knowledge about their processes and to have better understanding of physical phenomenon. This article presents a numerical methodology to identify optimal combinations of sensors. The methodology is firstly based on polymer injection molding simulation to collect virtual sensor data. In a second step, virtual sensor data are analyzed by modern data-driven modeling techniques to identify optimal sensor configurations.

Computational Study of the Effect of Governing Parameters on a Polymer Injection Molding Process for Single-Cavity and Multicavity Mold Systems

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
M. Tutar ◽  
A. Karakus
Keyword(s):  
Injection Molding ◽  
Mold Insert ◽  
Polymer Melt ◽  
Injection Molding Process ◽  
Melt Flow ◽  
Flow Conditions ◽  
Molding Process ◽  
Polymer Injection ◽  
Solution Approach ◽  
Polymer Injection Molding

In the present study a more complete numerical solution approach using parallel computing technology is provided for the three-dimensional modeling of mold insert polymer injection molding process by considering the effects of phase-change and compressibility for non-Newtonian fluid flow conditions. A volume of fluid (VOF) method coupled with a finite volume approach is used to simulate the mold-filling stage of the injection molding process. The variations in viscosity and density in the polymer melt flow are successfully resolved in the present VOF method to more accurately represent the rheological behavior of the polymer melt flow during the mold filling. A comprehensive high-resolution differencing scheme (compressive interface capturing scheme for arbitrary meshes or CICSAM) is successfully utilized to capture moving interfaces and the pressure-implicit with splitting operators pressure-velocity coupling algorithm is employed to enable a higher degree of approximate relation between corrections for pressure and velocity. The capabilities of the proposed numerical methodology in modeling real molding flow conditions are verified through quantitative and qualitative comparisons with other simulation programs and the data obtained from the experimental study conducted. The present numerical results are also compared with each other for a polypropylene female threaded adaptor pipe fitting model with a metallic insert for varying governing process conditions/parameters to assess the modeling constraints and enhancements of the present numerical procedure and the effects of these conditions to optimize the polymer melt flow for mold insert polymer injection molding process. The numerical results suggest that the present numerical solution approach can be used with a confidence for further studies of optimization of design of mold insert polymer injection molding processes.

Robust Parameter Design of the Precision Injection Molding Process

1994 ◽  
Author(s):  
Sornkrit Leartcheongchowasak ◽  
Merwan Mehta ◽  
Hamid Al-Kadi ◽  
Keith Sequeira ◽  
Brian Snow ◽  
...  
Keyword(s):  
Injection Molding ◽  
Taguchi Methods ◽  
Injection Molding Process ◽  
Robust Parameter Design ◽  
Molding Process ◽  
Product Lines ◽  
Molded Parts ◽  
Robust Parameter ◽  
Independent Process ◽  
The Right

Abstract The most important problem, causing defective parts, in the injection molding process, is nonuniform shrinkage of molded parts. This leads to an iterative trial-and-error cycles of modification of mold cavity and core to arrive at the right dimensional size required which can occasionally to complete retooling. For this process, there are many factors that can be thrown out of control. Using the traditional scientific approach, engineers have longed to understand the mechanics of the process to control it, with limited success. In this paper, a design of experiments setup, using the Taguchi Methods, was done to reduce the nonuniform shrinkage. The company where the experiment was carried out is a precision parts molder for their own product lines. By using the internal experts from the company, a list of independent process parameters with no interactions which were thought the most responsible for dimensional size were listed. As there were 13 such parameters, it was decided to use the L27 orthogonal array. The optimum value that the company experts thought would produce the right part were used as the settings for the initial experiment. The 27 experiments were then performed, allowing sufficient time to let the machine stabilized between the experiments. The S/N ratio calculation for 27 experiments was explained. Next the calculations for the percentage that each parameter contributes to the dimension was determined. Finally, a confirmation experiment was performed to verify the results.

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