scholarly journals A Recurrent Neural Network for Warpage Prediction in Injection Molding

2012 ◽  
Vol 10 (6) ◽  
Author(s):  
A. Alvarado-Iniesta ◽  
D.J. Valles-Rosales ◽  
J.L. García-Alcaraz ◽  
A. Maldonado-Macias
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Recurrent Neural Network ◽  
Injection Molding Process ◽  
Molding Process ◽  
Element Analysis ◽  
Vast Number ◽  
Production Run ◽  
Molded Parts ◽  
Plastic Components

Injection molding is classified as one of the most flexible and economical manufacturing processes with high volumeof plastic molded parts. Causes of variations in the process are related to the vast number of factors acting during aregular production run, which directly impacts the quality of final products. A common quality trouble in finishedproducts is the presence of warpage. Thus, this study aimed to design a system based on recurrent neural networksto predict warpage defects in products manufactured through injection molding. Five process parameters areemployed for being considered to be critical and have a great impact on the warpage of plastic components. Thisstudy used the finite element analysis software Moldflow to simulate the injection molding process to collect data inorder to train and test the recurrent neural network. Recurrent neural networks were used to understand the dynamicsof the process and due to their memorization ability, warpage values might be predicted accurately. Results show thedesigned network works well in prediction tasks, overcoming those predictions generated by feedforward neuralnetworks.

Determination of Optimum Process Parameters in Gas-Assisted Injection Molding

2011 ◽  
Vol 143-144 ◽  
pp. 494-498
Author(s):  
Ke Ming Zi ◽  
Li Heng Chen
Keyword(s):  
Injection Molding ◽  
Delay Time ◽  
Optimization Design ◽  
Gas Pressure ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Element Analysis ◽  
Melt Temperature

With finite element analysis software Moldflow, numerical simulation and studies about FM truck roof handle were conducted on gas-assisted injection molding process. The influences of melt pre-injection shot, gas pressure, delay time and melt temperature were observed by using multi-factor orthogonal experimental method. According to the analysis of the factors' impact on evaluation index, the optimized parameter combination is obtained. Therefore the optimization design of technological parameters is done. The results show that during the gas-assisted injection molding, optimum pre-injection shot is 94%,gas pressure is 15MPa,delay time is 0.5s,melt temperature is 240 oC. This study provided a more practical approach for the gas-assisted injection molding process optimization.

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.

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.

Comprehensive Online Control Strategies for Plastic Injection Molding Process

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Jaho Seo ◽  
Amir Khajepour ◽  
Jan P. Huissoon
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Thermal Control ◽  
Plastic Injection Molding ◽  
Injection Molding Process ◽  
Molding Process ◽  
Element Analysis ◽  
Thermal Dynamics ◽  
Uncertain Dynamics ◽  
Plastic Injection

This study proposes an effective thermal control for plastic injection molding (polymer: Santoprene 8211-45 with density of 790 kg/m3, injection pressure: 1400 psi (9,652,660 Pa)) in a laminated die. For this purpose, a comprehensive control strategy is provided to cover various themes. First, a new method for determining the optimal sensor locations as a prerequisite step for modeling and controller design is introduced. Second, system identification through offline and online training with finite element analysis and neural network techniques are used to develop an accurate model by incorporating uncertain dynamics of the laminated die. Third, an additive feedforward control by adding direct adaptive inverse control to self-adaptive PID is developed for temperature control of cavity wall (cavity size: 52.9 × 32.07 × 16.03 mm). A verification of designed controller's performance demonstrates that the proposed strategy provides accurate online temperature tracking and faster response under thermal dynamics with various cycle-times in the injection mold process.

Study on Improving the Life of Stereolithography Injection Mold

2012 ◽  
Vol 468-471 ◽  
pp. 1013-1016 ◽  
Author(s):  
Hua Qing Lai
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Injection Mold ◽  
Molding Process ◽  
Molded Parts ◽  
Injection Molded ◽  
Plastic Parts ◽  
Assembly Operations ◽  
Conventional Injection Molding ◽  
Shape And Size

Molding is one of the most versatile and important processes for manufacturing complex plastic parts. It is a method of fabricating plastic parts by utilizing a mold or cavity that has a shape and size similar to the part being produced. Molten polymer is injected into the cavity, resulting in the desired part upon solidification. The injection-molded parts typically have excellent dimensional tolerance and require almost no finishing and assembly operations. But new variations and emerging innovations of conventional injection molding have been continuously developed to offer special features and benefits that cannot be accomplished by the conventional injection molding process. This study aims to improving the life of stereolithography injection mold.

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

Simulating Part and Assembly Variation for Injection Molded Parts

2012 ◽  
Author(s):  
Samuel Lorin ◽  
Lars Lindkvist ◽  
Rikard Söderberg
Keyword(s):  
Injection Molding ◽  
Automotive Industry ◽  
Principal Component ◽  
Injection Molding Process ◽  
Molding Process ◽  
Process Noise ◽  
Variation Patterns ◽  
Plastic Components ◽  
Variation Simulation ◽  
Part Variation

Final geometrical variation and deviation have often a negative effect on product functionality and aesthetics. In the automotive industry, design concepts are being evaluated and optimized to withstand variation in the early phase of product development. For this end, simulation tools are employed. Input to these simulations is requirements on parts and fixtures or measurements from previously manufactured parts. In the case of plastic components, parts are often manufactured in the injection molding process. Here, different materials and process settings can make it difficult to predict deviation and variation based on similar parts. In order to perform accurate assembly variation simulation, part variation simulation need, therefore, to be included. In this work a methodology is presented to simulate part and assembly variation, due to process noise, for plastic components manufactured in the injection molding process. The methodology is based on designed computer experiment and utilizes the concept of geometrical covariance and principal component analysis to relate process noise to variation patterns using regression analysis. Part and assembly variation are simulated combined using the distribution of these variation patterns. The model used for part variation simulation has been verified against commercial injection molding software showing good agreement. An industrial case from the automotive industry is used to elicit the proposed methodology.

scholarly journals Surface Homogeneity of Injection Molded Parts

2018 ◽  
Vol 62 (4) ◽  
pp. 284-291 ◽  
Author(s):  
László Zsíros ◽  
József Gábor Kovács
Keyword(s):  
Injection Molding ◽  
Evaluation Method ◽  
Solid Phase ◽  
Color Space ◽  
Processing Parameters ◽  
Injection Molding Process ◽  
Mold Surface ◽  
Molding Process ◽  
Molded Parts ◽  
Injection Molded

In this paper we are presenting a novel method for color inhomogeneity evaluation. We proved that this method has a higher than 95 % linear correlation coefficient if results are correlated with human visual evaluations.We applied this evaluation method to analyze the homogenization in the injection molding process, therefore we measured the homogenization properties of various solid phase masterbatches on injection molded parts. We tested the effects of the processing parameters of injection molding and analyzed various dynamic and static mixers as well. We have also measured the influence of the mold surface texture on the sensation of inhomogeneities on the part surface.We have carried out our tests on an injection grade ABS material using various masterbatches. The method was based on the digitization of the molded flat specimens. The images of these specimens were evaluated with an own developed formula using the CIELAB color space resulting high correlation with human visual inspections.

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