A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

2010 ◽  
Vol 19 (5) ◽  
pp. 741-754 ◽  
Author(s):  
M. Fernández-Delgado ◽  
M. Reboreda ◽  
E. Cernadas ◽  
S. Barro
Keyword(s):  
Neural Networks ◽  
Injection Molding ◽  
Automotive Industry ◽  
Execution Times

Reaction Injection Molding in the Automotive Industry

1975 ◽  
Vol 11 (2) ◽  
pp. 87-98 ◽  
Author(s):  
David J. Prepelka ◽  
James L. Wharton
Keyword(s):  
Injection Molding ◽  
Automotive Industry ◽  
Reaction Injection Molding

Generation of the Storage Costs Function Using Neural Networks

2012 ◽  
Vol 245 ◽  
pp. 209-214
Author(s):  
Emilia Campean ◽  
Liviu Morar ◽  
Dumitru Pop ◽  
Stefan Pap
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Production Process ◽  
Automotive Industry ◽  
Raw Materials ◽  
Raw Material ◽  
Stock Management ◽  
Early Stages ◽  
Classical Models ◽  
Network Application

The main purpose of the paper is to develop a neural network application destined to the raw material stock management, as an performant alternative to the classical models of costs stock management. Stocks of goods that manufacturers would classify as raw materials stocks are, in a special sense, goods in early stages of the production process. The testing was made for three companies from the automotive industry, but it could be applied to any kind of Romanian organization.

Control of properties in injection molding by neural networks

2001 ◽  
Vol 14 (6) ◽  
pp. 819-823 ◽  
Author(s):  
S. Kenig ◽  
A. Ben-David ◽  
M. Omer ◽  
A. Sadeh
Keyword(s):  
Neural Networks ◽  
Injection Molding

Prediction of Powder Injection Molding Process Parameters Using Artificial Neural Networks

2012 ◽  
Vol 59 (2) ◽  
Author(s):  
Javad Rajabi ◽  
Norhamidi Muhamad ◽  
Maryam Rajabi ◽  
Jamal Rajabi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Injection Molding ◽  
Process Parameters ◽  
Powder Injection Molding ◽  
Powder Injection ◽  
Injection Molding Process ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural

The parameters of Powder Injection Molding (PIM) process were modeled by artificial neural networks (ANNs). The feed-forward multilayer perceptron was utilized and trained by back-propagation algorithm. Particle size, particle morphology, debinding time, and sintering temperature were taken into account and regarded as inputs of the ANN model. The outputs included relative density, wax loss, shrinkage, and hardness. The results obtained using the ANN model were in good agreement with the experimental data. In fact, they displayed an average R-value of 0.95 versus the experimental values. The optimum architecture of ANN was 7-4-1, in which the network was trained with Levenberg–Marquardt training algorithm. Thus, the ANN model can be used to evaluate, calculate, and forecast PIM process parameters.

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 Manufacture 4.0 with LabVIEW and Arduino Mega for distributed cooling control of the DeMag 250 Ton machine injection mold

2020 ◽  
pp. 5-10
Author(s):  
José Miguel MORENO ◽  
Victor Alfonso MORALES ◽  
Cesar Alejandro RUIZ ◽  
Guillermo Rubén PÉREZ
Keyword(s):  
Injection Molding ◽  
Automotive Industry ◽  
Injection Mold ◽  
Temperature Changes ◽  
Semicontinuous Process ◽  
Innovation Project ◽  
Plastics Injection Molding ◽  
Plastic Industry ◽  
And Control ◽  
Great Development

Nowdays the injection plastics processes at insdustrial level have had a great development in the bajio region with the arrival of new enterprise suppliers of the automotive industry that work particuary de injection molding. The plastics injection molding is a semicontinuous process that consists of inject a polymer in the molten state into a mold closed under pressure, throught a small hole called gate, in the mole the material is solidifies, the piece or final part is obtained when the mold is opened and remove the piece molding from the cavity [1]. To monitor and control the temperature changes in the plastics injection cicles permite reduce errors and costs in the process. In this project we propose to apply manufacture 4.0 using the Arduino Mega microcontroller and LabVIEW to monitor part of the process of the injection molding of the DeMag 250 Ton Machine, specifically the coolding system adapting to control the injection molde temperature. The preliminare results show that is apropiate to use the LabVIEW an Arduino Mega combination to generation of innovation project applied to the plastic industry.

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