Optimization of a medical device based on injection molding

Authors

  • Israel Morales Almendares Universidad Autónoma de Ciudad Juárez
  • Luis Alberto Rodríguez-Picón Universidad Autónoma de Ciudad Juárez
  • Soledad Vianey Torres Arguelles Universidad Autónoma de Ciudad Juárez
  • Iván Juan Carlos Pérez Olguín Universidad Autónoma de Ciudad Juárez

DOI:

https://doi.org/10.61799/2216-0388.617

Keywords:

injection molding, molding optimization, response surface, design of experiments

Abstract

In this article, the optimization of a medical product is presented, for this product injection molding was used as it is a process capable of providing low manufacturing costs, low times of transformation of the raw material and products obtained with different forms complex. The main factors were the mold temperature, melting temperature, injection time and cooling time. In the optimization design of experiments was used, later the most pronounced descent method was implemented, it was possible to adjust by means of the composite central design and the optimal point of the function was estimated by canonical analysis. Using the CAE Moldex3D software, the injection was simulated. The optimization reduced the total warpage of the piece up to 0.2 mm, resulting in more significant factors of the melting temperature, injection time and cooling time.

Downloads

Download data is not yet available.

References

I. Visnjic Kastalli and B. Van Looy, “Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance,” J. Oper. Manag., vol. 31, no. 4, pp. 169–180, 2013

C. Rusinko, “Green manufacturing: An evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes,” IEEE Trans. Eng. Manag., vol. 54, no. 3, pp. 445–454, 2007

S. Agarwal et al., Applied Plastics Engineering Handbook, 2nd ed. Norwich, NY: William Andrew Publishing, 2017

T.L. Smith, “Physical Properties of Polymers–an Introductory Discussion,” Polym. Eng. Sci., vol. 13, no. 3, pp. 161–175, 1973

P.K. Bharti, M.I. Khan, and H. Singh, “Recent Methods for Optimization of Plastic Injection Molding Process – a Retrospective and Literature Review,” Int. J. Eng. Sci. Technol., vol. 2, no.9, pp. 4540–4554, 2010

M. Leite, M. Barrozo, and J. Ribeiro, “Canonical Analysis Technique as an Approach to Determine Optimal Conditions for Lactic Acid Production by Lactobacillus helveticus ATCC 15009,” Int. J.Chem. Eng., vol. 2012, 2012

S. J. Téllez-Luis, A. Moldes, J. Alonso, and M. Vázquez, “Optimization of Lactic Acid Production by Lactobacillus delbrueckii through Response Surface Methodology,” J.Food Sci.,vol.68, pp.1454–1458, 2006

C. Liyana-Pathirana and F. Shahidi, “Optimization of extraction of phenolic compound from wheat using response surface methodology,” Food Chem., vol. 93, pp. 47–56, 2005

N. Yousefi, F. Zeynali, and M. Alizadeh, “Optimization of low-fat meat hamburger formulation containing quince seed gum using response surface methodology,”J.Food Sci.Technol.,vol.55,2017

G. Danmaliki, T. Saleh, and S. Ahmad, “Response Surface Methodology Optimization of Adsorptive Desulfurization on Nickel/Activated Carbon”, vol. 313, 2017

N. Sulaiman, R. Hashim, M.H. Mohamad Amini, M. Danish, and O. Sulaiman, “Optimization of activated carbon preparation from cassava stem using response surface methodology on surface area and yield,” J. Clean. Prod., vol. 198, 2018

L. Freeman, A. Ryan, J. Kensler, R. Dickinson, and G. Vining, “A Tutorial on the Planning of Experiments,” Qual. Eng., vol. 25, 2013

S.K.S. Fan and K.-N. Huang, “A new search procedure of steepest ascent in response surface exploration,” J. Stat. Comput. Simul., vol. 81, no. 6, pp. 661–678, 2011

G. E. P. Box and K. B. Wilson, “On the Experimental Attainment of Optimum Conditions,” 1992, pp. 270–310

G. E. P. Box and D. W. Behnken, “Some New Three Level Designs for the Study of Quantitative Variables,” Technometrics, vol. 2, no. 4, pp. 455–475, 1960

M. Raymond H, D. C. Montgomery, and C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons, 2016.

E. Del Castillo, Process Optimization:A Statistical Approach, vol. 105. Boston, MA: Springer US, 2007.

Y. Yang and F. Gao, “Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model,” Polym. Eng. Sci., vol. 46, pp. 540–548, 2006

U.M. Attia and J.R. Alcock, “An evaluation of process-parameter and part-geometry effects on the quality of filling in micro-injection moulding,” Microsyst. Technol., vol. 15, no. 12, pp.1861–1872, Dec. 2009

D.C. Montgomery, Design and Analysis of Experiments, 8th ed., vol. 2. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012

Published

2020-07-01

How to Cite

Morales Almendares, I. ., Rodríguez-Picón, L. A. ., Torres Arguelles, S. V. ., & Pérez Olguín, I. J. C. . (2020). Optimization of a medical device based on injection molding. Mundo FESC Journal, 10(20), 14–23. https://doi.org/10.61799/2216-0388.617

Issue

Section

Artículo Originales

Most read articles by the same author(s)