The Defects Reduction in Injection Molding by Fuzzy Logic based Machine Selection System
The effective machine-job assignment of injection
molding machines is very important for industry because it is not
only directly affects the quality of the product but also the
performance and lifetime of the machine as well. The phase of
machine selection was mostly done by professionals or experienced
planners, so the possibility of matching a job with an inappropriate
machine might occur when it was conducted by an inexperienced
person. It could lead to an uneconomical plan and defects. This
research aimed to develop a machine selection system for plastic
injection machines as a tool to help in decision making of the user.
This proposed system could be used both in normal times and in
times of emergency. Fuzzy logic principle is applied to deal with
uncertainty and mechanical factors in the selection of both quantity
and quality criteria. The six criteria were obtained from a plastic
manufacturer's case study to construct a system based on fuzzy logic
theory using MATLAB. The results showed that the system was able
to reduce the defects of Short Shot and Sink Mark to 24.0% and
8.0% and the total defects was reduced around 8.7% per month.
[1] M. T. Tabucanon, D. N. Batanov, and D. K. Verma, "Decision support
system for multicriteria machine selection for flexible manufacturing
systems," Computers in Industry, vol. 25, 1994, pp. 131-143.
[2] Y. T. Iç and M. Yurdakul, "Development of a decision support system
for machining center selection," Expert Systems with Applications, vol.
36, 2009, pp. 3505-3513.
[3] Z. C. Lin, and C. B. Yang, "Evaluation of machine selection by the
AHP method," Journal of Materials Processing Technology, vol. 57,
1996, pp. 253-258.
[4] M. C. Arslan, B. Çatay, and E. Budak, "A decision support system for
machine tool selection," Journal of Manufacturing Technology
Management, vol. 15, no. 1, 2004, pp. 101-109.
[5] E. Çimren, B. Çatay, and E. Budak, "Development of a machine tool
selection system using analytic hierarchy process," in 4th CIRP
0
500
1000
1500
2000
2500
3000
3500
Short Shot Sink Mark
Defected Product (pieces)
Defect Type
Current System
Proposed System
International Seminar on Intelligent Computation in Manufacturing
Engineering, 2004.
[6] R. V. Rao, "Decision making in the manufacturing environment-using
graph theory and fuzzy multiple attribute decision making methods,"
Machine selection in a flexible manufacturing cell, Springer-Verlag,
London, 2007, pp. 139-148.
[7] T. C. Chu and Y. C. Lin, "A fuzzy TOPSIS method for robot selection,"
International Journal of Advanced Manufacturing Technology, vol. 21,
2003, pp. 284-290.
[8] H. S. Byun and K. H. Lee, "A decision support system for the selection
of a rapid prototyping process using the modified TOPSIS method,"
International Journal of Advanced Manufacturing Technology, vol. 26,
2005, pp. 1338-1347.
[9] S. Önüt, S. S. Kara, and T. Efendigil, "A hybrid fuzzy MCDM approach
to machine tool selection," Journal of Intelligence Manufacturing, vol.
19, 2008, pp. 443-453.
[10] H. M. S. Lababidi and C. G. J. Baker, "An expert system for dryer
selection using fuzzy logic," Computers and Chemical Engineering
Supplement, 1999, pp. 691-694.
[11] T. Y. Wang, C. F. Shaw, and Y. L. Chen, "Machine selection in flexible
manufacturing cell: A fuzzy multiple attribute decision-making
approach," International Journal of Production Research, vol. 38, no. 9,
2000, pp. 2079-2097.
[12] A. S. Hanna, and W. B. Lotfallah, "A fuzzy logic approach to the
selection of cranes," Automation in Construction. vol. 8, 1999, pp. 597-
608.
[13] V. Subramaniam, G. K. Lee, T. Ramesh, G. S. Hong, and Y. S. Wong,
"Machine selection rules in a dynamic job shop," International Journal
of Advanced Manufacturing Technology, vol. 16, no. 12, 2000, pp. 902-
908.
[14] V. Subramaniam, T. Ramesh, and A. S. Raheja, "Exploiting the
flexibility of multiple job routes in a Dynamic Job Shop," Studies in
informatics and control, vol. 11, no. 2, 2002, Publishing House of the
National Institute for R&D in Informatics, ICI Bucharest.
[15] T. Al-Hawari, S. Khrais, O. Al-Araidah, and A. F. Al-Dwairi, "2D laser
scanner selection using fuzzy logic," Expert Systems with Applications,
2010.
[16] O. Kulak, M. B. Durmuşoğlu, and C. Kahraman, "Fuzzy multi-attribute
equipment selection based on information axiom," Journal of Materials
Processing Technology, vol. 169, 2005, pp. 337-345.
[17] I. T Tanev, T. Uozumi, and Y. Morotome, "Hybrid evolutionary
algorithm-based real-world flexible job shop scheduling problem:
application service provider approach," Applied Soft Computing, vol. 5,
2004, pp. 87-100.
[18] N. Nagarur, P. Vrat, and W. Duongsuwan, "Production planning and
scheduling for injection moulding of pipe fittings: A case study,"
International Journal of Production Economics, vol. 53, 1997, pp. 157-
170.
[19] J. Huang, G. A. S├╝er, and S. B. R. Urs, "Genetic algorithm for rotary
machine scheduling with dependent processing times," Journal of
Intelligence Manufacturing, 2011.
[20] D. Cao, M. Chen, and G. Wan, "Parallel machine selection and job
scheduling to minimize machine cost and job tardiness," Computers &
Operations Research, vol. 32, 2005, pp. 1995-2012.
[21] M. J. Gordon, Total quality process control for injection molding, 2nd
edition, NJ John Wiley & Sons, Inc., 2010.
[22] C. Nakason, Plastic processing, Prince of Songkla University, 3rd
edition, Bangkok: Four Press, 2004. (In Thai)
[23] J. Lin, and R. J. Lian, "Self-organizing fuzzy controller for injection
molding machines," Journal of Process Control, vol. 20, 2010, pp. 585-
595.
[24] J. Bown, Injection molding of plastic components, United Kingdom:
Mcgraw-Hill, 1979.
[25] J. Yen, R. Langari and L.A. Zadeh, Industrial applications of fuzzy logic
and intelligent systems, New York, USA, 1994.
[26] T. J. Ross, Fuzzy logic with engineering applications, 3rd Edition,
Chichester: John Wiley & Sons, Ltd., 2010.
[27] H. Jiang, R. Browning, and H. J. Sue, "Understanding of scratchinduced
damage mechanisms in polymers," Polymer, vol. 50, 2009, pp.
4056-4065.
[1] M. T. Tabucanon, D. N. Batanov, and D. K. Verma, "Decision support
system for multicriteria machine selection for flexible manufacturing
systems," Computers in Industry, vol. 25, 1994, pp. 131-143.
[2] Y. T. Iç and M. Yurdakul, "Development of a decision support system
for machining center selection," Expert Systems with Applications, vol.
36, 2009, pp. 3505-3513.
[3] Z. C. Lin, and C. B. Yang, "Evaluation of machine selection by the
AHP method," Journal of Materials Processing Technology, vol. 57,
1996, pp. 253-258.
[4] M. C. Arslan, B. Çatay, and E. Budak, "A decision support system for
machine tool selection," Journal of Manufacturing Technology
Management, vol. 15, no. 1, 2004, pp. 101-109.
[5] E. Çimren, B. Çatay, and E. Budak, "Development of a machine tool
selection system using analytic hierarchy process," in 4th CIRP
0
500
1000
1500
2000
2500
3000
3500
Short Shot Sink Mark
Defected Product (pieces)
Defect Type
Current System
Proposed System
International Seminar on Intelligent Computation in Manufacturing
Engineering, 2004.
[6] R. V. Rao, "Decision making in the manufacturing environment-using
graph theory and fuzzy multiple attribute decision making methods,"
Machine selection in a flexible manufacturing cell, Springer-Verlag,
London, 2007, pp. 139-148.
[7] T. C. Chu and Y. C. Lin, "A fuzzy TOPSIS method for robot selection,"
International Journal of Advanced Manufacturing Technology, vol. 21,
2003, pp. 284-290.
[8] H. S. Byun and K. H. Lee, "A decision support system for the selection
of a rapid prototyping process using the modified TOPSIS method,"
International Journal of Advanced Manufacturing Technology, vol. 26,
2005, pp. 1338-1347.
[9] S. Önüt, S. S. Kara, and T. Efendigil, "A hybrid fuzzy MCDM approach
to machine tool selection," Journal of Intelligence Manufacturing, vol.
19, 2008, pp. 443-453.
[10] H. M. S. Lababidi and C. G. J. Baker, "An expert system for dryer
selection using fuzzy logic," Computers and Chemical Engineering
Supplement, 1999, pp. 691-694.
[11] T. Y. Wang, C. F. Shaw, and Y. L. Chen, "Machine selection in flexible
manufacturing cell: A fuzzy multiple attribute decision-making
approach," International Journal of Production Research, vol. 38, no. 9,
2000, pp. 2079-2097.
[12] A. S. Hanna, and W. B. Lotfallah, "A fuzzy logic approach to the
selection of cranes," Automation in Construction. vol. 8, 1999, pp. 597-
608.
[13] V. Subramaniam, G. K. Lee, T. Ramesh, G. S. Hong, and Y. S. Wong,
"Machine selection rules in a dynamic job shop," International Journal
of Advanced Manufacturing Technology, vol. 16, no. 12, 2000, pp. 902-
908.
[14] V. Subramaniam, T. Ramesh, and A. S. Raheja, "Exploiting the
flexibility of multiple job routes in a Dynamic Job Shop," Studies in
informatics and control, vol. 11, no. 2, 2002, Publishing House of the
National Institute for R&D in Informatics, ICI Bucharest.
[15] T. Al-Hawari, S. Khrais, O. Al-Araidah, and A. F. Al-Dwairi, "2D laser
scanner selection using fuzzy logic," Expert Systems with Applications,
2010.
[16] O. Kulak, M. B. Durmuşoğlu, and C. Kahraman, "Fuzzy multi-attribute
equipment selection based on information axiom," Journal of Materials
Processing Technology, vol. 169, 2005, pp. 337-345.
[17] I. T Tanev, T. Uozumi, and Y. Morotome, "Hybrid evolutionary
algorithm-based real-world flexible job shop scheduling problem:
application service provider approach," Applied Soft Computing, vol. 5,
2004, pp. 87-100.
[18] N. Nagarur, P. Vrat, and W. Duongsuwan, "Production planning and
scheduling for injection moulding of pipe fittings: A case study,"
International Journal of Production Economics, vol. 53, 1997, pp. 157-
170.
[19] J. Huang, G. A. S├╝er, and S. B. R. Urs, "Genetic algorithm for rotary
machine scheduling with dependent processing times," Journal of
Intelligence Manufacturing, 2011.
[20] D. Cao, M. Chen, and G. Wan, "Parallel machine selection and job
scheduling to minimize machine cost and job tardiness," Computers &
Operations Research, vol. 32, 2005, pp. 1995-2012.
[21] M. J. Gordon, Total quality process control for injection molding, 2nd
edition, NJ John Wiley & Sons, Inc., 2010.
[22] C. Nakason, Plastic processing, Prince of Songkla University, 3rd
edition, Bangkok: Four Press, 2004. (In Thai)
[23] J. Lin, and R. J. Lian, "Self-organizing fuzzy controller for injection
molding machines," Journal of Process Control, vol. 20, 2010, pp. 585-
595.
[24] J. Bown, Injection molding of plastic components, United Kingdom:
Mcgraw-Hill, 1979.
[25] J. Yen, R. Langari and L.A. Zadeh, Industrial applications of fuzzy logic
and intelligent systems, New York, USA, 1994.
[26] T. J. Ross, Fuzzy logic with engineering applications, 3rd Edition,
Chichester: John Wiley & Sons, Ltd., 2010.
[27] H. Jiang, R. Browning, and H. J. Sue, "Understanding of scratchinduced
damage mechanisms in polymers," Polymer, vol. 50, 2009, pp.
4056-4065.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:53909", author = "S. Suwannasri and R. Sirovetnukul", title = "The Defects Reduction in Injection Molding by Fuzzy Logic based Machine Selection System", abstract = "The effective machine-job assignment of injection
molding machines is very important for industry because it is not
only directly affects the quality of the product but also the
performance and lifetime of the machine as well. The phase of
machine selection was mostly done by professionals or experienced
planners, so the possibility of matching a job with an inappropriate
machine might occur when it was conducted by an inexperienced
person. It could lead to an uneconomical plan and defects. This
research aimed to develop a machine selection system for plastic
injection machines as a tool to help in decision making of the user.
This proposed system could be used both in normal times and in
times of emergency. Fuzzy logic principle is applied to deal with
uncertainty and mechanical factors in the selection of both quantity
and quality criteria. The six criteria were obtained from a plastic
manufacturer's case study to construct a system based on fuzzy logic
theory using MATLAB. The results showed that the system was able
to reduce the defects of Short Shot and Sink Mark to 24.0% and
8.0% and the total defects was reduced around 8.7% per month.", keywords = "Injection molding machine, machine selection, fuzzy
logic, defects in injection molding, matlab.", volume = "7", number = "2", pages = "213-9", }
