Process Control
In injection molding, there are many ways to control the process—from the manual operator who can start or stop the cycle at will to a fully automated process operating the machine without direct operator intervention. Often,controls are based on a work parameter thatis integrated with respect to time during the injection of plasticized material into a mold,thus defining a “work index” thatis used to control the molding machine. Typically, a monitor records specified variables during each cycle. Such monitoring systems, however, require manual changes to be made by an operator to obtain consistently uniform products. These days,with computer control, the process can be completely automated. (RDC 11/6/2010)
U.S.Patent 4,146,601, 3/27/1979,”Injection Molding Process Control”
Recent US Patents
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8/4/2009
7,569,169
Injection molding flow control apparatus and method
7/14/2009
7,559,762
Open loop pressure control for injection molding
4/14/2009
7,517,480
Automatic process control for a multilayer injection molding apparatus
7,517,206
Control system of molding machine
7,521,008
Method for designing a resin product, method for producing a resin product and injection molding device
3/27/1979
4,146,601
Injection Molding Process Control
Bishop of HPM Corporation developed a control system based onintegration of a sensed injection pressure with respect to time from the beginning of an injection stroke until plasticized material in a mold reaches a predetermined pressure,resulting in a work index. If a mold cavity pressure sensor indicates that the cavity is full, it stops the integration and it may also reduce the injection pressure to a lower value, sufficient to supply additional material to compensate for shrinking due to cooling, yet not high enough to introduce strains or cause flashing. (RDC 11/6/2010)
Recent Journal Articles
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11/6/2010
Warpage Optimization in Injection Molding Using MPSO-HNN and MPSO Algorithm
(1247–1256) Polymer - Plastics Technology and Engineering 49 #12 (2010)
Liet al used a hybrid neural network trained by modified particle swarm optimization (MPSO-HNN) to predict warpage based on a Taguchi experiment. Their results show that the proposed algorithm requires fewercalculating amounts,spends less time,and has better generalization ability than those in related literature. (RDC 11/6/2010)
7/24/2009
Quality Problems during Mold Start-Up? Process Control
(13-16) Plastics Europe #6 (2009) English version of Kunststoff
Review Articles
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Editor's Notes
As the literature is reviewed, and items of interest concerning this topic are found. These may be added in an abbreviated form with the reference. Readers and contributors are invited to add their own notes. Contributors may add them directly and other readers can simply send their notes to the editor, Roger Corneliussen at rcorneliussen@4spe.org. He may edit and add them to this page at his discretion.