Mold Filling Simulation and Smart Manufacturing under Industry 4.0 (2): Smart Injection Machines & Their Adjustment Principles

Tober Sun

From the concept of integrating virtuality and reality in Industry 4.0 mentioned in the previous chapter, we understand that machine movement is highly relevant with product quality in injection molding. To attain accurate mold filling analysis, we must consider the machine movements, including the screw acceleration and deceleration in the injection process, the machine reaction at the moment when filling turns into packing, and the machine’s protection behaviors for preventing the injection pressure from being too high.

Currently, the core of molding equipment is injection machines. Therefore, under Industry 4.0, the smart machine applications are majorly on injection machine design. The applications include: 1. The injection machine sends back the production management data; 2. The data exchange among the injection machine and the peripheral assistant machines; 3. The smart adjustment to the molding process. The smart adjustment on the molding process, especially, dictates the future of the injection molding industries. The reason is, until now, the development in this industry still highly relies on people’s experience. Neither mold filling analysis nor smart machines can substitute people’s experience. Nevertheless, along with the data induction and management as well as production information collection, we are not far from automatic trial molding. In this article, we will introduce how injection machine manufacturers apply the injection information collected from injection process to enhance product quality stability.

To increase product quality stability, we can properly adjust molding parameters to offset the quality instability caused by the environment variation. Based on their experience, molding professionals know they sometimes must adjust molding parameters to offset the effects of environment temperature in the morning and evening as well as in summer and winter. The concept of smart machines is to directly change the molding conditions at every molding cycle through scientific methods. Currently, the major technology includes Engel (iQ flow control), Wittmann Battenfeld (HiQ-Flow) and KraussMaffei (APC). Take Battenfeld HiQ-Flow as an example, they set up the upper and lower limits of the injection pressure variation of the qualified product range by monitoring the injection pressure, that is, taking the qualified product’s injection pressure curve as the standard. If the mold, melt viscosity and injection pressure all remain the same, the injection pressure does not change. However, if the mold environment (the runner balance of a multiple-cavity mold or the mold temperature) or melt viscosity (melt temperature uniformity, degradation and batch difference) changes, the injection pressure curve will fall out of the qualified product range even under the same injection speed. It will cause unstable product quality.

Injection pressure is the product of shear rate and viscosity. If we observe a decrease in the injection pressure, while the machine injection speed remains the same, it means the melt viscosity has decreased. Usually it is caused by the increase of temperature. Meanwhile, the melt density decreases due to higher temperature. Therefore, if we want to keep the product weight the same, the injected volume/stroke should increase, or the VP switch point should be deferred. How much it should be deferred depends on how much this molding pressure deviates from the standard line of the qualified products. Injection machine manufacturers have different methods for this issue. Battenfeld’s adjustment is according to the equality of pressure and total area under the position, that is, the equality of the work of injection during the injection process. KraussMaffei’s adjustment is according to the compensation of the PVT properties. It means the material density (specific volume) changes as the temperature changes knowing that the injection pressure is different from the baseline. To compensate for the material density change, we must modify the VP switch point and packing pressure so that the product weight can remain the same. Through this method we must input the type of plastic used in the controller interface so that the correct PVT relationship can thus be decided from its databank. The following table summarizes the process adjustment technology from major manufacturers:

Manufacturer Adjustable Molding Conditions The Environment Variation to be Eliminated
Engel VP switch point Viscosity
Wittmann Battenfeld VP switch point, packing pressure, screw plasticization and the closing speed of the non-return valve Viscosity and the cushion area process
KraussMaffei VP switch point and packing pressure Viscosity and the non-return valve

We can understand that the injection machine has transformed from a simple provider of melt kinetic energy into the role as a sensor. We can also understand how the melt is injected into the mold so that we can further adjust the process for the melt behaviors under different temperatures and pressures. This helps us to achieve the goal of realizing smart machines. The immediate adjustment during production can be realized as “Predictive Manufacturing”. According to Dr. Jay Lee again, predictive manufacturing means not only to create the value of manufacturing but also to add the “introspection” function to the manufacturing process. In other words, the whole system, including the equipment itself, has to take immediate actions to the changes in the manufacturing process. On the Cross-Strait CEO Summit 2018,Dr. Jay Lee, who has just taken over the position of the vice president of Foxconn Industrial Internet (FII), said, “smart manufacturing is not born to solve problems, but is born to perceive and predict the problems, and solve the problems that we could not solve in the past.” Therefore, we can say that the era of Industry 4.0 is the era of predictive manufacturing.

Dr. Tober Sun
Director, Material Research Center of CoreTech System (Moldex3D)
With a doctoral degree of Polymer Science at University of Connecticut, Dr. Sun’s expertise is in the research of composites, biomedical materials, degradable polymer, plastic material application in industrial designs as well as polymer rheology, polymer processing and polymer physical properties. He has been the Manager of Moldex3D Technical Support Division and Automotive Project, and is a long-term lecturer of Moldex3D’s global professional courses and seminars.

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