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TECHNOLOGICAL WATCH

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Living by numbers: How to take process monitoring to the next level

Josef Gießauf, Engel Austria, looks how process monitoring can be taken to the next level using intelligent systems and explains just how much numbers matter.

The machine control enables the injection moulding process to be monitored effectively by means of quality-relevant process parameters

Cyclically measured process parameters are used for monitoring and optimising injection moulding processes. In the past, attention was focused on axis movements and the associated necessary forces and times. Now, it’s time to go a step further. Intelligent assistant systems enable plastics processors to generate parameters that are relevant for component quality: injected volume, melt viscosity variation, and pressure deviations during injection.

One decisive success factor for the efficiency of injection moulding processes is a low reject rate. In spite of repeatable machine movements, the quality of the produced components can be subject to certain variations, caused by changes in ambient conditions or the material properties, for example. If such variations are detected and compensated early enough during ongoing production, process efficiency can be improved. Hereby, it is most effective to use signals that are provided anyway by the sensors in the machine. For example, the signals from sensors used for the control and sequencing of machine movements, also permit conclusions about component quality to be drawn. However, it must be noted that these sensors are located further away from the actual processing location than sensors in the mould.

Taking these factors into account, intelligent assistant systems are developed. Integrated into the machine control, they even enable manufacturing processes to continuously optimise themselves – a feature that will be standard in the factory of the future! The software iQ weight monitor from Engel provides plastics processors with data informing them for which processes the investment in additional assistant systems is worthwhile.

Focus on filling procedure

While developing the software, particular attention was payed to cavity filling. The machine operator enters set-points for starting position, speed profile, and a switchover point for the screw movement in the speed-controlled filling phase. From this data, the machine control system calculates set-point pre-set values, which the injection controller fulfils as closely as possible.

Apart from injection speed, the resulting pressure curve depends on the amount of melt, the material's flowability, and the flow resistance. Due to the numerous influencing factors, the injection pressure curve is characteristic for the respective application – and therefore unique. Consequently, this curve is basically suitable for indirect quality monitoring.

Already several generations of injection moulding machine control systems offer the possibility of determining process parameters cycle by cycle from sensor signals such as injection pressure or screw displacement and monitoring them during the entire production process.

As an example, two usual process parameters for the filling procedure will be examined more closely: Melt cushion and flow number. It can be expected that the melt cushion provides data about shot weight, and the flow number about the material's flowability and the filling resistance.

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Table 1. Intentional manipulations of the process and their influence on component weight, melt cushion and flow number. Green: The parameter has changed in the expected direction. Red: The parameter has changed contrary to the expectation.

Classics under scrutiny

In order to determine the informative values of these two parameters, thin-walled test samples of polypropylene with 0.8 mm wall thickness and about 8.5 g shot weight were produced in a test series using a single-cavity mould. For this, an Engel Victory 330/120 injection moulding machine was used, which was fitted with an inline precision weighing system for automatic determination of the moulding weight.

The question was, how do the process parameters react to variations in viscosity, density, temperature, material quantity or flow resistance? In order to simulate such effects, deliberate changes were introduced in the process. The analysis of the resulting effects and influences on melt cushion, flow number, and component weight showed that the process parameters did not always change in the expected direction (Table 1). For example, it was expected that the melt cushion would change inversely to moulding weight – larger screw displacement leads to a smaller cushion. In the case of flow number, it was assumed that it would mirror the material's flowability. The figures show the amount of change. If no value is specified, this means that the change was less than the fluctuation range of the number.

That the process parameters reacted differently in practice, can be explained by the fact that the flow number is an integral value, for example. The area below the pressure curve not only responds to changes in pressure demand, but also to a shift of the curve along the time axis. Therefore, the flow number not only changes due to variations in melt flowability, but also as a function of the time when the backflow valve closes, as well as the actual amount of material. When interpreting the melt cushion, the problem is that the value only describes how far the screw has moved forwards, but not which melt quantity it has transported.

In most cases, melt cushion and flow number are able to indicate changes in the process, but neither process parameter permits conclusions about the cause of the change or its influence on component quality.

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Table 2. Intentionally introduced process changes and their influences on component weight: The process parameters injection volume and viscosity change in the respective expected directions.

Process parameters with significance

iQ weight monitor therefore focuses on other parameters. During every cycle, the software compares the injection pressure curve with a previously defined reference curve, and simultaneously calculates viscosity change as well as injection volume.

Here, injection volume is the primary process parameter. Even if the screw always moves in exactly the same manner. This does not mean that the same melt volume is also always transported. The reason for this is the backflow valve. Before injection starts, the valve is open, so that due to the different pressure conditions, the material is able to flow from the screw flights into the injection chamber. When injection starts, the pressure conditions are reversed. This means that the material flows back from the injection chamber into the screw flights until the valve closes. The software takes these phenomena into account and supplies a value for injection volume that corresponds to the actual shot weight.

The second parameter – viscosity change – is significant because viscosity determines melt flowability, which in turn determines the injection volume. Viscosity changes can result from variations in the material batches, the humidity content, or temperature changes.

Thirdly, the degree of conformity between the pressure curve and the reference curve provides valuable clues for additional interference factors during injection. For example, a strongly varying value could mean that the process setup is not ideal or a cold plug has formed.

One example illustrates how several factors can influence the injection process simultaneously. Contrary to the reference, the injection chamber contains 1cm³ more melt. This increased melt quantity leads to an earlier pressure increase. With the same viscosity, the pressure curve would correspond to the dashed line. But in this case, material viscosity is some 20 % higher. As if that were not enough, a cold plug has formed in the nozzle, which requires additional force at the start of the injection phase. Even in such a complex case, the algorithm of the iQ weight monitor can detect the respective magnitudes of the three individual effects.

The process parameters injection volume, viscosity change, and conformity of the pressure curves were also examined during the test series mentioned above. The results mirror the changes of the measured moulding weight with the calculated injection volume, although this also includes the sprue (Table 2). Contrary to melt cushion and flow number, the calculated viscosity change reacts exactly as can be assumed.

Because the monitoring system calculates the parameters already during injection, ie. before the mouldings can be weighed, it is possible to react to deviations from the set-points during the same cycle, thereby preventing rejects. Engel has also developed a solution for this inline control system. During every cycle, the iQ weight control adapts the switchover point to the actual state, thereby maintaining a constant injection volume. By shifting the switchover point, also the opening and closing times of the shut-off nozzles in the mould are adapted. If the software detects a change of viscosity, the system adjusts the holding pressure to ensure constant shrinkage compensation in the cavities.

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