Robust process control for rubber injection moulding with use of systematic simulations and improved material data

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1 Gummi Fasern Kunststoffe, 67, No. 10, 2014, pp Robust process control for rubber injection moulding with use of systematic simulations and improved material data M. Fasching 1, G. Berger 2, W. Friesenbichler 2, P. Filz 3, and B. Helbich 3* 1 Polymer Competence Center Leoben GmbH, Austria 2 University of Leoben, Austria 3 Simcon kunststofftechnische Software GmbH, Würselen, Germany Selected from International Polymer Science and Technology, 41, No. 11, 2014, reference GK 14/10/640; transl. serial no Translated by M. Grange Summary The heating phase is by far the most time-consuming part of rubber injection moulding. The determination of a suitable curing time is difficult despite extensive injection moulding trials, since in many cases the quality of a part, such as its degree of vulcanisation which is often visually detectable only where there is significant undercure (Figure 1a), cannot be assessed without closer examination (Figure 1b). Systematic injection moulding simulation with Varimos presents an effective solution because the Varimos methodology allows virtual detection of the impacts of process parameters and their interactions, as well as visualisation of e.g. impending undercure (Figure 1c). It therefore enables part, mould and process to be optimised automatically to the desired quality criteria. As a result, it is possible not only to save valuable machine time, which would otherwise be taken up for process development, but also to define the processing window more precisely and thus to improve the economic efficiency of the process while at the same time ensuring the necessary process stability. This was successfully investigated and demonstrated using a thick-walled test piece during the RubSim project at the Polymer Competence Center Leoben GmbH in collaboration with the Injection Moulding of Polymers Group of the University of Leoben and the industrial partners SKF Sealing Solutions Austria GmbH, Engel Austria GmbH, Simcon kunststofftechnische Software GmbH and Dr. Gierth Ingenieurgesellschaft mbh. INTRODUCTION For injection moulded thermoplastics, the Varimos optimisation system from Simcon kunststofftechnische Software GmbH is already a recognised software tool for the automatic optimisation of part, mould and process [1-4]. While the main focus in the injection moulding of thermoplastics is on the dimensional stability of the moulded parts and how this is affected by mould compensation, temperature control and process conditions, the most important parameter in rubber injection moulding in terms of obtaining the desired component quality is to achieve a defined degree of cure, which should be distributed as homogeneously as possible over the cross-section of the moulding. Within the RubSim project, the existing methodology has therefore been adapted for elastomer components with the aim of virtually modelling and optimising the response variables that can be obtained from an injection moulding simulation, such as component quality (degree of cure), process stability (incubation progress during the filling phase) and productivity. This was carried out at the Polymer Competence Center Leoben GmbH in collaboration with the Injection Moulding of Polymers Group of the University of Leoben and an industrial consortium consisting of SKF Sealing Solutions Austria GmbH, Engel Austria GmbH, Simcon kunststofftechnische Software GmbH, Germany and Dr. Gierth Ingenieurgesellschaft mbh, Germany Smithers Information Ltd. T/1

2 For the heating phase simulation, the vulcanisation behaviour of the rubber compound is most relevant. This was also measured using an RPA. As shown in Figure 2, both the incubation time and the reaction rate depend exponentially on the material temperature. Correct simulation of the time- and location-dependent material temperature is therefore also of great importance, with the thermal conductivity and specific heat capacity of the rubber compound representing the main influencing variables. Figure 1. (a) Blistering resulting from severe undercure, (b) visually undetectable undercure, (c) identification in the simulation MATERIAL DATA FOR INJECTION MOULDING SIMULATION A successful injection moulding simulation requires a knowledge of the actual material behaviour and its description in the simulation using suitable material models. Because of the complex formulation of rubber compounds and the high degree of customisation, there are problems associated with the use of existing material data sets from commercially available databases. The University of Leoben s Injection Moulding of Polymers Group therefore characterised NBR model compounds provided for the project by SKF Sealing Solutions Austria. For the filling phase simulation, the most important influencing variable is the shear viscosity. This was measured using a Rubber Process Analyzer (RPA) for low shear rates (below 300 s -1 ) and a high pressure capillary rheometer for shear rates higher than 100 s -1. With RPA measurements in particular, high filler contents and filler interactions by agglomeration have a significant impact on the measurement results, and therefore parameters were developed for pre-shearing the material prior to the actual measurement in order to reduce these interactions [5, 6]. This makes it possible to approximate the processing history occurring in reality, where these filler agglomerations are reduced by shearing in the screw. In addition, work has started on developing a new rheometer tool, which will permit direct viscosity measurements on the injection moulding machine with the actual processing history of the material. SYSTEMATIC SIMULATIONS After the calculation model had been successfully created (component geometry, cure and material datasets), the main influencing factors for the injection moulding process were defined. The mould temperature, injection temperature of the rubber compound, injection speed and heating time were used and were varied in a full-factorial experimental design with the addition of star points. A full-factorial experimental design combines a high and a low value of each input variable in every possible combination. With star points, individual input variables are set to values outside of the full-factorial experimental space, with all other input variables simultaneously set to the midpoint [7]. This makes it possible to define quadratic mathematical process models (cf. section 4), which can model the nonlinear nature of the material behaviour significantly better than would be the case with a purely linear model. Table 1 shows the process parameters selected, with selection of the upper and lower values being based on experience. Any star points that made no physical sense were omitted from the simulation design. The 21 individual simulation settings that resulted were prepared in Cadmould 3D-F and processed automatically using a task scheduler. To model as closely as possible the real heating of the test mould, which took place conventionally by means of heated platens, a tight network of heating/ cooling channels through which heat transfer oil flows at the appropriate nominal mould temperature was defined in the platen in the simulation instead of the cartridge heaters, as shown in Figure 3. The software functionality has since been extended so that electric cartridge heaters can also be simulated. It can be seen that the temperature profile of the material in the cavity Table 1. Values of input variables selected for the experimental design Star point Level Midpoint Level Star point Coded unit Mould temperature in C Injection temperature in C 36.22* Injection speed in cm 3 /s -7.03* Heating time in s 524* *Values make no physical sense and are therefore ignored T/2 International Polymer Science and Technology, Vol. 42, No. 3, 2015

3 is highly inhomogeneous, with the process being limited by the mould core in particular as this is only heated passively by thermal conduction (cf. section 5). MODELLING FOR INDIVIDUAL RESPONSE VARIABLES AND PARALLEL OPTIMISATION For three selected response variables, Minitab v. 16 statistical software was used to define mathematical models (Equation (1)) which allowed virtual calculation of the response variables within the entire experimental space starting from the influencing variables. The response variables were the scorch index at the end of the filling phase, the minimum degree of cure at the end of the heating phase and the resulting cycle time. The influencing variables were the mould temperature, injection temperature, injection speed and heating time. In Equation (1), Y denotes the respective response variable (1 i). K is a constant, A, B etc. are the model parameters for the main effects, AA, BB etc. the model parameters for quadratic effects and AB, AC etc. the model parameters for the respective interactions. x denotes the respective influencing variable (1 j). the quality specifications and since the production process has to be stable, the optimisation priority for productivity was downgraded relative to the other two response variables. Figure 4 shows the first-order model parameters, which have been normalised to the highest main influencing variable in each case to make it easier to compare them with one another. It can be seen that the quality feature of the scorch index at end of filling phase is most dependent on the influencing variables of mould temperature and injection speed, the degree of cure is determined primarily by the mould temperature and heating time and the cycle time depends substantially on the heating time selected. Y i = K + A x D x j + AA x DD x j 2 + AB x 1 x AD x 1 x j (1) To ensure adequate component quality, the minimum degree of cure was evaluated in the component after the heating phase and subsequent cooling at room temperature. The optimisation goal was to maximise this, with a degree of cure of 95% being set as the lower limit. Process stability was evaluated using the so-called scorch index at the end of the filling phase. The scorch index can assume values of between 0 and 1, with 0 meaning that the material s incubation time has only just started (to be precise, this exists only immediately after adding the curing system) and 1 meaning that the incubation time is over and the crosslinking reaction is starting. To avoid internal defects in the component which may be caused by the shearing of partly vulcanised material, a maximum scorch index of 0.9 was tolerated for the optimisation with a target value of 0.7. The resulting cycle time was used as a measure of productivity. Minimising the cycle time maximises productivity. This represents a direct conflict of the response variables for component quality, since a complete vulcanisation reaction is most easily achieved with a sufficiently long cure time, i.e. a sufficiently long heating time. On the other hand, if the mould temperature is increased in order to reduce the necessary heating time, the process stability will decrease because, with otherwise identical process settings, the scorch index at the end of the mould-filling phase goes up. However, since it is only possible to use components which meet Figure 2. Graph showing vulcanisation isotherms over time at different temperatures to illustrate the exponential temperature dependence of the reaction of the NBR used Figure 3. Material temperature across the component crosssection and component surface temperature in the recess after 800 s heating time shown with heating/cooling channels for modelling the heated platen 2015 Smithers Information Ltd. T/3

4 Using these models and the optimisation goals mentioned above, the set of parameters shown in Table 2 was proposed as the optimal process settings. However, it is by no means essential to use these precise process settings. The complete mathematical description of the experimental space enables the user to make manual changes to individual input variables at any time and to analyse the impact on the modelled response variables directly. The result of a change to the input variables can be shown directly using the Varimos software system. PRACTICAL APPLICATION Figure 4. Normalised first-order model parameters of the influencing variables for modelling the response variables of scorch index at end of filling phase, minimum degree of cure and cycle time Figure 5. Normalised model parameters for modelling the response variables of scorch index at end of filling phase, minimum degree of cure and cycle time with mould temperature (A), injection temperature (B), injection speed (C), heating time (D), 2 nd order effects (AA etc.) and interactions (AB etc.) The simulation results were verified within the framework of a reduced experimental design in real injection moulding experiments, using a test mould with a single cavity. The proposed process optimum was also tested and compared with initial values from a conventional optimisation. Part quality was evaluated both by visual assessment of the cross-section of the part (Figure 1) and by using the standard test to determine compression set based on ISO 815. The compression set test measures the deformation remaining after a defined load has been applied to a specimen. Lower residual deformation represents higher component quality. Figure 6 illustrates a comparison between the virtually optimised process point and conventionally optimised initial values (mould temperature 130 C, injection temperature 60 C, injection speed 24 cm 3 /s, heating time 2000 s). It can be seen that the virtual process optimum leads to significantly lower residual deformations and thus better material properties with half the heating time. Process optimisation using systematic simulations therefore succeeded, virtually and automatically, in improving the process significantly within 48 h. Better component quality (lower compression set) was achieved while Table 2. Virtually determined optimal process settings Mould temperature Injection temperature Injection speed Heating time C 64.0 C 23.4 cm 3 /s 938 s Figure 5 extends the illustration of the first-order influencing variables with the addition of higher-order effects and interactions. Relatively speaking, these are of lesser importance but overall they allow accurate modelling of the response variables. In the case of cycle time, it can be seen that no higher-order effects or interactions occur. This is to be expected, since the cycle time is simply a sum of the injection time (indirectly proportional to injection speed) and heating time, with the latter being predominant. Figure 6. Comparison of resulting compression set for setup with initial values from conventional optimisation versus virtual optimisation T/4 International Polymer Science and Technology, Vol. 42, No. 3, 2015

5 reducing the heating time from 2000 s to 938 s. The alternative without the use of simulation would have been to optimise heating time and mould temperature on the injection moulding machine in systematic real injection moulding experiments, in which case just a single change in mould temperature would have taken several hours. The virtual determination of the optimum settings was achieved by simultaneously varying the influencing variables of mould temperature, injection temperature, injection speed and heating time and optimising part quality (minimum degree of cure to be achieved: 95%) while ensuring process stability (incubation progress at the end of the filling phase in the range of ) and at the same time optimising productivity (minimum cycle time). Another advantage of systematic process simulation lies in identifying improvement potential in the mould even before it has been built. Figure 3, for example, shows thermal inhomogeneity in the heating of the material. In the industrial development phase, a constructor can use simulation to evaluate in advance whether or not the use of design measures (such as the use of additional cartridge heaters) would be worthwhile over the production time of the article and can therefore decide on the more economical variant. CONCLUSIONS The injection moulding process for a thick-walled elastomer test component was virtually modelled and optimised by systematic simulations. The virtual results were verified in real injection moulding tests. A process optimum was successfully found which ensures both component quality and the highest possible economic efficiency. It was thus possible to dispense with cost-intensive and time-consuming experiments in determining functioning sets of process parameters. In addition, ways of improving mould design were identified. Correct measurement and modelling of the material behaviour were essential for successful use of the model. In the follow-up project RubSim II which is currently underway, two new Cadmould modules, 3D T-Box and 3D Thermal Analysis, are being used to optimise the mapping of the thermal behaviour of mould and component. In addition, mechanisms for calculating elastomer shrinkage are being examined in RubSim II and the impact of compound storage time and temperature on the expected processing behaviour is being systematically investigated and mapped in the simulation. ACKNOWLEDGEMENTS The present research project was conducted at the Polymer Competence Center Leoben GmbH within the framework of the COMET Competence Centres programme of the Austrian Federal Ministry for Transport, Innovation and Technology and the Austrian Federal Ministry of Economy, Family and Youth, with the participation of the University of Leoben (Injection Moulding of Polymers Group) and the industrial partners SKF Sealing Solutions Austria GmbH, Engel Austria GmbH, Simcon kunststofftechnische Software GmbH and Dr. Gierth Ingenieurgesellschaft mbh with funding from the Austrian Federal Government and the states of Styria and Upper Austria. Many thanks to the funding sponsors and the industrial partners for their support! REFERENCES 1. Hopman, C., Grümer, B., Berger, G.R., Neuss, A., Gierth, M., Gröger, T., Virtuelle Musterung fur robuste Prozesse, Kunststoffe 10/2013, Hopmann, C., John, M., Grümer, B., Weniger Ausschuss durch robuste Prozesse, Kunststoffxtra 10/2012, Filz, P., Virtuelle und reale Optimierung von Bauteil und Spritzgießprozess, VDI Jahrestagung der Spritzgießer 2011, Baden-Baden, February Gierth, M., Grümer, B., Collective Research Network Project "Advanced Part Sim II", Friesenbichler, W., Fasching, M., Perko, L., Shear and extensional viscosity measurement for rubber compounds by means of high pressure capillary rheometry, Proceedings of the Polymer Processing Society 30th Annual Meeting, June Fasching, M., Friesenbichler, W., Berger, G.R., Influence of material condition on processing of rubber compounds and its representation in simulation, The proceedings of the Austrian- Slovenian Polymer Meeting 2013, April Montgomery, D.C., Design and Analysis of Experiments, Third Edition, John Wiley & Sons, New York, Smithers Information Ltd. T/5

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