Estimation of cure characteristics in rubber extrusion lines

GONZÁLEZ MARCOS, A.; CASTEJÓN LIMAS M.(*); ALBA ELÍAS F.; MARTÍNEZ DE PISÓN ASCACIBAR F.J.

Departamento de Ingeniería Eléctrica y Electrónica Universidad de León

Campus de Vegazana, sn. 24071 SPAIN

http://torio.unileon.es/~diemcl

Abstract: - A direct control strategy for the process of mixing components for producing rubber for profiles of automotive industry is not suitable, since the goal is to build a product with specified parameters which are not directly measurable. The measuring process is carried out offline in a laboratory and downwards the process. Also in these cases an open control strategy must be adopted. In this work, the authors develop a model to online predict the cure characteristics of the blends according to their compound formulation and mixing conditions. If properly integrated into the manufacturing system, cure characteristics models can provide useful information to process the blends according to their specific characteristics. Key-Words: - Rubber extrusion lines; cure characteristics; mixing process; knowledge discovery; data mining 1 Introduction Nowadays, industries try to improve their manufacturing processes in order to increase the final quality of the product, save costs and therefore survive in an increasingly competitive world. During last decades, the gradual appearance of modern control systems in the industrial arena, makes possible the storage of an everyday increasing amount of information. Information reduces our uncertainty and, therefore, allows us to take decisions based on better criteria. Nevertheless, this massive amount of data implies a decrease in the easiness of interpretation. Moreover, most of the companies have no idea how to extract useful information from this huge amount of data.

Fortunately, we can use Knowledge Discovery and Data Mining techniques as a tool to extract implicit, previously unknown and potentially useful information from data, i.e. discover and present nontrivial knowledge in a form easily comprehensible to humans.

This paper presents the application of these techniques to a rubber extrusion manufacturing process since there is no certain knowledge of the relationship between the production conditions and the final properties of the extruded product even if the production process involved has been used for a long time.

One of the main problems of rubber manufacturers is the intrinsic variability of the raw material. This means that, irrespectively of how you manipulate it to obtain consistent results, i.e. high quality batches without wasting materials, you still have to consider

the variability of the product itself. Therefore, there is no doubt that rubber manufacturers have to face difficulties and research is needed to produce rubber compounds of excellent quality in the most economical possible way.

Very serious efforts are being made both in rubber mixing and extrusion processes and we can find a number of works in the specialized literature [1] – [9].

Since the mixing process is the first phase in rubber extrusion lines and a material with adequate properties is needed to fulfill all quality demands, we propose to model the mixing process according to the compound formulations and mixing conditions in order to predict the characteristics of the blends.

Some works aimed at creating models to predict Mooney viscosity [10] - [11], cure characteristics [12], etc., have already been published. This work extends the model developed by [12], where the minimum and maximum torque and the time to reach 50% of the state of cure were modelled using compound formulations. Our approach also takes into account mixing conditions to predict other important parameters of the rheometric curve, such as the scorch time and cure time.

By integrating the proposed models into the manufacturing system, it is possible to improve the information flow through the plant as it is not longer necessary to wait for laboratory results.

The paper is organized as follows. First, a description of the analyzed rubber mixing process is given in section 2. Then, the different techniques used to develop the models are described in section 3.

Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)

Finally, conclusions underlining the advantages of the proposed approach are given in section 4. 2 Rubber mixing process Rubber mixing is performed in batches and involves two phases that avoid a premature cure of the blends due to the high temperatures reached during the mixing process. First, the polymer, together with carbon black and other fillers, plasticizers and additives, is fed into an internal mixer. Ingredients are added in two or three stages, depending on their quantity. Upon completion of the mix cycle, the mixture is discharged through a door at the bottom of the mixer.

Once the batch is cooled, it is dumped onto a mill where it is further worked while being cooled. Additional compounding ingredients, such as curatives, are added at this point [13]. Finally, a mixed rubber sample is taken to perform a test and the blend passes through the batch-off machine where it is cooled, cut and stored. 2.1 Rheometric curve The cure characteristics of the batches were studied using an Oscillating Disk Rheometer (ODR 2000). From the torque response as function of the time at the selected temperature (180 ºC), we could measure the following useful parameters [14]:

Minimum torque (ml), which gives an indication of viscosity.

Scorch time (ts1). The time to scorch is the time required at a specified temperature for a rubber compound to form incipient crosslinks, that is, the beginning of the cure. Scorch measurement is very important in determining whether a given rubber compound can be processed in a particular operation. ODR scorch time is defined as the time until one torque unit rise above the minimum is achieved.

Cure time (tc90). This time refers to the amount of time required to reach 90% of the state of cure. It is considered as the optimum vulcanisation time at a specified temperature.

Time to 50% state of cure (tc50). This is the amount of time required to reach 50% of the state of cure.

3 Cure characteristics models 3.1. Database description To obtain a good predictive model of the cure characteristics and determine the essential structure and characteristics of the data, we needed a database

without missing or erroneous values. Therefore, the

Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)

database used in this work consisted of 1,240 samples taken from six different compound formulations and the following variables:

Compound formulation (input variables): - Polymer (3 feedings) (vca1, vca2, vca3) - Carbon black (3 feedings) (vne1, vne2, vne3) - Plasticizer (vexten)

Mixing conditions (input variables): - Initial temperature of the compound (terein) - Final temperature of the compound (terefi) - Electrical power (pocome) - Rotor speed (2 cycles) (prerci1, prerci2) - Cycle mixing time (2 cycles) (predu1, predu2) - Total mixing time (dureme).

Rheometric curve (output variables): - Scorch time (ts1) - Cure time (tc90)

3.2. Data preprocessing As a previous step to the modelling, it is often useful to visualize the experimental data in order to observe and detect their structure, possible outliers, different groups, etc. Two main techniques were used in this work: scatterplot matrix and projection techniques.

A visual inspection of all the possible pairwise scatterplots in the variables gave an idea of the relationships among all variables. For example, in Figure 1 we can see some expected relationships such as the one between the amount of polymer in the first feeding (vca1) and the electrical power consumed (pocome): the more polymer, the more electrical power required during the mixing. Also, it is possible to observe a relationship between the initial (terein) and final (terefin) temperatures of the compound. The absolute correlation between these variables is 0.78 and 0.72, respectively. Finally, we can also observe the differences between compounds.

On the other hand, the data structure was visualized by mapping multivariate data onto low-dimensional projection space, preserving as far as possible the original data structure. The projection technique used in this work was a nonlinear mapping algorithm such as Sammon’s projection [15].

Sammon’s mapping results revealed the existence of five different clusters. As the blends of each compound are similar to one another, it is not strange to observe that each class corresponds to each formulation. In this case, there are 6 compounds and 5 clusters because the ingredients and mixing conditions of two formulations are very similar and can be considered as a single one.

Figure 1. Relationship between compound formulation, mixing conditions and cure characteristics.

Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)

3.3. Neural network models Neural network analysis was used to obtain predictive models for the cure characteristics. Neural networks can "learn," automatically, complex relationships among data. This feature makes the technique very useful in modelling processes for which mathematical modelling is difficult or impossible.

To obtain the best generalization, the data set was randomly split into three parts:

The training set used to train the neural net and fit the linear model.

The validation set used to determine the performance of the neural network on unseen patterns during learning. Learning was stopped at the minimum value of the validation set error.

A test set was finally used to check the general performance of the neural net.

To deal with the overfitting problems that occur when there are not enough examples compared to the number of input variables in supervised learning, a regularization method called weight decay was implemented in the learning process. The application of regularization reduces the complexity of the network and makes the learning process easier. Moreover, with the same number of training samples, a reduction in weight improves the generalization ability of neural networks [16].

Thus, the search for the predictive models was carried out by feedforward backpropagation networks with a variable number of hidden units:

Input variables: 15 Output variables: 1 Hidden units: variable (5-20) Learning function: Backpropagation with Weight

Decay (learning parameter: 0.2; weight decay term: 0.0000005)

Update function: Topological Order Initialization function: Randomize Weights

(interval: [-0.3, 0.3])

Activation function: ( )xact exf −+= 11)(

Number of training cycles: 100000 Validation interval: 30 Training set (63%): 781 Validation set (32%): 397 Test set (5%): 62 The software used to train the neural networks was

SNNS (Stuttgart Neural Network Simulator) [17]. Figures 3 and 4 present the results of the best

neural network models for scorch time and cure time, respectively, as well as residuals plots for judging the normality of their distribution. The overall pattern of the residuals should be similar to the bell-shaped pattern observed when plotting a histogram of normally distributed data.

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Figure 3. Scorch time test results: measured output (solid line), predicted output (dashed line)

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Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)

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Figure 4. Cure time test results: measured output (solid line), predicted output (dashed line) and

residual plots for the best neural network.

The number of neurons in the hidden layer of the scorch time network was fifteen and the prediction had a 2.83% mean error. Although there is an eccentric residual, which can be interpreted as an outlier, the results for this cure characteristic are good.

On the other hand, the cure time neural network had four neurons in the hidden layer and the prediction had a 1.12% mean error. In this case, the residuals plots illustrate an approximately normal distribution, which suggest a good cure time model.

4. Conclusions It has been shown that it is possible to create reasonable models based on process data for the cure characteristics analyzed. However, there is potential for improving the current models: further work could be achieved by using, for example, robust neural networks, such as [18], in order to obtain more accurate predictions.

The models proposed in this paper can be integrated in the manufacturing system in order to obtain online information about the cure characteristics of the produced blends. With this

information, it would be possible to control the rubber compound processability and improve the scheduling and control functions. Therefore, the extrusion line would be optimised and the final quality of the extruded products would be improved thanks to better processing conditions.

In this work, we have shown the results obtained for the mixing process in a rubber extrusion line. However, this methodologies based on data can be applied to other manufacturing processes in order to predict parameters of special interest for the experts involve in the optimization of the process. For example, the mechanical properties of hot dip galvanizing coils are measured after coating the coil by using a destructive method. In [19] we present a system to estimate the mechanical properties of galvanized coils according to the chemical composition and manufacturing conditions in the annealing furnace. Acknowledgements This work has been partially funded with a research scholarship granted by the State Secretary of Education and Universities of the Ministry of Education, Culture and Sport.

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Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)

We will thank the support from the CEUTIC (Interreg IIIA), the national funded program DPI2004-07264-C02-01, the ‘II Plan Riojano de I+D’ for their partial funded of this work and for making possible the diffusion of results.

Also, we sincerely thank the constant support provided by Metzeler Automotive Profile Systems Iberica, S.A. References: [1] Hou, L., Nassehi, V., Evaluation of stress-

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[3] Freakley, P.K. and Fletcher, J.B., The single-rotor continuous mixing system. Rubber World, vol. 226, nº 4, 2002, pp. 28-31.

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[5] Zhang, A., Wang, L., Zhou, Y., A study on rheological properties of carbon black extended powdered SBR using a torque rheometer. Polymer Testing, 22, 2003, pp. 133-141.

[6] Ordieres, J., López, L.M., Bello, A., and Forcada, A., Intelligent methods helping the design of a manufacturing system for die extrusion rubbers. International Journal of Computer Integrated Manufacturing, vol. 16, nº 3, 2003, pp. 173-180.

[7] Wu, C.-Y., Hsu, Y.-C., Optimal shape design of an extrusion die using polynomial networks and genetic algorithms. The International Journal of Advanced Manufacturing Technology, vol. 19, nº 2, 2002, pp. 79-87.

[8] Sombatsompop, N., and Dangtangee, R., Effects of the actual diameters and diameter ratios of barrels and dies on the elastic swell and entrance pressure drop of natural rubber in capillary die flow. Journal of Applied Polymer Science, vol. 86, nº 7, 2002, pp. 1762-1772.

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[11] Merikoski, S., Laurikkala, M., Koivisto, H., An adaptive neuro-fuzzy inference system as a soft sensor for viscosity in rubber mixing process. In N. Mastorakis (ed), Advances in neural networks and applications (Greece: World Scientific and Engineering Society Press), 2001, pp. 287-291.

[12] Schwartz, G.A., Prediction of rheometric properties of compounds by using artificial neural networks. Rubber Chemistry and Technology, vol. 74, nº 1, 2001, pp. 116-123.

[13] Morton-Jones, D.H., Procesamiento de plásticos, Mexico: Limusa, 1993.

[14] Royo, J., Manual de Tecnología del Caucho, Madrid: Consorcio Nacional de Industriales del Caucho, 1989.

[15] Sammon, J.W., A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, vol. 18, nº 5, 1969, pp. 401-409.

[16] Haykin, S., Neural networks, a comprehensive foundation, New Jersey: Prentice Hall, 1998.

[17] Zell, A. et al., SNNS - Stuttgart Neural Network Simulator. User Manual, Version 4.2, University of Stuttgart, 1998.

[18] Pernía Espinoza, A.V., Ordieres Meré, J.B., Martínez de Pisón, F.J., González Marcos, A., TAO-robust backpropagation learning algorithm, Neural Networks, vol. 18, 205, pp. 191-204.

[19] Ordieres Meré, J.B., González Marcos, A., González, J.A., Lobato Rubio, V., Estimation of mechanical properties of steel strip in hot dip galvanising lines. Ironmaking and Steelmaking, vol. 31, nº 1, 2004, pp. 43-50.

NOTES (*) A. González Marcos works at the University of Leon, Department of de Electrical Engineering, Project Management Area.

Proceedings of the 8th WSEAS Int. Conference on Automatic Control, Modeling and Simulation, Prague, Czech Republic, March 12-14, 2006 (pp189-194)