Part Quality Prediction in an Injection Moulding Process Using Neural Networks Noel Lopes†‡, Bernardete Ribeiro†
[email protected],
[email protected] ‡
Institute Polytechnic of Guarda –Department of Engineering Informatics Av. Dr. Francisco Sá Carneiro nº 50, P-6300 Guarda, Portugal Tel: (+351) 71 220111 Fax : +351 71 222690 †
CISUC – Department of Engineering Informatics DEI - FCTUC, Polo II, University of Coimbra P-3030 Coimbra, Portugal Tel: (+351) 39 790000 Fax: (+351) 39 701266
Abstract Competition is nowadays growing in every industrial field and the plastic industry is no exception. The superior quality demands, impose the development of intelligent systems able to cope with the defects on the parts produced, by avoiding them and correcting the process parameters. Fault detection and diagnosis is an essential component for the construction of such systems. This paper presents a first step to build a fault detection and diagnosis system for an injection moulding process. The basis for the approach takes two artificial neural networks, trained with the back-propagation algorithm for detecting part defects and monitoring a quality variable. The data was collected from the work carried out in an industrial site. Results show that the neural networks are able to embed the non-linear relationships between the process variables and the quality part variables. The strategy has a number of potential advantages for on-line quality prediction of part quality in injection moulding. Keywords : Neural Networks, Fault Diagnosis, Injection moulding.
1. Introduction As competition grows in plastics industry, companies see themselves forced to increase product quality and reduce production costs in order to survive. Thus there must be constant innovation and investment. Nowadays injection moulding its the most common process used to produce plastics. It can guarantee the production of a large amount of three-dimensional complex parts, but is characterised by extremely complex dynamics. Therefore it is very difficult to understand and predict the quality of the final parts.
The production of parts with defects should always be avoided, but above all the delivery of bad parts to the client should never occur, as it could damage seriously the image of the company and lead to unpredictable costs. Although statistical quality tests can be used, they increase production costs and can not guarantee the detection of all the defective parts. One way to achieve this is to built an online diagnosis and monitoring system that is able to minimise false alarms as well as to detect all parts with defects. Several methods have been developed for online diagnosing and detection of faults, such as the expert systems and the mathematical model based systems [Gertler98]. Both require the system to be well known [Watanabe89] [Hoskins91] [Joseph91], which is not always the case, especially when the system is highly non-linear and has extremely complex dynamics. Errors in the mathematical model as well as lack or mistaken knowledge in the expert system database can lead to false alarms or even worse to prevent defects from being detected when they occur. Additionally, the design of such systems is time-consuming [Hoskins91], making them economically unattractive. Statistical methods on the other hand, although performing well on linear data they can hardly be used in non-linear data [Joseph91]. Artificial Neural Networks (ANN) require little or no a priori knowledge [Chen99] of the system and have been successfully applied to a wide range of problems including fault diagnosis of chemical processes [Leonard91]. Its inherent parallel nature makes them fault tolerant systems that can easily learn complex non linear relations and successfully extrapolate or interpolate, when in the presence of never seen data. Additionally their ability to filter and tolerate noise is attractive and suitable for online robust process diagnosis.
The paper is organised as follows. In section two the injection moulding process is described. Section three deals with data collection and pre-processing. In section four a short description of ANN is introduced. In section five two ANN are presented, one capable of detect part defects and another capable of monitoring a quality variable. Results accomplished by this networks will be discussed. Finally, in section six, a summary of the conclusions is indicated and further work addressed.
2. Process Description Basically plastic injection moulding is a process where a solid thermoplastic material is heated until it reaches a state of fluidity, in other words until is melted. It is then injected under pressure into a mould where it is cooled until he reaches the solid state once again. This way the thermoplastic will duplicate all the details of the mould, forming complex three-dimensional plastic parts. At the beginning of each production cycle there should be enough quantity of granules (raw material) in the feeder. Depending on the kind of thermoplastic used, the humidity and temperature of the granules may affect the quality characteristics of the parts produced. Each cycle begins with the admission of granules. Simultaneously the screw inside the barrel starts rotating and moving back as shown in Figure 1. The effort exerted by the screw is designated by back pressure. Altogether, the rotation of the screw, the friction of the granules in the walls of the barrel and the heating elements placed outside it are responsible for melting the thermoplastic granules.
Figure 3 – Non returning valves. When the mould is completely full, pressure is maintained for an extra period of time, not only in order to avoid reflux of material but also to maintain the quality standards of the parts. At this time a small amount of extra material is squeezed into the mould. This compensates for the shrinkage of material as it cools. This pressure is called holding or packing pressure. When the material inside the mould reaches the required temperature the clamping unit opens the mould and the part is extracted. In order to accelerate this phase of the process the mould has cooling channels. During this period new material is fed into barrel.
3. Experimental Work This work was carried out at an industrial site where the data necessary to train a neural network was collected. It was obtained from 126 production cycles. In each cycle the left and right car parts (Figure 4) produced by a DEMAG D 325 NCIII machine were measured and analysed. Information of measures and defects found was then synchronised with the setups and the process variables.
Figure 1 – Admission of raw material into the injection machine.
Figure 4 – Car parts produced by a DEMAG D 325 NCIII machine.
When the barrel contains enough melted material to fulfil the mould, the screw goes forward, injecting the material into the mould cavity (Figure 2). Non return valves (Figure 3) guarantee that the material goes only forward. During this period of time the clamping unit is responsible for maintaining the mould tightly closed.
Table 1 shows the process setups, process variables and the index quality values obtained. As one sees only a few of the process variables could be obtained, being not possible to obtain either pressures or temperatures, which have a significant impact on the final quality of parts. As a consequence this set of data is far from being perfect, but it allows to build a first diagnostic system using artificial neural networks.
Figure 2 – Injection of material into the mould.
Table 1 – Setups, process and index quality variables. Setups Process Quality Plasticizing stroke Cycle time Measurement deviations Holding Pressure Metering time Spot marks Injection velocity Injection time Unfilled parts Metering stroke Cushion Warped parts Mould opening speed Screw Rotation Speed Burn marks R.P.M. Flash Injection Pressure Cooling Time Cushion Nozzle temperature Data was then pre-processed using a tool “Data preProcessing Tool for Neural Networks (DPTNN)”. Detailed information on data collection and preprocessing for the experiment related here can be found in [Lopes99].
4. Artificial Neural Networks An ANN can be viewed as a black-box that applies same kind of transformation (most likely non-linear) to a given input data in order to obtain same useful outputs. In this sense an ANN can be described as a complex non-linear function capable of transforming data from a N-dimensional space into a M-dimensional space. Essentially ANN are constituted by neurons usually organised in layers and by connections. Each connection is established between two neurons or between an input and a neuron and as associated a weight value. Figure 5 shows a typical ANN. Inputs are represented by squares, neurons by circles and connections by lines.
Figure 6 – Representation of an artificial neuron. The bias, W0j, can be represented by an extra connection whose input value is always 1. The neuron output is obtained by applying a function (usually non-linear) to its activation value. The type of function depends not only on the network topology, but also on the problem. The most common functions are the sigmoid, the Gaussian and the hyperbolic tangent. Proper function selection may enhance greatly the network performance. Several ANN topologies were developed, along with different training methods. Depending on the topology and training methods used, learning can be supervised or unsupervised. In both cases normally learning means adjust the weights of connections. For the case of supervised learning the objective is to minimise the sum square error between ANN outputs and the desired outputs. In order to achieve that, connection weights are updated during train. Detailed information about the foundations of ANN can be found in [Simpson92].
5. Neural Network Injection Moulding Diagnosis System Figure 5 – Typical artificial neural network. Each neuron receives inputs through its input connections (see Figure 6). The neuron multiplies each input by the corresponding connection weight. Results are then summed up with a bias (little triangles in Figure 5) in order to obtain the neuron activation.
At this point one aims at the design of a neural network capable of diagnosing part defects. Such a network should have as inputs the process variables and as outputs the corresponding quality part variables. It is impossible however to have all the quality variables as output, because as it was stated in section three we do not dispose of process variables which have significant impact on the final quality of parts.
The networks presented here were build using QwikNet [QwikNet]. The first, a feed-forward with eight inputs, three outputs and five neurons in the hidden layer was trained with the back-propagation algorithm (Figure 7), with a learning rate of 0.1 and a momentum of 0.7. The activation function was the hyperbolic tangent. It took
only 1000 epochs to train the network, using 101 patterns of a total of 126 (80%). The other 20% (25) were used to test the network. Notice that 10% of noise was added to the network during train. The addition of noise during train makes the network less sensitive to noise and improves its capacity of generalisation.
Figure 7 – Neural network for prediction of quality parts. The low magnitude of training and test errors (see Figure 8), indicates that the neural network has learn to
properly diagnose the defects presented in the training data set.
Figure 8 – RMS error for training and test sets versus training time. Although the above network gives information about part measures, it is only possible to know whether they lie within the standard range. It would be very interesting for operators or even for controller systems to have an estimate of how much the measures of a part
deviate from the standard ones. This would make possible process corrections before non-conforming parts had been produced. In order to achieve this goal a second neural network was designed (see Figure 9).
Figure 9 - Neural network for the predicting the deviation from standard measures. The network, with the same inputs as above and only one output (deviation from standard measures), was also trained with the back-propagation algorithm, with a learning rate of 0.1 and a momentum of 0.7. It took only 6000 epochs to train the network, using 95 patterns of a total of 126 (75%). The other 25% (31) were used to test the network. No noise was added to the network, since measures deviation have already noise (they were taken
manually and the machines were not properly calibrated after each measurement). Figure 10 shows the results. Note that the ANN could be trained further and the training error would continue to decrease, but the opposite would happen with the testing error. In this case the network would start to overfitt the training data.
Figure 10 - RMS error for training and test sets versus training time. Although the network presents relatively large errors, its output is still valuable. Figure 11 shows the network
output for the test data and Figure 12 shows the network output for the training data.
Figure 11 - Neural Network outputs versus test data.
Figure 12 - Neural Network outputs versus training data.
6. Conclusions An approach based on neural networks was developed for part quality prediction on an injection moulding process. The data for neural network training and test was obtained from experimental work carried out at an industrial site. The results shown demonstrate that even with less important variables than desired, an ANN system to diagnose faults can be successfully built. Further work will involve the design of neural networks using other architectures such as, for example, the radial basis function neural network (RBFNN). A study and comparison of several topologies could investigate which architecture performs better when several crucial variables are missing.
Further more a ANN can be build in order to relate setups and process variables. Such a network would have as inputs the setups and current process variables and would produce as outputs the process variables for the next production cycle. Then using the networks constructed here, or improved ones, the quality characteristics of the next production cycles could be predicted. This would allow to change setups accordingly preventing defects from occurring.
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