A Data Pre-Processing Tool for Neural Networks (DPTNN) Use in A Moulding Injection Machine 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 This paper presents a Data pre-Processing Tool for Neural Networks (DPTNN) use in a fault diagnosis system in the plastics industry. When properly understood and properly applied neural network technologies consistently build effective models from data. However, this task requires substantial investment in pre-processing and training. The developed tool allows to analyse and transform the data, to pick train and test sets and to select the right inputs to be used on a neural network. On-line measurements of key processing variables from an injection moulding machine were obtained from insite industry experiments. These were conducted to collect process data that was used to evaluate the ability of artificial neural networks to model the relationships among process variables changes and the moulded part quality. The quality and quantity of process data will have a significant impact on the performance of the diagnosis system. Future work will extend the embedded functions to create a network, and to optimise neural network models. Keywords : Neural Networks, Fault Diagnosis, Injection moulding, Data Analysis.
1. Introduction Neural networks are adaptive systems that have learning properties, that is, they adapt their internal parameters in order to satisfy constraints imposed by a training algorithm and by the input and output training data. In order to extract all the important features considerable attention must be paid to the form and characteristics of the data presented to the network at the input and output stages [Jackson97].
The neural computing approach can offer relatively simple solutions to complex pattern recognition and classification problems. Basically it consists of (i) gathering the data samples, (ii) choose and prepare the training and test sets from the samples, (iii) select an appropriate network topology, (iv) train the network until the required results are achieved and (v) test and validate the network. It has been described as a black-box approach since internal representations and mechanics of the neural network need not to be known, or understood, to find a plausible solution. This simplistic view, however, obscures the inner neural network complexities which contribute to the development of many successful applications. Thus very important to that, is the manner data is presented to the network, the mechanisms through which the real data is transformed into input vectors such that all relevant information is passed by to the network. It is argued in [Jackson97] that data representations are more critical than neural network topologies since the inherent flexibility in neural network learning can accommodate non optimal selection of weights and nodes whereas inappropriately structured data may lead to an incorrect learning map. Similarly, representations used at output of a neural network play an incisive role in the training process. This problematic is crucial in the framework of an industrial process, such as, the injection moulding machine with a complex dynamics and non-linear relationships among the process variables and the moulded part quality. The on-line measurements contribute usefully for a successful fault diagnosis if the data is properly prepared and transformed prior to be used in a neural network system model.
In this work a computational tool Data pre-Processing Tool for Neural Networks (DPTNN) use in a fault diagnosis system in an injection moulding machine is developed. It is an auxiliary tool for further neural network modelling of the industrial process. DPTNN provides several functions, such as, data transformation which includes remotion of outliers, normalisation and scaling, statistics reports, graphics analysis, generation of data training, test and cross-validation sets. It emphasises the importance that data preparation plays in developing successful neural network systems. The tool will be extended to embed several network architectures, selection of topologies and learning algorithms. The paper is organised as follows. In section two the injection moulding process is briefly described. Section three describes experimental work carried out on the factory. Section four presents the tool for data preparation and pre-processing (DPTNN) prior to its use in a neural computing approach to the injection moulding process. Finally, in section five, some conclusions will be taken and future work addressed.
plastic parts. A more detailed description of this process can be found in [Lopes99].
Figure 1 – Injection of material into the mould.
3. Working Data In order to collect the necessary data for neural network training some experiments were carried out at an industrial site during 126 production cycles. In each cycle the left and right car parts (Figure 2) 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.
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 (see Figure 1) 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
Figure 2 – Car parts produced by a DEMAG D 325 NCIII machine. To have an idea of the kind of data needed, Table 1 shows the process setups, process variables and the index quality values obtained.
Table 1 – Setups, process and index quality variables. Setups
Process
Quality
Plasticizing stroke
Cycle time
Mean 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. Injection Pressure Cooling Time Cushion Nozzle temperature
Flash
4. DPTNN Functions Data pre-processing is a fundamental key to successfully construct an artificial neural network. In this stage data should be analysed and treated, in order not only to select the proper inputs and outputs of the network, but also to build consistent training and test data sets.
Given the importance of data pre-processing a computational tool was developed for this purpose. The program Data pre-Processing Tool for Neural Networks (DPTNN) (see Figure 3) provides several mechanisms for data analysis.
Figure 3 – Data pre-processing tool for neural networks working screen. DPTNN provides several functions, such as, Data Transformation which includes elimination of outliers, normalisation and scaling, Data statistics, Data Graph Analysis, and Data Selection which enables the generation of data training, test and cross-validation sets. It emphasises the importance that data preparation plays in developing successful neural network systems.
4.1. Data Transformation Data outliers, when found, should be removed or replaced. Its presence jeopardises neural network proper learning. Additionally a network trained with outliers will have an unpredictable behaviour when in the presence of real data. Outliers came in two “flavours”, the obvious and the non obvious [Yong98]. Obvious outliers can be identified using prior knowledge. For instance if one knows that a certain variable must lie within some values then any value outside that range can be treated as an outlier. Non obvious outliers are
harder to find, knowledge of the problem being usually required. For example, in the injection moulding process described in section 2, the cycle time must be greater than the sum between the metering time and the injection time. When this does not happen we are certainly in the presence of an outlier. One of the functions provided by DPTNN allows to identify and treat obvious outliers (see Figure 4). Although it may not find all, it could save precious time by quickly identifying the majority of them. Regarding this, lack of knowledge may lead to situations were good data is treated as outliers or outliers are treated as good data. As an example, Figure 4 shows a situation where an outlier was found. Without process knowledge one is tempted to treat that data as an outlier. However if one knows that the cycle time includes production stops, one would associate that value to a production stop and therefore consider it as a normal value.
Figure 4 – Detection and treatment of outliers using DPTNN. Concerning the data in the underlying experiment, six outliers were found, regarding the “screw rotation speed”. Removing all the lines where outliers were found would result in the loss of data associated with the only warped part. The solution adopted was to replace all the outliers by the mean value of their adjacent lines. Data sets are often disturbed by problems of noise, bias and large variations in the dynamic range. This may turn it difficult to extract the important features of a given problem. There are several general data processing algorithms which remove the undesired variances and enhance the information content on data. One technique is normalisation which typically removes redundant information from a data set, compacting it or making it invariant to one or more features. The principle of normalisation is to reduce a vector (or data set) to a standard unit length, usually 1. The length of the vector is computed and each vector component is divide by its length. A vector or data set can be normalised across many different dimensions, and with respect to many different statistical measures such as the mean or variance [Wasserman93]. Furthermore it is necessary to re-scale all the inputs (normally between 0 and 1 or between –1 and 1). This should be done so that initially all the input variables have the same importance. During the learning phase the network will deliberately alter the importance of variables, by changing the strengths of the connections between the input layer and the hidden layer.
4.2. Data Statistics and Graph Analysis A neural network can be seen as a complex non-linear function that maps a given pattern of inputs into a given pattern of outputs. This means that the outputs must be related one way or another with the inputs, otherwise a neural network can not learn the underlying mapping function. In this phase correlation analysis may be handy. DPTNN allows not only to calculate covariance and correlation coefficients, but also to create several types of graphics between any variables (see Figure 3). These functions can be helpful when deciding about which ANN inputs and outputs should be chosen. The profile of variables may be analysed with the tool which allows several types of graphs. Figure 3 shows the corresponding graphs of the injection time and cushion with regards to the sample obtained experimentally.
4.3. Data Selection The knowledge acquired by the neural network is learnt from the training set. In this sense the quality of the knowledge owned by the neural network depends on a great extent on the quality and quantity of the training data set. Finally the data set must be divided in two sets, a training set and a test set. The training set will be used to train the ANN, whereas the test set will be used to test its capabilities of generalisation. DPTNN generates these data sets as a simple task (see Figure 5), allowing at the same time to rescale the variables between any chosen values. Note that rescaling can be done at any time and not necessarily when training and test set are built.
Figure 5 – Generation of training and test data sets using DPTNN.
5. Conclusions
References
In order to successfully create an ANN capable of diagnosing bad parts in the injection moulding process, data from both normal and faulty conditions must be obtained. Furthermore the data should be analysed and pre-processed before used in a neural computing approach. The pre-processing stage (data transformation, data analysis, data selection) becomes a fundamental key to build such a system.
[Jackson97] Thomas O. Jackson, “Data Input and Output Representations”, Handbook of Neural Computation, Eds. Emile Fisher and Russell Beale, IOP Publishing and Oxford University Press, 1997.
In this paper a computational tool (DPTNN) that simplifies greatly the task of a neural network solution for the case industry study is presented and described. It complements an on-line monitoring and diagnosis system for fault detection in an injection moulding machine. The tool will be extended to embed several network architectures, selection of topologies and learning algorithms. It is planned to develop two more functions Variable Selection and Network Training with the goal to select the minimal set of relevant variables which maximise overall performance. Thus the features of DPTNN provide all the building blocks necessary for system integration and embedding adaptive neural solutions within an industrial framework.
[Lopes99] Noel Lopes, Bernardete Ribeiro, “Part Quality Prediction in an Injection Moulding Process Using Neural Networks”, in proceedings of WMC, ISM, 1999. [Wasserman93] P. D. Wasserman, “Advanced Methods in Neural Computing”, New York: Van Nostrand Reinhold, 1993. [Yong98] Yong-Zai Lu., “Industrial Intelligent Control : Fundamentals and Applications”, Marcel Dekker, John Wiley & Sons Ltd, 1996.
