داده کاوی برای کنترل کیفیت: تشخیص با مته سوراخ کردن در روند حفاری

Drilling process is one of the most important operations in aeronautic industry. It is performed on the wings of the aeroplanes and its main problem lies with the burr generation. At present moment, there is a visual inspection and manual burr elimination task subsequent to the drilling and previous to the riveting to ensure the quality of the product. These operations increase the cost and the resources required during the process. The article shows the use of data mining techniques to obtain a reliable model to detect the generation of burr during high speed drilling in dry conditions on aluminium Al 7075-T6. It makes possible to eliminate the unproductive operations in order to optimize the process and reduce economic cost. Furthermore, this model should be able to be implemented later in a monitoring system to detect automatically and on-line when the generated burr is out of tolerance limits or not. The article explains the whole process of data analysis from the data preparation to the evaluation and selection of the final model.

Nowadays, practically all fields of industrial activities are moving towards automation of their processes. This automation should ensure the quality of the product while minimizing manufacturing cost and optimizing resources. Drilling is the most important operation for aeronautic industry because it implies a high economic cost. This cost is consequence of the visual inspection and burr elimination tasks. They are non-productive operations, carried out subsequent to drilling and they should be eliminated or minimized to the maximum extent possible. A small or medium size aeroplane has more than 250,000 holes to be inspected and if there is burr, it must be eliminated. It is necessary to eliminate this manual process and change it for a monitoring system able to detect automatically and on-line when there is a burr. The aim of this article is to obtain a model that can be implemented into the machine to predict the burr generation during the drilling process. The technological centre ‘C.I.C MARGUNE’, a Cooperative Research Centre for High Performance Manufacturing, patented a monitoring method, experimentally adjusted, able to detect if the size of the generated burr is between aeronautical limits or not (Peña, Aramendi, & Rivero, 2007). This method consists of a conventional mathematical model to burr detection based on the parameters extracted from the whole internal signal of the machine and its percentage of correct classification was 92%. Nevertheless, this model could not be implemented into the machine and there is currently no monitoring method for burr detection, so most of the research of this article was focused on obtaining a model that could be implemented in a monitoring system to predict automatically the burr generation during the drilling process. This model was derived from a process that extracts useful and understandable knowledge previously unknown from a set of experiments. Fig. 1 shows the communication among different phases of the knowledge extraction process.The storage, the organization and the information retrieval have been automated thanks to the data base systems and the availability of a huge quantity of information. There are some analytic techniques based on statistics that have been used to analyze this information, but they are cryptic for people who are not very experience with it. Data mining as explained in Michalski, Bratko, and Kubat (1998) and Kaelbling and Cohn (2003) is a multidisciplinary field easy to put into practice and it combines several techniques such as statistics, machine learning, decision-making support systems, and visualization, in order to extract knowledge from a data set. Each phase of the process includes a set of these techniques. The process is iterative and interactive. It is iterative because the output of any phase may turn back to the previous steps and because some iterations are necessary to extract high-quality knowledge. It needs to explore various models to find the most useful one to solve the problem. In the search of a good model it may be possible to return to previous phases and make changes in the data. Even the problem definition could be modified to give it a different approach. Moreover, the process is interactive because the expert in the problem domain should help in data preparation and evaluation. The evaluation is one of the most important phases in this process and it needs to have well-defined training and validation stages to decide which model offers better performance and accuracy. The idea is to estimate (train) the model with a subset of the dataset (training dataset) and then validate it with the rest of the dataset (test dataset). The main contribution of this article is to explain the usefulness and benefits of the data mining techniques to obtain a robust, accurate and reliable model that can be implemented into a monitoring system and used to predict burr generation during the drilling process as shown in Fig. 2.The rest of the article is organized as follows. Section 2 presents a review of related and previous works in the use of other data driven models in machining, and the position of the present work in the context of previous ones. Section 3 shows the experimental dataset, characteristics of the process, and data selection and preparation. It defines a clean and reliable dataset. Section 4 briefly describes the concept of machine learning. Next, Section 5 explains the results of the analysis and evaluation in order to obtain the best final model for detecting burr generation: application of machine learning algorithms without selection of variables, then with selection and combination of algorithms, and finally, makes a change of strategy to eliminate false negatives (cases in which the model predicts no burr but burr is generated. And finally, Section 6 closes the article with the most important conclusions.

Data mining is a multidisciplinary field that can be applied in many activity fields to data study and analysis. Sometimes the industry relies on a lot of data from which to extract previously unknown information. This information brings many benefits as shown in the article. From a simple design of experiments and data mining techniques, especially the selection of variables and machine learning algorithms, a model for detection of burr during the drilling process was developed. This model provides better accuracy than the previously used mathematical model and in addition it has certain advantages. Firstly, the model is based on the internal signal of the machine and certain parameters of the conditions of the process, so the implementation would be easier and without external sensors. It is able to be implemented later in a monitoring system to detect automatically and on-line when the burr occurs during the drilling process. And secondly, it provides information about which parameters of the drilling process define if there is occurrence of burr or not. Regarding the results, almost all developed models provide a higher accuracy than the current mathematical model. Moreover, for the final model based on Naïve Bayes the accuracy was 95% and standard deviation equal to 0, which means it is a very stable model. At this point, it must also be mentioned that in most of the cases in which the model performs a bad prediction, it is because the burr is very close to the aeronautical limits (127 μm), which makes more difficult to distinguish which is the corresponding class. Other aspect to take into account is the fact that the prediction error has not the same significance whether the drilled hole has burr or not. The main error is produced when a sample is classified as acceptable but, actually, it has not acceptable limits. For the detection of these cases, the model has been improved, reducing considerably the number of inspections and operations to remove burr. These techniques could be applied to other industrial processes such as moulding and chip removal processes, potentially obtaining good results as in the previous case and making possible the automation of some tasks which are made by hand at present. They are relatively new and novel techniques in certain industrial sectors such as metal, aeronautics or chemistry, and they should be investigated as a possible solution to some of their current problems. Further studies could be undertaken by using Bayesian network, a model representation for reasoning under uncertainty. Formally, its representation is a directed acyclic graph (DAG) where each node represents a random variable and the edges represent (often causal) dependence relations between them (Jensen, 1996). Each variable represents a unique event or hypothesis, with a finite set of mutually exclusive states: X = {X1, … , Xn}, and there must be a state for each possible value and its conditional probabilities. It uses Bayes’ theorem to upgrade the probabilities, and taking into account the good results obtained by the Naïve Bayes algorithm, Bayesian network would be a model to evaluate. Moreover, it could be possible to increase the number of predictive variables, such as the type of material, and the geometry of the bit. Another possibility would be to take into account the wear on the tool. At present, when the operator becomes aware of the worn condition of the tool, it should be changed. It is clear that many other aspects of the drilling process can be usefully explored.