A. Kochański

Prediction of ductile cast iron quality by artificial neural networks

Abstract The prediction of ductile cast iron properties by an analysis of physical and chemical phenomena occurring during melting process is discussed. Characterisation of the problem from the standpoint of artificial neural network (ANN) modelling is presented and the network input parameters are defined, based on their significance and availability in industrial practice. A large number of different networks have been constructed and trained using about 700 melts results recorded in a typical foundry. A comprehensive analysis of the prediction errors of tensile strength, elongation and hardness made by the networks is presented. It is concluded that ANNs can be used as a valuable tool for the on-line production control in the melt shops.

Comparison of data mining tools for significance analysis of process parameters in applications to process fault diagnosis

This paper presents an evaluation of various methodologies used to determine relative significances of input variables in data-driven models. Significance analysis applied to manufacturing process parameters can be a useful tool in fault diagnosis for various types of manufacturing processes. It can also be applied to building models that are used in process control. The relative significances of input variables can be determined by various data mining methods, including relatively simple statistical procedures as well as more advanced machine learning systems. Several methodologies suitable for carrying out classification tasks which are characteristic of fault diagnosis were evaluated and compared from the viewpoint of their accuracy, robustness of results and applicability. Two types of testing data were used: synthetic data with assumed dependencies and real data obtained from the foundry industry. The simple statistical method based on contingency tables revealed the best overall performance, whereas advanced machine learning models, such as ANNs and SVMs, appeared to be of less value.

Knowledge Discovery and Analysis in Manufacturing

ABSTRACT The quality and reliability requirements for next-generation manufacturing are reviewed, and current approaches are cited. The potential for augmenting current quality/reliability technology is described, and characteristics of potential future directions are postulated. Methods based on knowledge discovery and analysis in manufacturing (KDAM) are reviewed, and related successful applications in business and social fields are discussed. Typical KDAM applications are noted, along with general functions and specific KDAM-related technologies. A systematic knowledge discovery process model is reviewed, and examples of current work are given, including description of successful applications of KDAM to creation of rules for optimizing gas porosity in sand casting molds. Finally, directions in KDAM technology and associated research requirements are described, and comments related to application and acceptance of KDAM are provided.

Knowledge in Imperfect Data

Data bases collecting a huge amount of information pertaining to real-world processes, for example industrial ones, contain a significant number of data which are imprecise, mutually incoherent, and frequently even contradictory. It is often the case that data bases of this kind often lack important information. All available means and resources may and should be used to eliminate or at least minimize such problems at the stage of data collection. It should be emphasized, however, that the character of industrial data bases, as well as the ways in which such bases are created and the data are collected, preclude the elimination of all errors. It is, therefore, a necessity to find and develop methods for eliminating errors from already-existing data bases or for reducing their influence on the accuracy of analyses or hypotheses proposed with the application of these data bases. There are at least three main reasons for data preparation: (a) the possibility of using the data for modeling, (b) modeling acceleration, and (c) an increase in the accuracy of the model. An additional motivation for data preparation is that it offers a possibility of arriving at a deeper understanding of the process under modeling, including the understanding of the significance of its most important parameters.

Applications of Data Mining to Diagnosis and Control of Manufacturing Processes

In the majority of manufacturing companies large amounts of data are collected and stored, related to designs, products, equipment, materials, manufacturing processes etc. Utilization of that data for the improvement of product quality and lowering manufacturing costs requires extraction of knowledge from the data, in the form of conclusions, rules, relationships and procedures. Consequently, a rapidly growing interest in DM applications in manufacturing organizations, including the development of complex DM systems, can be observed in recent years (Chen et al. 2004; Chen et al. 2005; Dagli & Lee, 2001; Hur et al., 2006; Malh & Krikler, 2007; Tsang et al., 2007). A comprehensive and insightful characterization of the problems in manufacturing enterprises, as well as the potential benefits from the application of data mining (DM) in this area was presented in (Shahbaz et al., 2006). Examples and general characteristics of problems related to the usage of data mining techniques and systems in a manufacturing environment can be found in several review papers (Harding et al., 2006; Kusiak, 2006; Wang, 2007). Application of DM techniques can bring valuable information, both for designing new processes and for control of currently running ones. Designing the processes and tooling can be assisted by varied computer tools, including simulation software, expert systems based on knowledge acquired from human experts, as well as the knowledge extracted semi automatically by DM methods. The proper choice of the manufacturing process version and its parameters allows to reduce the number of necessary corrections resulting from simulation and/or floor tests. The knowledge obtained by DM methods can significantly contribute to the right decision making and optimum settings of the process parameters. In the design phase two main forms of knowledge may be particularly useful: the decision logic rules in the form: ‘IF (conditions) THEN (decision class)’ and the regression–type relationships. Although the latter have been widely utilized before the emergence of DM methods (e.g. in the form of empirical formulas) and the rules created by the human experts were also in use, the computational intelligence (CI) methods (learning systems) have remarkably enhanced possibilities of the knowledge extraction and its quality. For the manufacturing process control many varied methods are used, ranging from paper Statistical Process Control (SPC) charts to automated closed loop systems. In spite of the