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The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.

The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including data-driven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input - output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.

The paper undertakes an important topic of evaluation of effectiveness of SCADA (Supervisory Control and Data Acquisition) systems, used for monitoring and control of selected processing parameters of classic green sands used in foundry. Main focus was put on process studies of properties of so-called 1st generation molding sands in the respect of their preparation process. Possible methods of control of this processing are presented, with consideration of application of fresh raw materials, return sand (regenerate) and water. The studies conducted in one of European foundries were aimed at pointing out how much application of new, automated plant of sand processing incorporating the SCADA systems allows stabilizing results of measurement of selected sand parameters after its mixing. The studies concerned two comparative periods of time, before an implementation of the automated devices for green sands processing (ASMS - Automatic Sand Measurement System and MCM – Main Control Module) and after the implementation. Results of measurement of selected sand properties after implementation of the ASMS were also evaluated and compared with testing studies conducted periodically in laboratory.

Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors.

Simulation software can be used not only for checking the correctness of a particular design but also for finding rules which could be used in majority of future designs. In the present work the recommendations for optimal distance between a side feeder and a casting wall were formulated. The shrinkage problems with application of side feeders may arise from overheating of the moulding sand layer between casting wall and the feeder in case the neck is too short as well as formation of a hot spot at the junction of the neck and the casting. A large number of simulations using commercial software were carried out, in which the main independent variables were: the feeder’s neck length, type and geometry of the feeder, as well as geometry and material of the casting. It was found that the shrinkage defects do not appear for tubular castings, whereas for flat walled castings the neck length and the feeders’ geometry are important parameters to be set properly in order to avoid the shrinkage defects. The rules for optimal lengths were found using the Rough Sets Theory approach, separately for traditional and exothermic feeders.

Simulation software dedicated for design of casting processes is usually tested and calibrated by comparisons of shrinkage defects distribution predicted by the modelling with that observed in real castings produced in a given foundry. However, a large amount of expertise obtained from different foundries, including especially made experiments, is available from literature, in the form of recommendations for design of the rigging systems. This kind of information can be also used for assessment of the simulation predictions. In the present work two parameters used in the design of feeding systems are considered: feeding ranges in horizontal and vertical plates as well as efficiency (yield) of feeders of various shapes. The simulation tests were conducted using especially designed steel and aluminium castings with risers and a commercial FDM based software. It was found that the simulations cannot predict appearance of shrinkage porosity in horizontal and vertical plates of even cross-sections which would mean, that the feeding ranges are practically unlimited. The yield of all types of feeders obtained from the simulations appeared to be much higher than that reported in the literature. It can be concluded that the feeding flow modelling included in the tested software does not reflect phenomena responsible for the feeding processes in real castings properly. Further tests, with different types of software and more fundamental studies on the feeding process modelling would be desirable.

The purpose of this paper was to develop a methodology for diagnosing the causes of die-casting defects based on advanced modelling, to correctly diagnose and identify process parameters that have a significant impact on product defect generation, optimize the process parameters and rise the products' quality, thereby improving the manufacturing process efficiency. The industrial data used for modelling came from foundry being a leading manufacturer of the high-pressure die-casting production process of aluminum cylinder blocks for the world's leading automotive brands. The paper presents some aspects related to data analytics in the era of Industry 4.0. and Smart Factory concepts. The methodology includes computation tools for advanced data analysis and modelling, such as ANOVA (analysis of variance), ANN (artificial neural networks) both applied on the Statistica platform, then gradient and evolutionary optimization methods applied in MS Excel program's Solver add-in. The main features of the presented methodology are explained and presented in tables and illustrated with appropriate graphs. All opportunities and risks of implementing data-driven modelling systems in high-pressure die-casting processes have been considered. [ABSTRACT FROM AUTHOR]