The paper presents a production scheduling problem in a foundry equipped with two furnaces and one casting line, where the line is a bottleneck and furnaces, of the same capacity, work in parallel. The amount of produced castings may not exceed the capacity of the line and the furnaces, and their loads determine metal type from which the products are manufactured on the casting line. The purpose of planning is to create the processing order of metal production to prevent delays in the delivery of the ordered products to the customers. The problem is a mix of a lot-sizing and scheduling problems on two machines (the furnaces) run in parallel. The article gives a mathematical model that defines the optimization problem, and its relaxed version based on the concept of a rolling-horizon planning. The proposed approaches, i.e. commercial solver and Iterated Local Search (ILS) heuristic, were tested on a sample data and different problem sizes. The tests have shown that rolling horizon approach gives the best results for most problems, however, developed ILS algorithm gives better results for the largest problem instances with tight furnace capacity.
The paper presents the issue of production processes improvement in foundries in the area of finishing treatment of iron casts manufactured on automated foundry lines with vertical or horizontal mould division. Due to numerous factors which influence the efficiency of the processes, multi-criterion assessment tools were proposed in order to select the optimal solution for the assumed criteria. After determining the criteria weight using the Saaty method, a simulation experiment was designed and carried out which presents possible scenarios of casts finishing treatment operations. Basing on experiment reports from a computer model, particular solutions were evaluated using the Yager’s method. The evaluation of the experiment results was performed by experts who assessed different options according to each of the criteria adopted. After the establishment of the total standardized ratings by averaging the scores given by individual experts, the final decision was generated. Using the presented method, the best solution was chosen from among the analyzed scenarios.
The paper presents a practical example of improving quality and occupational safety on automated casting lines. Working conditions on the line of box moulding with horizontal mould split were analysed due to low degree of automation at the stage of cores or filters installation as well as spheroidizing mortar dosing. A simulation analysis was carried out, which was related to the grounds of introducing an automatic mortar dispenser to the mould. To carry out the research, a simulation model of a line in universal Arena software for modelling and simulation of manufacturing systems by Rockwell Software Inc. was created. A simulation experiment was carried out on a model in order to determine basic parameters of the working system. Organization and working conditions in other sections of the line were also analysed, paying particular attention to quality, ergonomics and occupational safety. Ergonomics analysis was carried out on manual cores installation workplace and filters installation workplace, and changes to these workplaces were suggested in order to eliminate actions being unnecessary and onerous for employees.
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.
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 datadriven 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.
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.
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.
For the die casting conditions of aluminium bronzes assumed based on the literature data, a thick-walled bush was cast, made of complex aluminium bronze (Cu-Al-Fe-Ni-Cr). After the cast was removed from the mould, cracks were observed inside it. In order to identify the stage in the technological production process at which, potentially, the formation of stresses damaging the continuity of the microstructure created in the cast was possible (hot cracking and/or cold cracking), a computer simulation was performed. The article presents the results of the computer simulation of the process of casting the material into the gravity die as well as solidifying and cooling of the cast in the shape of a thick-walled bush. The simulation was performed with the use of the MAGMA5 program and by application of the CuAl10Ni5,5Fe4,5 alloy from the MAGMA5 program database. The results were compared with the location of the defects identified in the actual cast. As a result of the simulation of the die-casting process of this bush, potential regions were identified where significant principal stresses accumulate, which can cause local hot and cold cracking. Until now, no research has been made of die-cast aluminium bronzes with a Cr addition. Correlating the results of the computer simulation validated by the analysis of the actual cast made it possible to clearly determine the critical regions in the cast exposed to cracking and point to the causes of its occurrence. Proposals of changes in the bush die casting process were elaborated, in order to avoid hot tearing and cold cracking. The article discusses the results of preliminary tests being a prologue to the optimization of the die-casting process parameters of complex aluminium bronze thick-walled bushs.
The aim of the paper was an attempt at applying the time-series analysis to the control of the melting process of grey cast iron in production conditions. The production data were collected in one of Polish foundries in the form of spectrometer printouts. The quality of the alloy was controlled by its chemical composition in about 0.5 hour time intervals. The procedure of preparation of the industrial data is presented, including OCR-based method of transformation to the electronic numerical format as well as generation of records related to particular weekdays. The computations for time-series analysis were made using the author’s own software having a wide range of capabilities, including detection of important periodicity in data as well as regression modeling of the residual data, i.e. the values obtained after subtraction of general trend, trend of variability amplitude and the periodical component. The most interesting results of the analysis include: significant 2-measurements periodicity of percentages of all components, significance 7-day periodicity of silicon content measured at the end of a day and the relatively good prediction accuracy obtained without modeling of residual data for various types of expected values. Some practical conclusions have been formulated, related to possible improvements in the melting process control procedures as well as more general tips concerning applications of time-series analysis in foundry production.
Mathematical description of alloys solidification in a macro scale can be formulated using the one domain method (fixed domain approach). The energy equation corresponding to this model contains the parameter called a substitute thermal capacity (STC). The analytical form of STC results from the assumption concerning the course of the function fS = fS (T) describing the changes of solid state volumetric fraction and the temperature at the point considered. Between border temperatures TS , TL the function fS changes from 1 to 0. In this paper the volumetric fraction fS (more precisely fL = 1- fS ) is found using the simple models of macrosegregation (the lever arm rule, the Scheil model). In this way one obtains the formulas determining the course of STC resulting from the certain physical considerations and this approach seems to be closer to the real course of thermal processes proceeding in domain of solidifying alloy.
Thin metal film subjected to a short-pulse laser heating is considered. The parabolic two-temperature model describing the temporal and spatial evolution of the lattice and electrons temperatures is discussed and the melting process of thin layer is taken into account. At the stage of numerical computations the finite difference method is used. In the final part of the paper the examples of computations are shown.
The paper presents a practical example of improvement of foundry production systems in terms of post-finishing of nodular iron castings produced in the conditions of bulk production for automotive industry. The attention was paid to high labour-intensive efforts, which are difficult to be subjected to mechanization and automation. The times of actions related to grinding processing of castings in three grinding positions connected with a belt conveyor were estimated with the use of a time study method. A bottleneck as well as limiting factors were specified in a system. A number of improvements were proposed, aimed at improving work organization on the castings postfinishing line. An analysis of work ergonomics at the workplace was made in order to eliminate unnecessary and onerous for the employee actions. A model of production system using the Arena software, on which a simulation experiment was conducted, was drawn up in order to visualize the analysed phenomena. The effects of the project were shown on graphs comparing times, costs, work ergonomics and overall efficiency of production equipment indicator.
Article present various forms of transfer of information available on the Internet. An attempt was made to show the possibility of such a selection of the knowledge sources that, taking into account user preferences, would arouse his interest, showing in parallel the intended substantive content. This commitment is shown in the context of the current assumptions of building a platform dedicated to support the needs of production processes in foundry and metallurgy.