Production processes at KGHM are complex and require from customers products of constantly higher quality at relatively lowest prices. Such situation results in an increase of the importance of optimisation of processes. As products and technologies change rapidly, technologists at the plant in Głogów have less time to achieve optimisation basing on own experiences. Analysing a particular process, we can e.g. detect occurring disturbances, find factors having an influence on quality problems, select optimal settings or compare various production procedures. Analysis of the course of production process is the basis of process optimisation. One optimisation in case of the process of decopperisation of flash slag can be a change of a technological additive to a less energy-consuming one, and its final result can be an improvement of the productivity index, a change of the relation between final effects and born expenditures, as well as optimisation of production costs.
Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. The presented investigations were aimed at the selection of the neural network able to predict the active bentonite content in the moulding sand on the basis of this sand properties such as: permeability, compactibility and the compressive strength. Then, the data of selected parameters of new moulding sand were set to selected artificial neural network models. This was made to test the universality of the model in relation to other moulding sands. An application of the Statistica program allowed to select automatically the type of network proper for the representation of dependencies occurring in between the proposed moulding sand parameters. The most advantageous conditions were obtained for the uni-directional multi-layer perception (MLP) network. Knowledge of the neural network sensitivity to individual moulding sand parameters, allowed to eliminate not essential ones.
Studies were conducted on a zinc coating produced on the surface of ductile iron grade EN-GJS-500-7 to determine the eutectic grain effect. For this purpose, castings with a wall thickness of 5 to 30 mm were made and the resulting structure was examined. To obtain a homogeneous metal matrix, samples were subjected to a ferritising annealing treatment. To enlarge the reaction surface, the top layer was removed from casting by machining. Then hot dip galvanising treatment was performed at 450°C to capture the kinetics of growth of the zinc coating (in the period from 60 to 600 seconds). Analysing the test results it was found that within the same time of hot dip galvanising, the differences in the resulting zinc coating thickness on samples taken from castings with different wall cross-sections were small but could, particularly for shorter times of treatment, reduce the continuity of the alloyed layer of the zinc coating.
Lean manufacturing [LM], quality management system and environmental management system are clear initiatives with a goal of improving effectiveness and efficiencies of organizations. Many organisations tackle lean philosophy, ISO standards individually but this kind of attempt do not focus on the synergy and the advantage from the potential collaboration. This paper aims to present the possibility of integration Lean Management concept with ISO management systems – Quality Management System [QMS] ISO 9001and Environmental Management System [EMS] ISO 14001 already implemented in the enterprises. The integration of these three concepts can be obtain due to improvement of main KPI’s defined in the organization. Based on critical research literature and participant observation presented as a case study (one of the author of the paper works as a consultant and is being implemented Lean Manufacturing concept in different organization since ten years) authors defined concept of integration of EMS and QMS (already implemented in the organization) with chosen Lean Management tools. Concept has been developed based on literature analysis and experience of the authors. Results and summary from concept implementation has been described in last chapter of the paper.
Conducting reliable and credible evaluation and statistical interpretation of empirical results related to the operation of production systems in foundries is for most managers complicated and labour-intensive. Additionally, in many cases, statistical evaluation is either ignored and considered a necessary evil, or is completely useless because of improper selection of methods and subsequent misinterpretation of the results. In this article, after discussing the key elements necessary for the proper selection of statistical methods, a wide spectrum of these methods has been presented, including regression analysis, uni- and multivariate correlation, one-way analysis of variance for factorial designs, and selected forecasting methods. Each statistical method has been illustrated with numerous examples related to the foundry practice.
The FMEA (Failure Mode and Effects Analysis) method consists in analysis of failure modes and evaluation of their effects based on determination of cause-effect relationships for formation of possible product or process defects. Identified irregularities which occur during the production process of piston castings for internal combustion engines were ordered according to their failure rates, and using Pareto-Lorenz analysis, their per cent and cumulated shares were determined. The assessments of risk of defects occurrence and their causes were carried out in ten-point scale of integers, while taking three following criteria into account: significance of effects of the defect occurrence (LPZ), defect occurrence probability (LPW) and detectability of the defect found (LPO). A product of these quantities constituted the risk score index connected with a failure occurrence (a so-called “priority number,” LPR). Based on the observations of the piston casting process and on the knowledge of production supervisors, a set of corrective actions was developed and the FMEA was carried out again. It was shown that the proposed improvements reduce the risk of occurrence of process failures significantly, translating into a decrease in defects and irregularities during the production of piston castings for internal combustion engines.
The present research was conducted on thin-walled castings with 5 mm wall thicknesses. This study addresses the effect of the influence of different master alloys, namely: (1) Al-5%Ti-1%B, (2) Al-5%Ti and (3) Al-3%B, respectively on the structure and the degree of undercooling (ΔTα = Tα-Tmin, where Tα - the equilibrium solidification temperature, Tmin - the minimum temperature at the beginning of α(Al) solidification) of an Al-Cu alloy. The process of fading has been investigated at different times spent on the refinement treatment ie. from 3, 20, 45 and 90 minutes respectively, from the dissolution of master alloys. A thermal analysis was performed (using a type-S thermocouple) to determine cooling curves. The degree of undercooling and recalescence were determined from cooling and solidification curves, whereas macrostructure characteristics were conducted based on a metallographic examination. The fading effect of the refinement of the primary structure is accompanied by a significant change in the number (dimension) of primary grains, which is strongly correlated to solidification parameters, determined by thermal analysis. In addition to that, the analysis of grain refinement stability has been shown with relation to different grain refinements and initial titanium concentration in Al-Cu base alloy. Finally, it has been shown that the refinement process of the primary structure is unstable and requires strict metallurgical control.
Achieving control of coating thickness in foundry moulds is needed in order to guarantee uniform properties of the mould but also to achieve control of drying time. Since drying time of water based coatings is heavily dependent on the amount of water present in the coating layer, a stable coating process is prerequisite for a stable drying process. In this study, we analyse the effect of different variables on the coating layer properties. We start by considering four critical variables identified in a previous study such as sand compaction, coating density, dipping time and gravity and then we add centre points to the original experimental plans to identify possible non-linear effects and variation in process stability. Finally, we investigate the relation between coating penetration (a variable that is relatively simple to measure in production) and other coating layer thickness properties (relevant for the drying process design). Correlations are found and equations are provided. In particular it is found that water thickness can be directly correlated to penetration with a simple linear equation and without the need to account for other variables.
In order to predict the distribution of shrinkage porosity in steel ingot efficiently and accurately, a criterion R√L and a method to obtain its threshold value were proposed. The criterion R√L was derived based on the solidification characteristics of steel ingot and pressure gradient in the mushy zone, in which the physical properties, the thermal parameters, the structure of the mushy zone and the secondary dendrite arm spacing were all taken into consideration. The threshold value of the criterion R√L was obtained with combination of numerical simulation of ingot solidification and total solidification shrinkage rate. Prediction of the shrinkage porosity in a 5.5 ton ingot of 2Cr13 steel with criterion R√L>0.21 m･℃1/2･s -3/2 agreed well with the results of experimental sectioning. Based on this criterion, optimization of the ingot was carried out by decreasing the height-to-diameter ratio and increasing the taper, which successfully eliminated the centreline porosity and further proved the applicability of this criterion.
The scope of this work focuses on the aspects of quality and safety assurance of the iron cast manufacturing processes. Special attention was given to the processes of quality control and after-machining of iron casts manufactured on automatic foundry lines. Due to low level of automation and huge work intensity at this stage of the process, a model area was established which underwent reorganization in accordance with the assumptions of the World Class Manufacturing (WCM). An analysis of work intensity was carried out and the costs were divided in order to identify operations with no value added, particularly at individual manufacturing departments. Also an analysis of ergonomics at work stations was carried out to eliminate activities that are uncomfortable and dangerous to the workers' health. Several solutions were proposed in terms of rationalization of work organization at iron cast after-machining work stations. The proposed solutions were assessed with the use of multi-criteria assessment tools and then the best variant was selected based on the assumed optimization criteria. The summary of the obtained results reflects benefits from implementation of the proposed solutions.
Non-metallic inclusions found in steel can affect its performance characteristics. Their impact depends not only on their quality, but also, among others, on their size and distribution in the steel volume. The literature mainly describes the results of tests on hard steels, particularly bearing steels. The amount of non-metallic inclusions found in steel with a medium carbon content melted under industrial conditions is rarely presented in the literature. The tested steel was melted in an electric arc furnace and then desulfurized and argonrefined. Seven typical industrial melts were analyzed, in which ca. 75% secondary raw materials were used. The amount of non-metallic inclusions was determined by optical and extraction methods. The test results are presented using stereometric indices. Inclusions are characterized by measuring ranges. The chemical composition of steel and contents of inclusions in every melts are presented. The results are shown in graphical form. The presented analysis of the tests results on the amount and size of non-metallic inclusions can be used to assess them operational strength and durability of steel melted and refined in the desulfurization and argon refining processes.
One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore, from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.
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.
The paper deals with problem of optimal used automatic workplace for HPDC technology - mainly from aspects of operations sequence, efficiency of work cycle and planning of using and servicing of HPDC casting machine. Presented are possible ways to analyse automatic units for HPDC. The experimental part was focused on the rationalization of the current work cycle time for die casting of aluminium alloy. The working place was described in detail in the project. The measurements were carried out in detail with the help of charts and graphs mapped cycle of casting workplace. Other parameters and settings have been identified. The proposals for improvements were made after the first measurements and these improvements were subsequently verified. The main actions were mainly software modifications of casting center. It is for the reason that today's sophisticated workplaces have the option of a relatively wide range of modifications without any physical harm to machines themselves. It is possible to change settings or unlock some unsatisfactory parameters.
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 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.
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.
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.