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.

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.

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.

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.

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.

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.

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.

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.

Maintenance of process plants requires application of good maintenance practice due to

a great level of complexity. From a plant maintenance point of view, the most significant activity

is turnaround, an activity carried out through project task with long planning process

period and very short execution period, which makes it one of the most complex projects

of maintenance in general. It is exactly this kind of maintenance that is based on multidisciplinarity

which has to be implemented through the system of quality management on all

levels of maintenance management. This paper defines the most significant factors determining

the process of turnaround projects quality management and its efficiency. Such relation

is observed through moderating influence of complexity on process management efficiency

in the turnaround project. The empirical research was conducted based on the survey of

turnaround project participants in five refineries in Croatia, Italy, Slovakia and Hungary.

For exploring the influence of research variables testing of the target relation is carried out

by applying logistical regression. Research results confirm the significance of complexity as

variable that significantly contributes to the project performance through the moderating

influence on success of the project, as well as the influence of an efficient management on

a plant turnaround project key results. Beside theoretical indications, practical implications

that arise from this research study mainly refers to management process of the industrial

plant maintenance project.

This study presents a customized root cause analysis approach to investigate the reasons,

provide improvements measures for the cost overruns, and schedule slippage in papermachine-

building projects. The proposed approach is an analytical-survey approach that

uses both actual technical data and experts’ opinions. Various analysis tools are embedded

in the approach including: data collection and clustering, interviews with experts, 5-Whys,

Pareto charts, cause and effect diagram, and critical ratio control charts. The approach was

implemented on seven projects obtained from a leading international paper machine supplier.

As a result, it was found that the main causes behind cost and schedule deviations

are products’ related; including technical accidents in the Press section, damaged parts, design

issues, optimization of the machine and missing parts. Based on the results, prevention

measures were perceived.

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 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.