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 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.
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.
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 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.