This paper concerns the issues of measurement techniques, analysis and assessment of the machined surface geometric structure. The aim of this work was to show the application of surface analysis in diagnosing the causes of discrepancies occurring in the manufacturing process, which may result from ill-matched (poorly fitting) process parameters. An appropriate system of control and interpretation of results may allow early reaction to unfavorable trends (for example blunting of the tool) and prevention of undesirable defects. The subject of research was a waste basket used in the construction of retaining sewer systems. In this paper, the quality of the waste basket as well as its manufacturing process were analyzed and assessed. The research was carried out with the use of three measurement stands, i.e. optical microscopy (OM), scanning electron microscopy (SEM) and white light interferometer (WLI). The surface analysis proved to be important from the viewpoint of outlining the production process as well as improving the product quality. The software used for topographical analysis appeared to be significant for the success of the analysis, providing notable economic effects, namely the lack of defects.
The chosen, typical causes of quality defects of cast-iron „alphin” rings embedded in aluminum cast are being presented in this paper. Diffusive joint of those inserts with the pistons casts is being used, due to extreme work conditions of destructive influence of the fuel mix and variable thermo-mechanical loads, which reign in the combustion motor working chamber.
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.