In this paper, the applications of the multivariate data analysis and optimization on vibration signals from compressors have been tested on the assembly line to identify nonconforming products. The multivariate analysis has wide applicability in the optimization of weather forecasting, agricultural experiments, or, as in this case study, in quality control. The techniques of discriminant analysis and linear program were used to solve the problem. The acceleration and velocity signals used in this work were measured in twenty-five rotating compressors, of which eleven were classified as good baseline compressors and fourteen with manufacturing defects by the specialists in the final acoustic test of the production line. The results obtained with the discriminant analysis separated the conforming and nonconforming groups with a significance level of 0.01, which validated the proposed methodology.
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 preliminary results of investigations on a relationship between turbidity and other quality parameters in the SBR plant effluent. The laboratory tests demonstrated a high correlation between an effluent turbidity and a total suspended solids (TSS) concentration as well as between TSS and COD. Such a relationship would help to continuously monitor and control quality of a wastewater discharge using turbidity measurement.
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.