Hocine Bendjama

The monitoring of casting quality is very important to ensure the safe operation of casting processes. In this paper, in order to improve the accurate detection of casting defects, a combined method based on Principal Component Analysis (PCA) and Self-Organizing Map (SOM) is presented. The proposed method reduces the dimensionality of the original data by the projection of the data onto a smaller subspace through PCA. It uses Hotelling’s T² and Q statistics as essential features for characterizing the process functionality. The SOM is used to improve the separation between casting defects. It computes the metric distances based similarity, using the T² and Q (T²Q) statistics as input. A comparative study between conventional SOM, SOM with reduced data and SOM with selected features is examined. The proposed method is used to identify the running conditions of the low pressure lost foam casting process. The monitoring results indicate that the SOM based on T²Q as feature vectors remains important comparatively to conventional SOM and SOM based on reduced data.

The aim of this work is to study the plastic instabilities occurring on the stamped sheets during deep drawing process. The analysis of the plastic deformation of the material showed that the deformation occurs in bi-axial extension at the bottom of the punch due to thinning of the sheet, in local necking together at the vertical wall level of the sheet and below the blank holder due to thickening of the sheet. As a first step, an experimental characterization of the material is undertaken, whose experimental tests made it possible to determine the fundamental characteristics of the material. In the second step, a study of the material behaviour during forming process by numerical simulation using Abaqus finite element code is proposed. The various simulations undertaken showed the variation of the two parameters; the blank holder force and the friction effect. The blank holder force and friction, applied respectively to the blank flange region and between the tool-blank surfaces, make it possible to optimize the deformation limits and to repel any instability which may appear on the material in deep drawing. The simulations carried out on Abaqus code allow to visualize the material behaviour during deformation, by locating the thinning and necking zones on the sheet and from there, in order to locate areas at risk of failure. An optimization of the process is proposed by varying the considered parameters in a validated numerical model. Satisfactory results have been obtained which clearly show the failure and the safe zones.