Methodology of analysis of casting defects

Purpose: The goal of this publication is to present the methodology of the automatic supervision and control of the technological process of manufacturing the elements from aluminium alloys and of the methodology of the automatic quality assessment of these elements basing on analysis of images obtained with the X-ray defect detection, employing the artificial intelligence tools. The methodologies developed will make identification and classification of defects possible and the appropriate process control will make it possible to reduce them and to eliminate them - at least in part. Design/methodology/approach: The methodology is presented in the paper, making it possible to determine the types and classes of defects developed during casting the elements from aluminium alloys, making use photos obtained with the flaw detection method with the X-ray radiation. It is very important to prepare the neural network data in the appropriate way, including their standardization, carrying out the proper image analysis and correct selection and calculation of the geometrical coefficients of flaws in the X-ray images. The computer software was developed for this task. Findings: Combining of all methods making use of image analysis, geometrical shape coefficients, and neural networks will make it possible to achieve the better efficiency of class recognition of flaws developed in the material. Practical implications: The presented issues may be essential, among others, for manufacturers of car subassemblies from light alloys, where meeting the stringent quality requirements ensures the demanded service life of the manufactured products. Originality/value: The correctly specified number of products enables such technological process control that the number of castings defects can be reduced by means of the proper correction of the process.