Aco Antić

Evaluation of residual stresses after irregular interrupted machining

Residual stress occurs in many machined components and parts. Over time, several methods have been developed for the investigation of residual stress in the material – destructive and non-destructive. This article deals with the evaluation and comparison of residual stress in the material when machining steel C45 and 11CrMo910, when the tool enters into the cut and stands out in conditions of an interrupted cut. A non-destructive method, based on X-Ray diffraction, was applied to evaluate the residual stress. The points were measured on the last machined slat by using interrupted cut simulator. An irregular interrupted cut was achieved by gradually machining 4, 3, 2 and 1 slat. The experiments were realized in co-operation with the Faculty of Mechanical Engineering, VSB – Technical University of Ostrava, Czech Republic and the Faculty of Mechanical Engineering, the University of Žilina, Slovakia.

USE OF SOFT COMPUTING TECHNIQUE FOR MODELLING AND PREDICTION OF CNC GRINDING PROCESS

Due to the complexity of grinding process of multilayer ceramics, and the need for a specific product quality, the choice of optimal technological parameters is a challenging task for the manufacturers. The main aim of investigation is to secure the demanded final product quality (plane parallelism) in the function of input parameters (machine, machine operator, foil and production line). “Soft computing techniques” are becoming more interesting to the researchers for the modelling of processing parameters of complex technological processes. In this paper, a soft computing technique, known as the Artificial Neural Networks (ANN), is used for the modelling and prediction of parameters of technological process of CNC grinding of multilayer ceramics. The results show that the ANN with the back- propagation algorithm justifies the application also to this problem. By designing different architectures of ANN (learning rules, transfer functions, number and structure of hidden layers and other) on the set of data from the production - technological process, the best result of RMS error (10, 76 %) in the process of learning and 12, 07 % in the process of validation was achieved. The achieved results confirm the acceptability and the application of this investigation in the technological and operational preparation of production.