Regenerative reassembly phenomenon in the turning process machining the A1020 steel

Abstract

The chattering in the machine tools is a phenomenon that causes instability in the machining process, surface finish with high roughness, also produces excessive and accelerated wear on the tool in the metal cutting processes, this phenomenon consists of self-excited vibrations which are produced and maintained due to the cutting forces, the purpose of this article is to analyze regenerative chatter and predict the optimal points of operation in the turning process for 1020 carbon steel by developing analytical methods for generating stability lobe diagrams. To achieve this objective, the methods proposed by Altinas and Budak to stabilize the self-excited vibrations in the turning process applied to the orthogonal cut were studied in detail. These methods are widely accepted within the community of researchers and specialists in the field, thanks to the excellent results obtained in practice. Once the models studied were compressed, a computational algorithm was developed with the help of the MATLAB® software that was able to generate stability lobe diagrams for the turning operation in order to find the optimal points of operation so that there was no chatter, thus improving the material removal rate and increasing productivity.

Analyzing chatter vibration during turning on computer numerical control lathe using ensemble local mean decomposition and probabilistic approach

“…In machining processes, specifically in turning operation, three types of mechanical vibrations may occur: free, forced, and self-excited vibration. 25 The free type of vibrations is induced due to the sudden shock experienced within the machine, while the forced vibrations are prompted due to the unbalanced shaft, bearing, etc. These free and forced vibrations can be identified and eliminated easily as they are induced due to shock and unbalance in the machine, respectively.…”

“…Initially, CI values for all the 27 experiments have been plotted as scatter plot. Thereafter, equation (25) has been used to find the probability density function of the Nakagami distribution. These density values were joined to form a short of the Gaussian curve as represented in Figure 7(a).…”