Comparison of different mathematical models for prediction of self-excited vibrations occurance in milling process

In modern production, despite the existence of other production methods, metal cutting still plays an important role. The performance of machine tools has a decisive role in terms of productivity and quality of production increase. Undoubtedly, productivity and quality of production are two mail requirements which are key elements to stay on top in a competitive market. One of the most influencing factor that affect the machine tools are vibrations. The most unwanted vibrations that can appear during metal cutting process are self-excited vibrations, which are one of the three kinds of mechanical vibration, free vibration, forced vibration, and self-excited vibration. When it comes to improving the performance of machine tools, the analysis of the appearance of self-excited vibrations and their isolation occupy a significant place. The aim of this paper derives from trends and limitations exists in metal production. The way to isolate the self-excited vibrations is to predict their occurrence by defining the stability lobe diagram. The paper presents two popular analytical methods for identifying stability lobe diagrams in milling, which shows the boundary between stable and unstable zone of machining operations, depending on the number of revolutions of the spindle and cutting depth. First considered method is Fourier series approach and second one id average tooth angle approach. Lather, both stability lobe diagrams were compared with results obtained experimentally

Significant factors of the successful lean six-sigma implementation

© 2017 International Journal of Mathematical, Engineering and Management Sciences. Based on an extensive literature review we have selected factors critical for Lean Six Sigma implementation success. Four variables were selected to be used as output variables measuring this project success: project on time completion, achievement of financial goals, sigma level achieved (that was measured using Defects per Million Opportunities, DPMO), and overall project success. Using empirical data from 256 Lean Six Sigma Projects, we present the model developed and identify significant factors for Lean Six Sigma implementation success. Empirical results, which were collected during Lean Six Sigma implementation in 39 business units of an Automotive Sector Company in North America and Europe, were analysed using Multivariate Analysis of Variance (MANOVA) and General Linear Model (GLM). Two main factors were found as positively linked with the different aspects of project success: the competency of the Black Belts team and the management support to the project