Mathematical Representation of Friction in Metal Forming Analysis
As recently stressed by Kalpakjian [1]. Schey [2] and many other authors [3-8] the development is an improved friction model is an important topic in metal farming analysis. In the present study a fresh friction approach is suggested. It connects the friction shear stress not only with the normal stress but also with the relative displacement between tool and workpiece and the relative surface increase. The model presented is based on the physical conception of the friction phenomenon as described by Schey [2.4″], Wanheim, [7] and Dautzenberg [8, 9]. Connections with the well known Coulomb model (T Fr = up) and the so called constant friction model [T Fr = m J F /✓3 and discussed. Theoretical findings are verified with experimental data from different tests. Although a few questions are still open, some progress in technical understanding of friction in forming tools could be achieved.
The prediction of tool pressures in coining
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Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.