Influence of Resistant Forces on the Positioning Precision of Kinematic Feed Chains Used on CNC Machine Tools

Many researches from the international literature and also those of the specialised companies from the machine tools domain, have focused in the past years on increasing the positioning precision and implicitly, the manufacturing precision of the numerically controlled machine tools. The results of these efforts have led to the elimination or compensation of different factors which affect the positioning precision of the kinematic feed chains. Nevertheless, the effects of some factors still find themselves in the positioning error of the kinematic feed chains, an important component of this error being represented by the cutting and friction force induced error (resistant errors). This paper presents a new method of experimental analysis for establishing the influence of resistant forces on the positioning precision of the kinematic feed axis. Due to the fact that measuring the positioning precision during the cutting process is difficult to achieve, the experiments were carried out using a simulated force. In order to simulate an axial force, a hydraulic system was adopted, composed of a hydraulic cylinder, a distributor valve and a check valve.

The Spatio-Temporal Structure of Force Variability in Static Grasp Suggests a Continually Active Neural Controller

Fingertip forces during simple and static tripod grasp exhibit a surprisingly rich dynamics [1]. Here we explore the hypothesis that, even for this apparently simple manipulation task, these fluctuations are shaped by a neural controller rather than by signal-dependent motor noise. We fed band-limited noise processes scaled to mean force level into a 21-muscle model of 3-finger grasp, and compare model output with experimental force recordings. We find that the spatial and spectral characteristics of simulated force fluctuations differed greatly from those observed in actual static tripod grasp. In light of current literature [2], we propose that a continually active neural controller is at work even for this simplest example of multifinger manipulation.