Konrad Wegener

TL;DR: In this article, a parametric analysis of dry electrical discharge machining (DEDM) under different processing conditions by advanced plasma diagnostics is presented, which supports a better understanding of the effects of different working conditions on the DEDM discharges.

TL;DR: An unsupervised clustering approach is demonstrated to be capable of separating wear data such that meaningful and accurate estimations of the part condition are possible and the herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions.

Abstract: Up to 80% of all deviations on machine tools are caused by thermal effects [1]. To investigate the influence of thermal caused location errors of rotating and swiveling axes, different measurement set-ups (e.g. R-test) are examined. Thermal errors caused by rotary axes are compared to thermal errors caused by the environment, linear axes and spindles. It is shown that rotary axes can have a significant share in the total thermal distortions of 5-axis machine tools.

TL;DR: In this paper, the effect of different laser energy input distributions, such as pulse frequency and burst pulses, on the processing conditions in deep cutting kerfs and the resulting processing speed was examined.

TL;DR: In this article, an analytical approach for modeling the effect of clad height and substrate thickness is experimentally validated, and the authors highlight the potential of mechanical modeling and scanning strategy optimizations to increase shape accuracy for industrial applications in the field of additive manufacturing.