LONG-TERM THERMAL COMPENSATION OF 5-AXIS MACHINE TOOLS DUE TO THERMAL ADAPTIVE LEARNING CONTROL

Abstract

This paper presents a prediction and compensation approach for thermal errors of 5-axis machine tools, based on supervised online machine learning. Process-intermittent probing is used to identify and update a thermal autoregressive with exogenous input (ARX) model. The approach is capable of predicting and compensating thermal displacements of the tool center point based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like dry or wet machining. The self-optimized machine tool shows very stable long-term behavior under drastically varying machining and boundary conditions. The implementation is validated on a set of thermal test pieces. The test pieces show that the major share of thermal workpiece errors are reduced by the thermal adaptive learning control.

Recommended articles

INVESTIGATION OF THE COOLANT FLUID FLOW DISTRIBUTION IN THE GRINDING GAP

K. Wegener, C. Baumgart
Keywords: Grinding; Coolant supply; Coolant nozzles; Grinding fluid flows; Grinding gap; Metal working fluids

INVESTIGATION ON THE PRODUCTIVITY OF MILLING TI6AL4V WITH CRYOGENIC MINIMUM QUANTITY LUBRICATION

D. Gross, M. Appis, N. Hanenkamp
Keywords: Carbon dioxide; Cryogenic; CMQL; Ti6Al4V; Milling; Spray test