Towards self-optimizing and self-adaptive milling processes (English)

How to get this document?

Local TIB services
TIB document delivery Purchase

This paper presents a novel control architecture system which is composed of a multi-objective cost function which Pareto optimises the programming of cutting parameters while adapting the milling process to new cutting conditions if new constraints appear. The paper combines a self-optimised module which looks for and finds Pareto optimal cutting parameters according to multi-objective purposes and, a multiestimation adaptive control module which keeps the cutting forces under prescribed upper safety limits independently of programmed cutting conditions and material properties while maintaining the performance of the process. A supervised controller acts as decision support-software to automatically switch to best performance tracking adaptive controller among those available at each required time. A novel control scheme is proposed. It is composed of two levels. The first one, the self-optimised cutting parameters layer compromises life of the tool, material remove rate, surface roughness and the robustness of the system. While the second one, the multi-parallel adaptive controller, provides an environment to adaptively control the milling process under changes in cutting parameters. A rule-based supervised controller is able to choose automatically the most suitable controller among the set of designed for each Pareto optimal cutting parameters. As a result, the control architecture leads to automatically work out the complex milling system using an easy interface with the operator. Simulation results support the performance of the system.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

29
Towards self-optimizing and self-adaptive milling processes
Rubio, L. / Longstaff, A.P. / Fletcher, S. / Myers, A. | 2013
39
Single beam method for three-axial vibration measurement
Brecher, C. / Bäumler, S. / Wissmann, M. / Guralnik, A. | 2013
69
Ultra-precision machine system feedback-controlled using hexapod-type measurement device for six-degree-of-freedom relative motions between tool and workpiece
Oiwa, Takaaki / Yao, WenBo / Asama, Junichi | 2013
79
Comparative study of ANN and ANFIS predprediction models for thermal error compensation on CNC machine tools
Abdulshahed, A. / Longstaff, A. / Fletcher, S. / Myers, A. | 2013
111
In-process measurement of machine structure deformation and compensation of resulting work piece inaccuracies
Brecher, C. / Flore, J. / Klatte, M. / Wenzel, C. | 2013
133
Traceable measurements using machine tools
Schmitt, Robert / Jatzkowski, Philipp / Peterek, Martin | 2013
161
Comprehensive calibration of robots and large machine tools using high precision laser-multilateration
Brecher, C. / Behrens, J. / Flore, J. / Wenzel, C. | 2013
171
Real-time surface defect detection and the traceable measurement of defect depth in 3D
Tailor, M. / Phaithoonbuathong, P. / Petzing, J. / Jackson, M. / Parkin, R. | 2013
181
5D precision process monitoring
Brecher, C. / Lindemann, D. / Merz, A. / Wenzel, C. | 2013
213
Systematic analysis of 5-axis machine error budgets: Decreasing the calibration effort without decreasing the machining accuracy
Brecher, C. / Flore, J. / Wenzel, C. | 2013
245
Integrated approach of different simulation strategies to achieve an efficient reconfiguration of assembly systems
Quinders, S. | 2013
299
Grinding of monocrystalline diamond
Brecher, C. / Sobotka, A. / Wenzel, C. | 2013
337
A novel method for producing a polymer microfluidic device
Väyrynen, Juha / Mönkkönen, Kari / Siitonen, Samuli / Hassinen, Sami | 2013
365
Advanced laser profile scanner application for micro part detection
Kuhfuss, B. / Schenck, C. / Wilhelmi, P. | 2013