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

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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

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Towards self-optimizing and self-adaptive milling processes
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