Adaptive network based inference system for cutting force simulation in milling of multi-layered metal materials
Journal Title: Proceedings in Manufacturing Systems - Year 2017, Vol 12, Issue 2
Abstract
This paper outlines the experimental research on cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an adaptive neuro-fuzzy inference (ANFIS) modelling technique for simulating the dynamic cutting force components during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. A Kistler dynamometer was used to measure the actual cutting force, which was compared with the estimated one obtained via suggested procedure. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during developing of the ANFIS models. Analysis of the developed models has been performed to test their validity. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.1%.
Authors and Affiliations
Uros Zuperl, Franc Cus
Keywords
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- EP ID EP240825
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