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Open AccessArticle

Prediction of Tool Point Frequency Response Functions Within Machine Tool Work Volume Considering the Position and Feed Direction Dependence

by Congying Deng 1,2,3, Yi Feng 2,*, Jie Shu 2, Zhiyu Huang 3 and Qian Tang 1
1
College of Mechanical Engineering, Chongqing University, Chongqing, 400030, China
2
School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3
Chongqing Machinery and Electrical Holding (Group) Co.Ltd., Chongqing 401123, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(7), 1073; https://doi.org/10.3390/sym12071073
Received: 17 June 2020 / Revised: 26 June 2020 / Accepted: 27 June 2020 / Published: 30 June 2020
A chatter vibration in milling process results in poor surface finish and machining efficiency. To avoid the chatter vibration, the stability lobe diagram (SLD) which is the function of tool point frequency response functions (FRFs) is adopted to predict the chatter-free machining parameters. However, the tool point FRF varies with the changes of machining positions and feed directions within machine tool work volume. Considering this situation, this paper presents a method to predict the position and feed direction-dependent tool point FRF. First, modal parameters of the tool point FRFs obtained at some typical positions and feed directions are identified by the modal theory and matrix transformation method. With the sample information, a back propagation (BP) neural network whose inputs are the position coordinates and feed angle and outputs are the modal parameters can be trained with the aid of the particle swarm optimization (PSO) algorithm. Then, modal parameters corresponding to any position and feed direction can be predicted by the trained BP neural network and used to reorganize the tool point FRFs with the modal fitting technique. A case study was performed on a real vertical machining center to demonstrate the accurate prediction of position and feed direction-dependent tool point FRFs. Furthermore, the position and feed direction-dependent milling stability was researched and origin-symmetric distributions of the limiting axial cutting depths at each machining position were observed.
Keywords: machine tool work volume; position and feed direction-dependent tool point FRF; BP neural network; particle swarm optimization; modal fitting technique machine tool work volume; position and feed direction-dependent tool point FRF; BP neural network; particle swarm optimization; modal fitting technique
MDPI and ACS Style

Deng, C.; Feng, Y.; Shu, J.; Huang, Z.; Tang, Q. Prediction of Tool Point Frequency Response Functions Within Machine Tool Work Volume Considering the Position and Feed Direction Dependence. Symmetry 2020, 12, 1073.

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