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A Discrete and Hybrid Approach to Predicting Diametrical Errors in Slender Shaft Turning

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A Discrete and Hybrid Approach to Predicting Diametrical Errors in Slender Shaft Turning

Fang, Xiaoyi (2020) A Discrete and Hybrid Approach to Predicting Diametrical Errors in Slender Shaft Turning. Masters thesis, Concordia University.

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Abstract

Slender shafts have a high length-to-radius ratio, low rigidity, and are often machined on a lathe to comply with the tight tolerance requirement. The machined accuracy for slender shafts, which is reflected in its diametrical deviations, is very sensitive to forces exerted by the cutting tool. To compensate for such errors effectively, this research aims to predict the diametrical deviations in slender shafts turning process efficiently and accurately. First, based on the geometric principles in turning processes, a mathematical model is built, in order to relate the given depth of cut and the shaft deflection due to the force of the cutting tool to the diametrical deviations. Then a novel finite element model considering the practical machining situation is developed to solve the aforementioned mathematical model. Compared with traditional finite element methods, the novel method addresses the interaction between the depth of cut and the cutting force throughout the machining process. A discretization method is employed to handle this coupled interaction. The approach to diametrical deviations prediction is verified with the experimental data and situations for various machining parameters and stock materials are discussed. The approach is also extended to the generic case involving workpieces with different diameters features along the shaft.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Fang, Xiaoyi
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:28 October 2020
Thesis Supervisor(s):Chen, Zezhong
ID Code:987647
Deposited By: XIAOYI FANG
Deposited On:23 Jun 2021 16:34
Last Modified:23 Jun 2021 16:34
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