Research Papers

State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variations

[+] Author and Article Information
José V. Abellan-Nebot

Department of Industrial Engineering and Design,  Universitat Jaume I, Castellón de la Plana E-12071, Spainabellan@uji.es

Jian Liu1

Department of Systems and Industrial Engineering,  University of Arizona, Tucson, AZ 85718jianliu@sie.arizona.edu

Fernando Romero Subirón

Department of Industrial Engineering and Design,  Universitat Jaume I, Castellón de la Plana E-12071, Spainfromero@uji.es

Jianjun Shi

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332jianjun.shi@isye.gatech.edu


Corresponding author.

J. Manuf. Sci. Eng 134(2), 021002 (Apr 04, 2012) (13 pages) doi:10.1115/1.4005790 History: Received March 28, 2010; Revised November 24, 2011; Published March 30, 2012; Online April 04, 2012

In spite of the success of the stream of variation (SoV) approach to modeling variation propagation in multistation machining processes (MMPs), the absence of machining-induced variations could be an important factor that limits its application in accurate variation prediction. Such machining-induced variations are caused by geometric-thermal effects, cutting-tool wear, etc. In this paper, a generic framework for machining-induced variation representation based on differential motion vectors is presented. Based on this representation framework, machining-induced variations can be explicitly incorporated in the SoV model. An experimentation is designed and implemented to estimate the model coefficients related to spindle thermal-induced variations and cutting-tool wear-induced variations. The proposed model is compared with the conventional SoV model resulting in an average improvement on quality prediction of 67%. This result verifies the advantage of the proposed extended SoV model. The application of the new model can significantly extend the capability of SoV-model-based methodologies in solving more complex quality improvement problems for MMPs, such as process diagnosis and process tolerance allocation, etc.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

Example of sources of variation in a MMP

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Figure 2

Required modeling parameters for estimating machining-induced variations

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Figure 3

Example of the CSs involved in a 5-axis CNC machine-tool

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Figure 4

DMV from CS 2 to CS 1 if both CSs deviate from nominal values

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Figure 5

Relationships between the different CSs in a n-axis machine-tool

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Figure 6

Summary of the procedure to derive the extended state space model

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Figure 7

Experimentation to model machining-induced variations

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Figure 8

Aluminum 6061 part investigated by the case study. Surface CSs from S0 to S8

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Figure 9

A three-station machining process

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Figure 10

Relationship between feature deviation and the spindle temperature

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Figure 11

Relationship between machined feature deviations and: (a) flank wear at the primary cutting-tool edge, and (b) flank wear at the secondary cutting-tool edge

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Figure 12

Comparison of the average prediction errors in the five experimental conditions




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