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
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 5

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

Grahic Jump Location
Figure 6

Summary of the procedure to derive the extended state space model

Grahic Jump Location
Figure 7

Experimentation to model machining-induced variations

Grahic Jump Location
Figure 8

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

Grahic Jump Location
Figure 9

A three-station machining process

Grahic Jump Location
Figure 10

Relationship between feature deviation and the spindle temperature

Grahic Jump Location
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

Grahic Jump Location
Figure 12

Comparison of the average prediction errors in the five experimental conditions

Grahic Jump Location
Figure 1

Example of sources of variation in a MMP

Grahic Jump Location
Figure 2

Required modeling parameters for estimating machining-induced variations

Grahic Jump Location
Figure 3

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

Grahic Jump Location
Figure 4

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



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In