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Research Papers

An Approach Based on Process Signature Modeling for Roundness Evaluation of Manufactured Items

[+] Author and Article Information
Giovanni Moroni

 Dipartimento di Meccanica, Politecnico di Milano, I-20156 Milano, Italygiovanni.moroni@polimi.it

Massimo Pacella

Dipartimento di Ingegneria dell’Innovazione, Università del Salento, I-73100 Lecce, Italymassimo.pacella@unile.it

J. Comput. Inf. Sci. Eng 8(2), 021003 (Apr 30, 2008) (10 pages) doi:10.1115/1.2904923 History: Received September 30, 2005; Revised February 26, 2008; Published April 30, 2008

In evaluating the geometrical characteristics of mechanical part, cleverness may be added with the definition of an empirical model representing the “signature” left by the manufacturing process used to make the part. This manufacturing signature is the systematic pattern that characterizes all the features machined with that process. If such a model is available, it may be exploited to enhance geometrical inspection accuracy. In this paper, an approach for geometrical inspection of machined profiles is proposed. This approach consists in computing form deviations by reconstructing the actual profile using a frequency model of process signature. The method has been thoroughly investigated in different simulated scenarios and benefits in terms of improved accuracy are demonstrated. Within the paper, a case study, related to roundness of mechanical parts obtained by turning, is used. The relationships between the number of sampled points and fitting algorithms are also pointed out.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

An N-point sampled circular profile

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Figure 2

Interval plots of err (ordinate axis percentage error—adimensional). Simulation results (10,000 replications).

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Figure 3

Plots for err(ARMAX) (a) and err(Cho and Tu) (b) using the MZ fitting algorithm (ordinate axis percentage error—adimensional). The dashed lines refer to profile reconstruction case (rec=1); continuous lines refer to no reconstruction case (rec=0). Simulation results based on 10,000 replications (simulation error negligible).

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Figure 4

95% confidence interval for err(ARMAX) and err(Cho and Tu) using MZ fitting algorithm (ordinate axis percentage error—adimensional). (a) 6 sampled points; (b) 12 sampled points; (c) 23 sampled points; (d) 47 sampled points; (e) 94 sampled points; (f) 187 sampled points. Simulation results (10,000 replications).

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Figure 5

95% confidence interval for err(ARMAX) and err(Cho and Tu) using the MZ fitting algorithm (ordinate axis percentage error—adimensional). 374 sampled points. Simulation results (10,000 replications).

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Figure 6

Plots for err(ARMAX) (a) and err(Cho and Tu) (b) using the LS fitting algorithm (ordinate axis percentage error—adimensional). The dashed lines refer to profile reconstruction case (rec=1); the continuous lines refer to no reconstruction case (rec=0). Simulation results based on 10,000 replications (simulation error negligible).

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Figure 7

95% confidence interval for err(ARMAX) and err(Cho and Tu) using the LS fitting algorithm (ordinate axis percentage error—adimensional). (a) 6 sampled points; (b) 12 sampled points; (c) 23 sampled points; (d) 47 sampled points; (e) 94 sampled points; (f) 187 sampled points. Simulation results (10,000 replications).

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Figure 8

95% confidence interval for err(ARMAX) and err(Cho and Tu) using the LS fitting algorithm (ordinate axis percentage error—adimensional). 374 sampled points. Simulation results (10,000 replications).

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