Research Papers

Surface Variation Modeling by Fusing Multiresolution Spatially Nonstationary Data Under a Transfer Learning Framework

[+] Author and Article Information
Jie Ren

Department of Industrial and
Manufacturing Engineering,
Florida A&M University-Florida State
University College of Engineering,
Tallahassee, FL 32310

Hui Wang

Department of Industrial and
Manufacturing Engineering,
Florida A&M University-Florida State
University College of Engineering,
Tallahassee, FL 32310
e-mail: hwang10@fsu.edu

1Corresponding author.

Manuscript received January 15, 2018; final manuscript received September 6, 2018; published online October 10, 2018. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 141(1), 011002 (Oct 10, 2018) (11 pages) Paper No: MANU-18-1031; doi: 10.1115/1.4041425 History: Received January 15, 2018; Revised September 06, 2018

High-definition metrology (HDM) has gained significant attention for surface quality inspection since it can reveal spatial surface variations in detail. Due to its cost and durability, such HDM measurements are occasionally implemented. The limitation creates a new research opportunity to improve surface variation characterization by fusing the insights gained from limited HDM data with widely available low-resolution surface data during quality inspections. A useful insight from state-of-the-art research using HDM is the revealed relationship and positive correlation between surface height and certain measurable covariates, such as material removal rate (MRR). Such a relationship was assumed spatially constant and integrated with surface measurements to improve surface quality modeling. However, this method encounters challenges when the covariates have nonstationary relationships with the surface height over different surface areas, i.e., the covariate-surface height relationship is spatially varying. Additionally, the nonstationary relationship can only be captured by HDM, adding to the challenge of surface modeling when most training data are measured at low resolution. This paper proposes a transfer learning (TL) framework to deal with these challenges by which the common information from a spatial model of an HDM-measured surface is transferred to a new surface where only low-resolution data are available. Under this framework, the paper develops and compares three surface models to characterize the nonstationary relationship including two varying coefficient-based spatial models and an inference rule-based spatial model. Real-world case studies were conducted to demonstrate the proposed methods for improving surface modeling.

Copyright © 2019 by ASME
Topics: Modeling , Algorithms
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Fig. 4

Varying model coefficients of two engine head surfaces: (a) coefficient c1 of surface I and (b) coefficient c1 of surface II

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Fig. 3

Correlation between process variables and surface height: (a) two directions while cutting; (b) normalized MRR and its relation with surface variation; and (c) cutter insert engagement with surface and its impact on surface height

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Fig. 2

An outline of the research problem formulation for HDM-based surface variation modeling

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Fig. 1

Profilometer measurements on surface roughness and CMM measurements on surface flatness

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Fig. 6

Architecture of ANFIS and tl-ANFIS

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Fig. 5

Takagi-Sugeno fuzzy rules. Adapted from [44].

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Fig. 9

Logic of TL-based spatial modeling for the block surface: (a) HDM measurements for block I; (b) normalized MRR and the number of insert-engagement for block I; (c) LDM measurements for block II; (d) normalized MRR and the number of insert-engagement for block II; and (e) modeling of block II using TL-based spatial model

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Fig. 7

Logic of transfer learning (TL)-based spatial modeling for an engine head surface: (a) HDM measurements for surface I (partial enlarged detail shown in the box); (b) normalized MRR for surface I; (c) LDM measurements for surface II; (d) normalized MRR for surface II; and (e) modeling of surface II using TL-based spatial model (partial enlarged detail shown in the box)

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Fig. 8

Modeling results of tail sections on surface II versus true data for the engine head deck face. It can be seen that the proposed methods (b) and (d) achieve an estimation closer to the true data (a) compared with a state-of-the-art method (e) and a commonly used method (f). Method (c) estimates bad at the left tail section.

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Fig. 10

Surface modeling results for block II. The results show that the proposed tl-GWR (b), tl-RR(c), and tl-ANFIS (d) achieve the estimation closer to the true data (a) compared with a state-of-the-art method (e) and a commonly used method (f).



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