Research Papers

Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing

[+] Author and Article Information
Seyyed Hadi Seifi

Department of Industrial and Systems Engineering,
Mississippi State University,
Starkville, MS 39762
e-mail: ss4350@msstate.edu

Wenmeng Tian

Department of Industrial and Systems Engineering,
Mississippi State University,
Starkville, MS 39762
e-mail: tian@ise.msstate.edu

Haley Doude

Center for Advanced Vehicular Systems,
Mississippi State University,
Starkville, MS 39762
e-mail: haley@cavs.msstate.edu

Mark A. Tschopp

Fellow ASME
Army Research Laboratory,
Chicago, IL 60615
e-mail: mark.a.tschopp.civ@mail.mil

Linkan Bian

Department of Industrial and Systems Engineering,
Center for Advanced Vehicular Systems,
Mississippi State University,
Starkville, MS 39762
e-mail: bian@ise.msstate.edu

1Corresponding author.

Manuscript received August 4, 2018; final manuscript received May 23, 2019; published online June 21, 2019. Assoc. Editor: Qiang Huang. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government’s contributions.

J. Manuf. Sci. Eng 141(8), 081013 (Jun 21, 2019) (12 pages) Paper No: MANU-18-1582; doi: 10.1115/1.4043898 History: Received August 04, 2018; Accepted May 26, 2019

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of approaching this problem, how to develop informative process signatures to detect part anomalies for quality control is still an open question. The objective of this study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with the layer-wise quality of the part. The resultant layer-wise quality features can be used to predict the overall defect distribution of a fabricated layer during the build. The proposed model is validated through a case study based on a direct laser deposition experiment, where the layer-wise quality of the part is predicted on the fly. The accuracy of prediction is calculated using three measures (i.e., recall, precision, and F-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The proposed quality prediction methodology enables online process correction to eliminate anomalies and to ultimately improve the quality of the fabricated parts.

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Fig. 1

Illustration of the four main steps toward achieving the key signatures: (a) initial layer-based thermal images, (b) tensor structure of the layer, (c) extracted principal components, and (d) layer-wise process signature

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Fig. 2

Demonstration of the change in layer feature behavior due to a change in the single melt pool feature where square dot stands for a normal melt pool and triangle dot stands for an abnormal melt pool

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Fig. 3

Illustration of (a) corrupted image with no melt pool information and (b) melt pool with missing temperature measurements (circled)

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Fig. 4

An illustration of different number of melt pools within each layer when fabricating a 60-layer thin wall

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Fig. 5

Illustration of data transformation where (a) is the initial high dimensional data and (b) is the low-dimensional extracted grid with emphasis on heat affected zone

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Fig. 6

Illustration of the primary layer-wise key signature. Examples of (a) a healthy layer and (b) an unhealthy layer based on the first two PCs extracted from MPCA.

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Fig. 7

Demonstration of the norm of residuals for melt pools of two layers. Patterned bars are the norms for melt pools of the unhealthy layer where solid bars are for the healthy layer. There is only one defected melt pool in the defected layer which demonstrates the maximum norm of residuals.

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Fig. 8

The illustration of the part of an unhealthy layer which includes pores

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Fig. 9

Comparison of three different kernel variations of Gaussian family

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Fig. 10

General overview of parameter selection using cross validation

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Fig. 11

Illustration of the frequency of selected number of PCs in leave-one-out cross validation: (a) proposed and (b) benchmark methodology

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Fig. 12

Illustration of the frequency of selected number of PCs in Monte-Carlo cross validation: (a) proposed and (b) benchmark methodology

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Fig. 13

Illustration of one iteration of Monte-Carlo cross validation



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