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research-article

Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing

[+] Author and Article Information
Bing Yao

Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA 16802
bzy111@psu.edu

Farhad Imani

Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA 16802
fxi1@psu.edu

Aniket S. Sakpal

Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA 16802
aniketskpl19@gmail.com

Edward (Ted) Reutzel

Department of Laser System Engineering and Integration, The Pennsylvania State University, P.O. Box 30, State College, PA, USA
ewr101@arl.psu.edu

Hui Yang

Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA 16802
huy25@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4037891 History: Received July 18, 2017; Revised August 30, 2017

Abstract

Metal-based Powder-Bed-Fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conventional subtractive manufacturing processes, and is flexible to produce parts with complex geometries. Quality and repeatability of AM parts remain a challenging issue that persistently hampers wide applications of AM technology. Rapid advancements in sensing technology, especially imaging sensing systems, provide an opportunity to overcome such challenges. However, little has been done to fully utilize the image profiles acquired in the AM process and study the fractal patterns for the purpose of process monitoring, quality assessment and control. This paper presents a new multifractal methodology for the characterization and detection of defects in PBF-AM built components. Both simulation and real-world case studies show that the proposed approach effectively detects and characterizes various defect patterns in AM images and has strong potential for quality control of AM processes.

Copyright (c) 2017 by ASME
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