Abstract
In this paper, we propose a process inspection framework for metal additive manufacturing (AM) processes. AM, also known as 3D printing, is the process of joining materials to make objects on the basis of 3D model data and is envisioned to play a strategic role in maintaining economic and scientific dominance. Different from conventional manufacturing methods, the AM process is a point-by-point and layer-by-layer manufacturing. Thus, there are many opportunities to generate a process error that can cause quality issues in an AM part. A systematic AM process inspection is needed to yield acceptable performance of the part. The critical parameters that may affect the part quality are identified before processing, during processing, and after processing. The framework of the initial AM process inspection is presented. By using basic sensors, such as a microhardness tester and profilometer, we can obtain critical information about an additive manufactured part.
Recommended Citation
C. Cheng et al., "A Framework for Process Inspection of Metal Additive Manufacturing," Sensors and Materials, vol. 31, no. 2, pp. 411 - 420, M Y U Scientific Publishing Division, Feb 2019.
The definitive version is available at https://doi.org/10.18494/SAM.2019.2106
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Additive Manufacturing; Process Inspection; Process Parameters
International Standard Serial Number (ISSN)
0914-4935
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Feb 2019
Comments
The authors would like to thank the Ministry of Science and Technology (MOST) and Fisheries Agency, Council of Agriculture (FA.COA) for their support of the project [Grant Nos. MOST 107-2218-E-006-031-, MOST 107-2218-E-110-004-, and 107AS-14.2.7-FA-F1(3)]. Additionally, this research was, in part, supported by the Ministry of Education, Taiwan, Headquarters of Advancement to the Intelligent Manufacturing Center (iMRC), National Cheng Kung University (NCKU), and US National Science Foundation (CMMI 1625736).