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A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Tapia, Gustavo, Elwany, Alaa·2014·Journal of Manufacturing Science and Engineering

Abstract

There is consensus among both the research and industrial communities, and even the general public, that additive manufacturing (AM) processes capable of processing metallic materials are a set of game changing technologies that offer unique capabilities with tremendous application potential that cannot be matched by traditional manufacturing technologies. Unfortunately, with all what AM has to offer, the quality and repeatability of metal parts still hamper significantly their widespread as viable manufacturing processes. This is particularly true in industrial sectors with stringent requirements on part quality such as the aerospace and healthcare sectors. One approach to overcome this challenge that has recently been receiving increasing attention is process monitoring and real-time process control to enhance part quality and repeatability. This has been addressed by numerous research efforts in the past decade and continues to be identified as a high priority research goal. In this review paper, we fill an important gap in the literature represented by the absence of one single source that comprehensively describes what has been achieved and provides insight on what still needs to be achieved in the field of process monitoring and control for metal-based AM processes.

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Metadata

Authors
Tapia, G., & Elwany, A.
Venue
Journal of Manufacturing Science and Engineering
Published
2014-10-24
Volume
136
Issue
6
ISSN
  • 1087-1357 (ISSN-L)
  • 1528-8935
OpenAlex
W2148430292
Cited by
711
References
0
FWCI
36.62
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing. https://app.cochat.ai/papers/10.1115/1.4028540
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Tapia, G., & Elwany, A. (2014). A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4028540

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.