Abstract
Laser metal additive manufacturing (LMAM) is a significant part of the advanced manufacturing industry. The technology enables the production of complex parts via layer-by-layer deposition through robust bonding mechanisms. Despite the inherent advantages of additive technology, the quality control of its products remains the challenge to increase the applicability of the AM parts and products as complex physical processes occur at the melt pool (MP) where laser and metal interact. There are many variables in LMAM that impact the final part quality, and many scholars have investigated how the individual variable affects the part quality. The contribution this review makes is to assess how process parameters affect process signatures (derived from in situ techniques) and how these in turn affect part quality. By focussing on the connection across process parameters, process signatures, and part quality, the review aims to systematically tackle the complexity of the LMAM process and thus provide insight into online diagnostic of quality estimation. This paper starts with common part quality issues and briefly reviews process parameters to part quality correlations. As a vital step to link the process parameters and part quality, different in situ monitoring techniques and their acquired data are summarised. The correlations between the important factors affecting the quality of the additive process are systematically reviewed. These correlations include parameter-signature, signature-quality, and parameter-signature-quality correlations. Linking process parameters and part quality through the process signatures is highly desirable. These correlations bring opportunities to design a more comprehensive feedback controller to improve the repeatability and reliability of LMAM systems. Results found through the investigation of disconnected factors contribute to building a holistic understanding of LMAM.
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Abbreviations
- 2D:
-
Two-dimensional
- 3D:
-
Three-dimensional
- ANN:
-
Artificial neural network
- BP:
-
Backpropagation
- CAD:
-
Computer-aided design
- CCD:
-
Charge-coupled device
- CMOS:
-
Complementary metal–oxide–semiconductor
- CNN:
-
Convolutional neural network
- CT:
-
Computed tomography
- DBN:
-
Deep belief network
- DSLR:
-
Digital single-lens reflex
- FEM:
-
Finite element method
- FVM:
-
Finite volume method
- GA:
-
Genetic algorithm
- HIP:
-
Hot isostatic pressing
- ILC:
-
Iterative learning control
- IR:
-
Infrared
- KNN:
-
K-nearest neighbours
- LMAM:
-
Laser metal additive manufacturing
- LMD:
-
Laser metal deposition
- LS-SVM:
-
Least square support vector machine
- LWIR:
-
Long-wave infrared
- MIMO:
-
Multi-input-multi-output
- ML:
-
Machine learning
- MP:
-
Melt pool
- MWIR:
-
Middle-wave infrared
- NDT:
-
Non-destructive testing
- NIR:
-
Near-infrared
- NN:
-
Neural network
- PCA:
-
Principal component analysis
- PSO:
-
Particle swarm optimization
- SISO:
-
Single-input–single-output
- SLM:
-
Selective laser melting
- SLS:
-
Selective laser sintering
- SOM:
-
Self-organizing map
- SWIR:
-
Short-wave infrared
- SVM:
-
Support vector machine
- SVR:
-
Support vector regression
- UTS:
-
Ultimate tensile strength
- VIS:
-
Visible
- YS:
-
Yield strength
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The authors acknowledge the support through the provision of the RMIT-CSIRO Scholarship.
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This work was supported by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and its Active Integrated Matter Future Science Platform (AIM FSP) (Grant number [AIM FSP_TB10_WP05]).
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All authors contributed to the study conception and design, as well as data collection and analysis. Contribution of knowledge was performed by Nazmul Alam and Ivan Cole. The first draft of the manuscript was written by Jiayu Ye, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Ye, J., Bab-hadiashar, A., Alam, N. et al. A review of the parameter-signature-quality correlations through in situ sensing in laser metal additive manufacturing. Int J Adv Manuf Technol 124, 1401–1427 (2023). https://doi.org/10.1007/s00170-022-10618-0
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DOI: https://doi.org/10.1007/s00170-022-10618-0