Abstract
Due to its unique benefits over standard conventional “subtractive” manufacturing, additive manufacturing is attracting growing interest in academic and industrial sectors. Here, special emphasis is given to wire arc additive manufacturing (WAAM), a directed energy deposition process that employs arc welding tools and wire to build metallic components by deposition of weld material. The WAAM process has several advantages, e.g., low cost, rapid deposition rate, and suitability for large complex metallic components. However, many WAAM challenges such as large welding deformation, undesirable porosity, and components with high residual stress remain to be overcome. Multidisciplinary cross-fusion research involving manufacturing, material science, automation control, and artificial intelligence/machine learning (ML) are deployed to overcome the above-mentioned problems. ML enables improved product quality control, process optimization, and modeling of complex multiphysics systems in the WAAM process. This research utilizes a data-driven literature review process, a defined and deliberate approach to localizing, evaluating, and analyzing published studies in the literature. The most relevant studies in the literature are analyzed using keyword co-occurrence and cluster analysis. Numerous aspects of WAAM, including design for WAAM, material analytics/characterization, defect detection/monitoring, as well as process modeling and optimization, have been examined to identify state-of-the-art research in ML for WAAM. Finally, the challenges and opportunities for using ML in the WAAM process are identified and summarized.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- 1D:
-
One-dimensional
- 2D:
-
Two-dimensional
- 3D:
-
Three-dimensional
- ABR:
-
Adaboostregressor
- ADRC:
-
Active disturbance rejection control
- AE:
-
Arc extinguishing
- AI:
-
Artificial intelligence
- AM:
-
Additive manufacturing
- ANFIS:
-
Adaptive neuro-fuzzy inference system
- ANN:
-
Artificial neural networks
- AS:
-
Arc striking
- BGO:
-
Bio-geography-based optimizer
- BH:
-
Bead height
- BW:
-
Bead width
- CAD:
-
Computer aided design
- CCT:
-
Continuous cooling transformation
- CMES:
-
Covariance matrix adaptation evolution strategy
- CMT:
-
Cold metal transfer
- CNN:
-
Convolutional neural network
- ConvLSTM:
-
Convolutional lstm
- CS:
-
Cuckoo search
- DED:
-
Directed energy deposition
- DfAM:
-
Design for am
- DfWAAM:
-
Design for WAAM
- DL:
-
Deep learning
- DOE:
-
Design of experiments
- DT:
-
Decision tree
- ELM:
-
Extreme learning machines
- EPNet:
-
Efficient patch-based deep network
- FA:
-
Firefly algorithm
- FEA:
-
Finite element analysis
- FNN:
-
Feedforward neural network
- FPGA:
-
Field-programmable gate array
- GA:
-
Genetic algorithm
- GANs:
-
Generative adversarial networks
- GBR:
-
Gradient boosting regressor
- GF:
-
Gas flow rate
- GMAW:
-
Gas metal arc welding
- GPR:
-
Gaussian process regressor
- GRU:
-
Gated recurrent unit
- GTAW:
-
Gas tungsten arc welding
- GWO:
-
Grey wolf optimization
- ICME:
-
Integrated computational materials engineering
- IoT:
-
Internet of things
- IT:
-
Interpass temperature
- KNN:
-
K-nearest neighbor
- LCA:
-
Life-cycle analysis
- LPBF:
-
Laser powder bed fusion
- LPP:
-
Locality-preserving projection
- LR:
-
Linear regression
- LSTM:
-
Long short-term memory
- MFAILC:
-
Model-free adaptive iterative learning control
- ML:
-
Machine learning
- MLP:
-
Multi-layer perceptron
- NoSQL:
-
Not only structured query language
- PAW:
-
Plasma arc welding
- PRT:
-
Pseudo-random ternary
- PSO:
-
Particle swarm optimization
- PSPP:
-
Process-structure-properties-performance
- RBFNN:
-
Radial basis function networks
- ResNet:
-
Residual neural network
- REST:
-
Representational state transfer
- RF:
-
Random forest
- RL:
-
Reinforcement learning
- RNN:
-
Recurrent neural network
- RoI:
-
Region-of-interest
- SEM:
-
Scanning electron microscopy
- STL:
-
Stereolithography
- SVR/SVM:
-
Support vector regressor/machine
- TLBO:
-
Teaching–learning-based optimization
- TS:
-
Travel speed
- VAE:
-
Variational autoencoders
- VGG-16:
-
Convolutional neural network with 16 hidden layers
- WAAM:
-
Wire arc additive manufacturing
- WF:
-
Wire feed rate
- WFS:
-
Wire feed speed
- XGBR:
-
Extreme gradient boosting regressor
- YOLO_v3:
-
You only look once_version 3
References
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., & Vijayanarasimhan, S. (2016). YouTube-8M: A large-scale video classification benchmark. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arxiv.1609.08675
Ahsan, N., Habib, A., & Khoda, B. (2016). Geometric analysis for concurrent process optimization of AM. Procedia Manufacturing, 5, 974–988. https://doi.org/10.1016/J.PROMFG.2016.08.085
Aldalur, E., Veiga, F., Suárez, A., Bilbao, J., & Lamikiz, A. (2020). High deposition wire arc additive manufacturing of mild steel: Strategies and heat input effect on microstructure and mechanical properties. Journal of Manufacturing Processes, 58, 615–626. https://doi.org/10.1016/J.JMAPRO.2020.08.060
Altıparmak, S. C., & Xiao, B. (2021). A market assessment of additive manufacturing potential for the aerospace industry. Journal of Manufacturing Processes, 68, 728–738. https://doi.org/10.1016/J.JMAPRO.2021.05.072
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., et al. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/J.INFFUS.2019.12.012
Barrionuevo, German Omar, Rios, S., Williams, S. W., & Andres Ramos-Grez, J. (2021). Comparative evaluation of machine learning regressors for the layer geometry prediction in wire arc additive manufacturing. Proceedings of 2021 IEEE 12th international conference on mechanical and intelligent manufacturing technologies, ICMIMT 2021, pp. 186–190. https://doi.org/10.1109/ICMIMT52186.2021.9476168
Barrionuevo, G. O., Sequeira-Almeida, P. M., Ríos, S., Ramos-Grez, J. A., & Williams, S. W. (2022). A machine learning approach for the prediction of melting efficiency in wire arc additive manufacturing. International Journal of Advanced Manufacturing Technology, 120(5–6), 3123–3133. https://doi.org/10.1007/S00170-022-08966-Y
Bekker, A. C. M., Verlinden, J. C., & Galimberti, G. (2016). Challenges in assessing the sustainability of wire + arc additive manufacturing for large structures. https://repositories.lib.utexas.edu/handle/2152/89563. Accessed 1 October 2022
Bimbraw, K. (2015). Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. In 12th International conference on informatics in control, automation and robotics (ICINCO) (pp. 191–198).
Bose, S., Biswas, A., Tiwari, Y., Mukherjee, M., & Shekhar Roy, S. (2022). Artificial neural network-based approaches for Bi-directional modelling of robotic wire arc additive manufacturing. Materials Today: Proceedings. https://doi.org/10.1016/J.MATPR.2022.04.331
Chen, H., Yaseer, A., & Zhang, Y. (2022a). Top surface roughness modeling for robotic wire arc additive manufacturing. Journal of Manufacturing and Materials Processing. https://doi.org/10.3390/JMMP6020039
Chen, X., Fu, Y., Kong, F., Li, R., Xiao, Y., Hu, J., & Zhang, H. (2022b). An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing. Rapid Prototyping Journal, 28(3), 573–584. https://doi.org/10.1108/RPJ-02-2021-0034
Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018). Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1), 126–136. https://doi.org/10.1109/MSP.2017.2765695
Cho, H. W., Shin, S. J., Seo, G. J., Kim, D. B., & Lee, D. H. (2022). Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: Molybdenum material. Journal of Materials Processing Technology. https://doi.org/10.1016/J.JMATPROTEC.2022.117495
Chowdhury, S., & Anand, S. (2016). Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes. International Manufacturing Science and Engineering Conference. https://doi.org/10.1115/MSEC2016-8784
Chowdhury, S., Mhapsekar, K., & Anand, S. (2018). Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. Journal of Manufacturing Science and Engineering, Transactions of the ASME,. https://doi.org/10.1115/1.4038293/366667
Cooke, S., Ahmadi, K., Willerth, S., & Herring, R. (2020). Metal additive manufacturing: Technology, metallurgy and modelling. Journal of Manufacturing Processes, 57, 978–1003. https://doi.org/10.1016/J.JMAPRO.2020.07.025
Dass, A., & Moridi, A. (2019). State of the art in directed energy deposition: From additive manufacturing to materials design. Coatings, 9(7), 418. https://doi.org/10.3390/COATINGS9070418
DebRoy, T., Mukherjee, T., Milewski, J. O., Elmer, J. W., Ribic, B., Blecher, J. J., & Zhang, W. (2019). Scientific, technological and economic issues in metal printing and their solutions. Nature Materials, 18(10), 1026–1032. https://doi.org/10.1038/s41563-019-0408-2
DebRoy, T., Wei, H. L., Zuback, J. S., Mukherjee, T., Elmer, J. W., Milewski, J. O., et al. (2018). Additive manufacturing of metallic components—Process, structure and properties. Progress in Materials Science, 92, 112–224. https://doi.org/10.1016/J.PMATSCI.2017.10.001
Decker, N., & Huang, Q. (2019). Geometric accuracy prediction for additive manufacturing through machine learning of triangular mesh data. ASME 2019 14th international manufacturing science and engineering conference, MSEC 2019, “Introduction”. https://doi.org/10.1115/MSEC2019-3050
Decker, N., & Huang, Q. (2020). Intelligent accuracy control service system for small-scale additive manufacturing. Manufacturing Letters, 26, 48–52. https://doi.org/10.1016/J.MFGLET.2020.09.009
Deng, B. L., Li, G., Han, S., Shi, L., & Xie, Y. (2020). Model compression and hardware acceleration for neural networks: A comprehensive survey. Proceedings of the IEEE, 108(4), 485–532. https://doi.org/10.1109/JPROC.2020.2976475
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, Li. (2010). ImageNet: A large-scale hierarchical image database (pp. 248–255). Piscataway: IEEE.
Dhar, A. R., Gupta, D., Paul, A. R., Roy, S. S., & Mukherjee, M. (2021). Hybridized artificial neural network-based expert systems for modelling of robotic-wire and arc additive manufacturing process. Journal of the Institution of Engineers India: Series C, 102(6), 1461–1471. https://doi.org/10.1007/S40032-021-00762-Z
Dharmawan, A. G., Xiong, Y., Foong, S., & Song Soh, G. (2020). A model-based reinforcement learning and correction framework for process control of robotic wire arc additive manufacturing. Proceedings—IEEE international conference on robotics and automation, pp. 4030–4036. https://doi.org/10.1109/ICRA40945.2020.9197222
Dias, M., Pragana, J. P. M., Ferreira, B., Ribeiro, I., & Silva, C. M. A. (2022). Economic and environmental potential of wire-arc additive manufacturing. Sustainability, 14(9), 5197. https://doi.org/10.3390/SU14095197
Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dolen, M. (2017). The role of additive manufacturing in the era of industry 4.0. Procedia Manufacturing, 11, 545–554. https://doi.org/10.1016/J.PROMFG.2017.07.148
Ding, D., He, F., Yuan, L., Pan, Z., Wang, L., & Ros, M. (2021). The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration. Journal of Industrial Information Integration. https://doi.org/10.1016/J.JII.2021.100218
Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018). Explainable artificial intelligence: A survey. 2018 41st International convention on information and communication technology, electronics and microelectronics, MIPRO 2018—Proceedings, pp. 210–215. https://doi.org/10.23919/MIPRO.2018.8400040
Du, Y., Mukherjee, T., & DebRoy, T. (2021). Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects. Applied Materials Today, 24, 101123. https://doi.org/10.1016/J.APMT.2021.101123
Everton, S. K., Hirsch, M., Stavroulakis, P. I., Leach, R. K., & Clare, A. T. (2016). Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design, 95, 431–445. https://doi.org/10.1016/J.MATDES.2016.01.099
Farias, F. W. C., da Filho, J. C. P., & Moraes Oliveira, V. H. P. (2021). Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network. Additive Manufacturing. https://doi.org/10.1016/J.ADDMA.2021.102387
Frazier, W. E. (2014). Metal additive manufacturing: A review. Journal of Materials Engineering and Performance, 23(6), 1917–1928. https://doi.org/10.1007/S11665-014-0958-Z/FIGURES/9
Fu, Y., Downey, A. R. J., Yuan, L., Zhang, T., Pratt, A., & Balogun, Y. (2022). Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 75, 693–710. https://doi.org/10.1016/J.JMAPRO.2021.12.061
Geng, R., Du, J., Wei, Z., Xu, S., & Ma, N. (2021). Modelling and experimental observation of the deposition geometry and microstructure evolution of aluminum alloy fabricated by wire-arc additive manufacturing. Journal of Manufacturing Processes, 64, 369–378. https://doi.org/10.1016/J.JMAPRO.2021.01.037
Gibson, I., Rosen, D., Stucker, B., & Khorasani, M. (2021). Design for additive manufacturing. Additive Manufacturing Technologies. https://doi.org/10.1007/978-3-030-56127-7_19
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139–144. https://doi.org/10.1145/3422622
Gorsse, S., Hutchinson, C., Gouné, M., & Banerjee, R. (2017). Additive manufacturing of metals: A brief review of the characteristic microstructures and properties of steels, Ti-6Al-4V and high-entropy alloys. Science and Technology of Advanced Materials, 18(1), 584–610. https://doi.org/10.1080/14686996.2017.1361305
Guo, S., Agarwal, M., Cooper, C., Tian, Q., Gao, R. X., Guo, W. G., & Guo, Y. B. (2022). Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm. Journal of Manufacturing Systems, 62, 145–163. https://doi.org/10.1016/J.JMSY.2021.11.003
Guo, Y., Zhao, Z., Han, J., & Bai, L. (2018). Quality monitoring in wire-arc additive manufacturing based on spectrum. ACM International Conference Proceeding Series. https://doi.org/10.1145/3301506.3301534
Hackenhaar, W., Mazzaferro, J. A. E., Montevecchi, F., & Campatelli, G. (2020). An experimental-numerical study of active cooling in wire arc additive manufacturing. Journal of Manufacturing Processes, 52, 58–65. https://doi.org/10.1016/J.JMAPRO.2020.01.051
Hamrani, A., Akbarzadeh, A., & Madramootoo, C. A. (2020). Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of the Total Environment, 741, 140338. https://doi.org/10.1016/J.SCITOTENV.2020.140338
Hashemi, S. M., Parvizi, S., Baghbanijavid, H., Tan, A. T. L., Nematollahi, M., Ramazani, A., et al. (2021). Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: A review. International Materials Reviews, 67(1), 1–46. https://doi.org/10.1080/09506608.2020.1868889
Herzog, D., Seyda, V., Wycisk, E., & Emmelmann, C. (2016). Additive manufacturing of metals. Acta Materialia, 117, 371–392. https://doi.org/10.1016/J.ACTAMAT.2016.07.019
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266.
Hossain, R. E. N., Lewis, J., & Moore, A. L. (2021). In situ infrared temperature sensing for real-time defect detection in additive manufacturing. Additive Manufacturing, 47, 102328. https://doi.org/10.1016/J.ADDMA.2021.102328
Hou, X., Shen, L., Sun, K., & Qiu, G. (2017). Deep feature consistent variational autoencoder. Proceedings—2017 IEEE winter conference on applications of computer vision, WACV 2017, pp. 1133–1141. https://doi.org/10.1109/WACV.2017.131
Hu, F., Liu, Y., Qin, J., Sun, X., & Witherell, P. (2020). Feature-level data fusion for energy consumption analytics in additive manufacturing. IEEE International Conference on Automation Science and Engineering. https://doi.org/10.1109/CASE48305.2020.9216947
Huang, R., Riddle, M., Graziano, D., Warren, J., Das, S., Nimbalkar, S., et al. (2016). Energy and emissions saving potential of additive manufacturing: The case of lightweight aircraft components. Journal of Cleaner Production, 135, 1559–1570. https://doi.org/10.1016/J.JCLEPRO.2015.04.109
Ingarao, G., & Priarone, P. C. (2020). A comparative assessment of energy demand and life cycle costs for additive- and subtractive-based manufacturing approaches. Journal of Manufacturing Processes, 56, 1219–1229. https://doi.org/10.1016/J.JMAPRO.2020.06.009
Jafari, D., Vaneker, T. H. J., & Gibson, I. (2021). Wire and arc additive manufacturing: Opportunities and challenges to control the quality and accuracy of manufactured parts. Materials & Design, 202, 109471. https://doi.org/10.1016/J.MATDES.2021.109471
Jiang, J., Xiong, Y., Zhang, Z., & Rosen, D. W. (2020). Machine learning integrated design for additive manufacturing. Journal of Intelligent Manufacturing, 33(4), 1073–1086. https://doi.org/10.1007/S10845-020-01715-6
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422–440. https://doi.org/10.1038/s42254-021-00314-5
Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M. A., & Bian, L. (2018). Porosity prediction: Supervised-learning of thermal history for direct laser deposition. Journal of Manufacturing Systems, 47, 69–82. https://doi.org/10.1016/J.JMSY.2018.04.001
Ko, H., Witherell, P., Lu, Y., Kim, S., & Rosen, D. W. (2021). Machine learning and knowledge graph based design rule construction for additive manufacturing. Additive Manufacturing, 37, 101620. https://doi.org/10.1016/J.ADDMA.2020.101620
Koeppe, A., Hernandez Padilla, C. A., Voshage, M., Schleifenbaum, J. H., & Markert, B. (2018). Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks. Manufacturing Letters, 15, 147–150. https://doi.org/10.1016/J.MFGLET.2018.01.002
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 60, 84.
Kumar, A., & Maji, K. (2020). Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm. Journal of Materials Engineering and Performance, 29(5), 3334–3352. https://doi.org/10.1007/S11665-020-04847-1/FIGURES/21
Kumar, N., Bhavsar, H., Mahesh, P. V. S., Srivastava, A. K., Bora, B. J., Saxena, A., & Dixit, A. R. (2022). Wire arc additive manufacturing—A revolutionary method in additive manufacturing. Materials Chemistry and Physics, 285, 126144. https://doi.org/10.1016/J.MATCHEMPHYS.2022.126144
Kumke, M., Watschke, H., & Vietor, T. (2016). A new methodological framework for design for additive manufacturing. Virtual and Physical Prototyping, 11(1), 3–19. https://doi.org/10.1080/17452759.2016.1139377
Kunchala, B. K. R., Gamini, S., & Anilkumar, T. C. (2022). Inclusion of IoT technology in additive manufacturing: Machine learning-based adaptive bead modeling and path planning for sustainable wire arc additive manufacturing and process optimization. Proceedings of the Institution of Mechanical Engineers, Part c: Journal of Mechanical Engineering Science. https://doi.org/10.1177/09544062221117660
Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G. H., Cho, J. H., et al. (2020). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing, 31(2), 375–386. https://doi.org/10.1007/S10845-018-1451-6/FIGURES/9
Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/S12599-014-0334-4
Le, V. T., Nguyen, H. D., Bui, M. C., Pham, T. Q. D., Le, H. T., Tran, V. X., & Tran, H. S. (2022). Rapid and accurate prediction of temperature evolution in wire plus arc additive manufacturing using feedforward neural network. Manufacturing Letters, 32, 28–31. https://doi.org/10.1016/J.MFGLET.2022.02.003
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2323. https://doi.org/10.1109/5.726791
Lee, C., Seo, G., Kim, D., Kim, M., & Shin, J. H. (2021). Development of defect detection ai model for wire + arc additive manufacturing using high dynamic range images. Applied Sciences (switzerland), 11(16), 7541. https://doi.org/10.3390/APP11167541
Lehmann, T., Jain, A., Jain, Y., Stainer, H., Wolfe, T., Henein, H., & Qureshi, A. J. (2020). Concurrent geometry- and material-based process identification and optimization for robotic CMT-based wire arc additive manufacturing. Materials & Design, 194, 108841. https://doi.org/10.1016/J.MATDES.2020.108841
Lew, A. J., & Buehler, M. J. (2021). Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model. Forces in Mechanics, 5, 100054. https://doi.org/10.1016/J.FINMEC.2021.100054
Lewandowski, J. J., & Seifi, M. (2016). Metal additive manufacturing: A review of mechanical properties. Annual Review of Materials Researc, 46, 151–186. https://doi.org/10.1146/ANNUREV-MATSCI-070115-032024
Li, Y., Mu, H., Polden, J., Li, H., Wang, L., Xia, C., & Pan, Z. (2022b). Towards intelligent monitoring system in wire arc additive manufacturing: A surface anomaly detector on a small dataset. International Journal of Advanced Manufacturing Technology, 120(7–8), 5225–5242. https://doi.org/10.1007/S00170-022-09076-5
Li, Y., Polden, J., Pan, Z., Cui, J., Xia, C., He, F., et al. (2022c). A defect detection system for wire arc additive manufacturing using incremental learning. Journal of Industrial Information Integration. https://doi.org/10.1016/J.JII.2021.100291
Li, Y., Su, C., & Zhu, J. (2022a). Comprehensive review of wire arc additive manufacturing: Hardware system, physical process, monitoring, property characterization, application and future prospects. Results in Engineering, 13, 100330. https://doi.org/10.1016/J.RINENG.2021.100330
Li, Y., Sun, Y., Han, Q., Zhang, G., & Horváth, I. (2018). Enhanced beads overlapping model for wire and arc additive manufacturing of multi-layer multi-bead metallic parts. Journal of Materials Processing Technology, 252, 838–848. https://doi.org/10.1016/J.JMATPROTEC.2017.10.017
Liu, R., Liu, S., & Zhang, X. (2021). A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. International Journal of Advanced Manufacturing Technology, 113(7–8), 1943–1958. https://doi.org/10.1007/S00170-021-06640-3/FIGURES/15
Lu, Y., Witherell, P., & Donmez, A. (2017). A collaborative data management system for additive manufacturing. Proceedings of the ASME Design Engineering Technical Conference. https://doi.org/10.1115/DETC2017-68457
Majeed, A., Zhang, Y., Ren, S., Lv, J., Peng, T., Waqar, S., & Yin, E. (2021). A big data-driven framework for sustainable and smart additive manufacturing. Robotics and Computer-Integrated Manufacturing, 67, 102026. https://doi.org/10.1016/J.RCIM.2020.102026
Makhzani, A., Shlens, J., Jaitly, N., Brain, G., Openai, I. G., & Frey, B. (2015). Adversarial autoencoders. Machine Learning. https://doi.org/10.48550/arxiv.1511.05644
Mattera, G., Nele, L., & Paolella, D. (2023). Monitoring and control the wire arc additive manufacturing process using artificial intelligence techniques: A review. Journal of Intelligent Manufacturing. https://doi.org/10.1007/S10845-023-02085-5/FIGURES/1
Maurya, A. K., Yeom, J. T., Kang, S. W., Park, C. H., Hong, J. K., & Reddy, N. S. (2022). Optimization of hybrid manufacturing process combining forging and wire-arc additive manufactured Ti-6Al-4V through hot deformation characterization. Journal of Alloys and Compounds. https://doi.org/10.1016/J.JALLCOM.2021.162453
McGregor, D. J., Bimrose, M. V., Shao, C., Tawfick, S., & King, W. P. (2022). Using machine learning to predict dimensions and qualify diverse part designs across multiple additive machines and materials. Additive Manufacturing, 55, 102848. https://doi.org/10.1016/J.ADDMA.2022.102848
Milewski, J. O. (2017). Additive manufacturing metal, the art of the possible. Springer Series in Materials Science, 258, 7–33. https://doi.org/10.1007/978-3-319-58205-4_2
Mishra, V., Ayas, C., Langelaar, M., & van Keulen, F. (2022). Simultaneous topology and deposition direction optimization for wire and arc additive manufacturing. Manufacturing Letters, 31, 45–51. https://doi.org/10.1016/J.MFGLET.2021.05.011
Montevecchi, F., Venturini, G., Scippa, A., & Campatelli, G. (2016). Finite element modelling of wire-arc-additive-manufacturing process. Procedia CIRP, 55, 109–114. https://doi.org/10.1016/J.PROCIR.2016.08.024
Morell, A., Cano, J.-C., Hsu, T.-H., Wang, Z.-H., & See, A. R. (2022). A cloud-edge-smart IoT architecture for speeding up the deployment of neural network models with transfer learning techniques. Electronics, 11(14), 2255. https://doi.org/10.3390/ELECTRONICS11142255
Motaman, S. A. H., Kies, F., Köhnen, P., Létang, M., Lin, M., Molotnikov, A., & Haase, C. (2020). Optimal design for metal additive manufacturing: An integrated computational materials engineering (ICME) approach. JOM Journal of the Minerals Metals and Materials Society, 72(3), 1092–1104. https://doi.org/10.1007/S11837-020-04028-4
Mukherjee, T., Manvatkar, V., De, A., & DebRoy, T. (2017). Mitigation of thermal distortion during additive manufacturing. Scripta Materialia, 127, 79–83. https://doi.org/10.1016/J.SCRIPTAMAT.2016.09.001
Nalajam, P. K., & Ramesh, V. (2021). Microstructural porosity segmentation using machine learning techniques in wire-based direct energy deposition of AA6061. Micron. https://doi.org/10.1016/J.MICRON.2021.103161
Nalajam, P. K., & Varadarajan, R. (2021). A hybrid deep learning model for layer-wise melt pool temperature forecasting in wire-arc additive manufacturing process. IEEE Access, 9, 100652–100664. https://doi.org/10.1109/ACCESS.2021.3097177
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review. IEEE Access, 7, 19143–19165. https://doi.org/10.1109/ACCESS.2019.2896880
Naveen Srinivas, M., Vimal, K. E. K., Manikandan, N., & Sritharanandh, G. (2022). Parametric optimization and multiple regression modelling for fabrication of aluminium alloy thin plate using wire arc additive manufacturing. International Journal on Interactive Design and Manufacturing (IJIDeM), 2022, 1–11. https://doi.org/10.1007/S12008-022-00921-1
Ness, K. L., Paul, A., Sun, L., & Zhang, Z. (2022). Towards a generic physics-based machine learning model for geometry invariant thermal history prediction in additive manufacturing. Journal of Materials Processing Technology, 302, 117472. https://doi.org/10.1016/J.JMATPROTEC.2021.117472
Ng, A. Y. (2004). Feature selection, L 1 vs. L 2 regularization, and rotational invariance. Twenty-first international conference on machine learning—ICML ’04. https://doi.org/10.1145/1015330
Nguyen, L., Buhl, J., & Bambach, M. (2020a). Continuous Eulerian tool path strategies for wire-arc additive manufacturing of rib-web structures with machine-learning-based adaptive void filling. Additive Manufacturing. https://doi.org/10.1016/J.ADDMA.2020.101265
Nguyen, L., Buhl, J., & Bambach, M. (2020b). Continuous Eulerian tool path strategies for wire-arc additive manufacturing of rib-web structures with machine-learning-based adaptive void filling. Additive Manufacturing, 35, 101265. https://doi.org/10.1016/J.ADDMA.2020.101265
Ou, W., Mukherjee, T., Knapp, G. L., Wei, Y., & DebRoy, T. (2018). Fusion zone geometries, cooling rates and solidification parameters during wire arc additive manufacturing. International Journal of Heat and Mass Transfer, 127, 1084–1094. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2018.08.111
Ou, W., Wei, Y., Liu, R., Zhao, W., & Cai, J. (2020). Determination of the control points for circle and triangle route in wire arc additive manufacturing (WAAM). Journal of Manufacturing Processes, 53, 84–98. https://doi.org/10.1016/J.JMAPRO.2020.02.003
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
Pei, K., Cao, Y., Yang, J., & Jana, S. (2017). DeepXplore: Automated whitebox testing of deep learning systems. Proceedings of the ACM Symposium on Operating Systems Principles, 17, 1–18. https://doi.org/10.1145/3132747.3132785
Petrich, J., Snow, Z., Corbin, D., & Reutzel, E. W. (2021). Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing. Additive Manufacturing, 48, 102364. https://doi.org/10.1016/J.ADDMA.2021.102364
Petrik, J., Sydow, B., & Bambach, M. (2022). Beyond parabolic weld bead models: AI-based 3D reconstruction of weld beads under transient conditions in wire-arc additive manufacturing. Journal of Materials Processing Technology. https://doi.org/10.1016/J.JMATPROTEC.2021.117457
Priarone, P. C., Pagone, E., Martina, F., Catalano, A. R., & Settineri, L. (2020). Multi-criteria environmental and economic impact assessment of wire arc additive manufacturing. CIRP Annals, 69(1), 37–40. https://doi.org/10.1016/J.CIRP.2020.04.010
Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5(4), 721–729. https://doi.org/10.1016/J.ENG.2019.04.012
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., et al. (2022a). Research and application of machine learning for additive manufacturing. Additive Manufacturing, 52, 102691. https://doi.org/10.1016/J.ADDMA.2022.102691
Qin, J., Liu, Y., & Grosvenor, R. (2018). Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics, 38, 840–850. https://doi.org/10.1016/J.AEI.2018.10.008
Qin, J., Wang, Y., Ding, J., & Williams, S. (2022b). Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning. Journal of Intelligent Manufacturing, 33(7), 2179–2191. https://doi.org/10.1007/S10845-022-01986-1/FIGURES/8
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ questions for machine comprehension of text. EMNLP 2016—conference on empirical methods in natural language processing, proceedings, pp. 2383–2392. https://doi.org/10.48550/arxiv.1606.05250
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. Npj Computational Materials, 3(1), 1–13. https://doi.org/10.1038/s41524-017-0056-5
Reimann, J., Hammer, S., Henckell, P., Rohe, M., Ali, Y., Rauch, A., et al. (2021). Directed energy deposition-arc (Ded-arc) and numerical welding simulation as a hybrid data source for future machine learning applications. Applied Sciences (switzerland), 11(15), 7075. https://doi.org/10.3390/APP11157075
Reisch, R., Hauser, T., Lutz, B., Pantano, M., Kamps, T., & Knoll, A. (2020). Distance-based multivariate anomaly detection in wire arc additive manufacturing. Proceedings—19th IEEE international conference on machine learning and applications, ICMLA 2020, pp. 659–664. https://doi.org/10.1109/ICMLA51294.2020.00109
Reisch, R. T., Hauser, T., Franke, J., Heinrich, F., Theodorou, K., Kamps, T., & Knoll, A. (2021). Nozzle-to-work distance measurement and control in wire arc additive manufacturing. ACM International Conference Proceeding Series. https://doi.org/10.1145/3501774.3501798
Reisch, R. T., Hauser, T., Lutz, B., Tsakpinis, A., Winter, D., Kamps, T., & Knoll, A. (2022). Context awareness in process monitoring of additive manufacturing using a digital twin. International Journal of Advanced Manufacturing Technology, 119(5–6), 3483–3500. https://doi.org/10.1007/S00170-021-08636-5
Ren, K., Chew, Y., Zhang, Y. F., Fuh, J. Y. H., & Bi, G. J. (2020). Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning. Computer Methods in Applied Mechanics and Engineering, 362, 112734. https://doi.org/10.1016/J.CMA.2019.112734
Ren, W., Wen, G., Zhang, Z., & Mazumder, J. (2021). Quality monitoring in additive manufacturing using emission spectroscopy and unsupervised deep learning. Materials and Manufacturing Processes, 37(11), 1339–1346. https://doi.org/10.1080/10426914.2021.1906891
Ruiz, C., Jafari, D., Venkata Subramanian, V., Vaneker, T. H. J., Ya, W., & Huang, Q. (2022). Prediction and control of product shape quality for wire and arc additive manufacturing. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4054721
Sames, W. J., List, F. A., Pannala, S., Dehoff, R. R., & Babu, S. S. (2016). The metallurgy and processing science of metal additive manufacturing. International Materials Reviews, 61(5), 315–360. https://doi.org/10.1080/09506608.2015.1116649
Scheck, M., Franz, J., Richter, A., Gehling, T., Treutler, K., Beitler, S., et al. (2022). Identification and modeling of wire arc additive manufacturing under consideration of interpass temperature. 2022 13th UKACC international conference on control, Control, pp. 219–225. https://doi.org/10.1109/CONTROL55989.2022.9781450
Shen, B., Lu, J., Wang, Y., Chen, D., Han, J., Zhang, Y., & Zhao, Z. (2022). Multimodal-based weld reinforcement monitoring system for wire arc additive manufacturing. Journal of Materials Research and Technology, 20, 561–571. https://doi.org/10.1016/J.JMRT.2022.07.086
Silwal, B., Pudasaini, N., Roy, S., Murphy, A. B., Nycz, A., & Noakes, M. W. (2022). Altering the supply of shielding gases to fabricate distinct geometry in GMA additive manufacturing. Applied Sciences, 12(7), 3679. https://doi.org/10.3390/APP12073679
Singh, S., Ramakrishna, S., & Singh, R. (2017). Material issues in additive manufacturing: A review. Journal of Manufacturing Processes, 25, 185–200. https://doi.org/10.1016/J.JMAPRO.2016.11.006
Srivastava, N., Hinton, G., Krizhevsky, A., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56), 1929–1958.
Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79. https://doi.org/10.1007/S11192-010-0259-8/FIGURES/3
Tang, S., Wang, G., Huang, C., Li, R., Zhou, S., & Zhang, H. (2020). Investigation, modeling and optimization of abnormal areas of weld beads in wire and arc additive manufacturing. Rapid Prototyping Journal, 26(7), 1183–1195. https://doi.org/10.1108/RPJ-08-2019-0229/FULL/PDF
Tang, S., Wang, G., Song, H., Li, R., & Zhang, H. (2021). A novel method of bead modeling and control for wire and arc additive manufacturing. Rapid Prototyping Journal, 27(2), 311–320. https://doi.org/10.1108/RPJ-05-2020-0097
Tang, Y., Dong, G., Zhou, Q., & Zhao, Y. F. (2018). Lattice structure design and optimization with additive manufacturing constraints. IEEE Transactions on Automation Science and Engineering, 15(4), 1546–1562. https://doi.org/10.1109/TASE.2017.2685643
Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., et al. (2016). Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals, 65(2), 737–760. https://doi.org/10.1016/J.CIRP.2016.05.004
Van Den, A., Deepmind, O. G., Kalchbrenner, N., Deepmind, G., Vinyals, O., Espeholt, L., et al. (2016). Conditional image generation with pixelCNN decoders. Advances in Neural Information Processing Systems, 29, 1.
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/S11192-009-0146-3/FIGURES/7
Wacker, C., Köhler, M., David, M., Aschersleben, F., Gabriel, F., Hensel, J., et al. (2021). Geometry and distortion prediction of multiple layers for wire arc additive manufacturing with artificial neural networks. Applied Sciences (switzerland), 11(10), 4694. https://doi.org/10.3390/APP11104694
Waltman, L., van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/J.JOI.2010.07.002
Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020a). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538. https://doi.org/10.1016/J.ADDMA.2020.101538
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/J.JMSY.2018.01.003
Wang, K., Song, Y., Huang, Z., Sun, Y., Xu, J., & Zhang, S. (2022). Additive manufacturing energy consumption measurement and prediction in fabricating lattice structure based on recallable multimodal fusion network. Measurement, 196, 111215. https://doi.org/10.1016/J.MEASUREMENT.2022.111215
Wang, Y., Lu, J., Zhao, Z., Deng, W., Han, J., Bai, L., et al. (2021a). Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning. Journal of Manufacturing Processes, 67, 364–375. https://doi.org/10.1016/J.JMAPRO.2021.05.005
Wang, Y., Xu, X., Zhao, Z., Deng, W., Han, J., Bai, L., et al. (2021b). Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process. Journal of Manufacturing Processes, 71, 306–316. https://doi.org/10.1016/J.JMAPRO.2021.09.033
Wang, Y., Zhang, C., Lu, J., Bai, L., Zhao, Z., & Han, J. (2020b). Weld reinforcement analysis based on long-term prediction of molten pool image in additive manufacturing. IEEE Access, 8, 69908–69918. https://doi.org/10.1109/ACCESS.2020.2986130
Wang, Z., Zimmer-Chevret, S., Léonard, F., & Abba, G. (2021c). Prediction of bead geometry with consideration of interlayer temperature effect for CMT-based wire-arc additive manufacturing. Welding in the World, 65(12), 2255–2266. https://doi.org/10.1007/S40194-021-01192-2
Weiss, K., Khoshgoftaar, T. M., & Wang, D. D. (2016). A survey of transfer learning. Journal of Big Data, 3(1), 1–40. https://doi.org/10.1186/S40537-016-0043-6/TABLES/6
Wiberg, A., Persson, J., & Ölvander, J. (2019). Design for additive manufacturing—A review of available design methods and software. Rapid Prototyping Journal, 25(6), 1080–1094. https://doi.org/10.1108/RPJ-10-2018-0262/FULL/PDF
Williams, S. W., Martina, F., Addison, A. C., Ding, J., Pardal, G., & Colegrove, P. (2016). Wire + arc additive manufacturing. Materials Science and Technology, 32(7), 641–647. https://doi.org/10.1179/1743284715Y.0000000073
Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., & Norrish, J. (2018). A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement. Journal of Manufacturing Processes, 35, 127–139. https://doi.org/10.1016/J.JMAPRO.2018.08.001
Wu, Q., Mukherjee, T., De, A., & DebRoy, T. (2020). Residual stresses in wire-arc additive manufacturing—Hierarchy of influential variables. Additive Manufacturing. https://doi.org/10.1016/J.ADDMA.2020.101355
Wu, Q., Mukherjee, T., Liu, C., Lu, J., & DebRoy, T. (2019). Residual stresses and distortion in the patterned printing of titanium and nickel alloys. Additive Manufacturing, 29, 100808. https://doi.org/10.1016/J.ADDMA.2019.100808
Xames, M. D., Torsha, F. K., & Sarwar, F. (2023). A systematic literature review on recent trends of machine learning applications in additive manufacturing. Journal of Intelligent Manufacturing, 34, 2529–2555. https://doi.org/10.1007/S10845-022-01957-6
Xia, C., Pan, Z., Li, Y., Chen, J., & Li, H. (2022a). Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. International Journal of Advanced Manufacturing Technology, 120(1–2), 551–562. https://doi.org/10.1007/S00170-022-08811-2
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S. (2022b). Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing, 33(5), 1467–1482. https://doi.org/10.1007/S10845-020-01725-4
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., Chen, S., & Zhang, Y. (2020a). A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system. Journal of Manufacturing Systems, 57, 31–45. https://doi.org/10.1016/J.JMSY.2020.08.008
Xia, C., Pan, Z., Zhang, S., Li, H., Xu, Y., & Chen, S. (2020b). Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing. International Journal of Advanced Manufacturing Technology, 110(7–8), 2131–2142. https://doi.org/10.1007/S00170-020-05998-0
Xia, C., Pan, Z., Zhang, S., Polden, J., Li, H., Xu, Y., & Chen, S. (2020c). Mask R-CNN-based welding image object detection and dynamic modelling for WAAM. Transactions on Intelligent Welding Manufacturing. https://doi.org/10.1007/978-981-15-7215-9_4/FIGURES/14
Xiao, X., Waddell, C., Hamilton, C., & Xiao, H. (2022). Quality prediction and control in wire arc additive manufacturing via novel machine learning framework. Micromachines, 13(1), 137. https://doi.org/10.3390/MI13010137
Xiao, Y., & Watson, M. (2017). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Xue, Q., Ma, S., Liang, Y., Wang, J., Wang, Y., He, F., & Liu, M. (2018). Weld bead geometry prediction of additive manufacturing based on neural network. Proceedings International Symposium on Computational Intelligence and Design, 2, 47–51. https://doi.org/10.1109/ISCID.2018.10112
Yao, X., Moon, S. K., & Bi, G. (2017). A hybrid machine learning approach for additive manufacturing design feature recommendation. Rapid Prototyping Journal, 23(6), 983–997. https://doi.org/10.1108/RPJ-03-2016-0041/FULL/PDF
Yaseer, A., Chen, H., & Zhang, B. (2021). Predicting layer roughness with weaving path in robotic wire arc additive manufacturing using multilayer perceptron. 2021 IEEE 11th Annual international conference on CYBER technology in automation, control, and intelligent systems, CYBER 2021, pp. 61–66. https://doi.org/10.1109/CYBER53097.2021.9588272
Yaseer, A., & Chen, H. (2021). Machine learning based layer roughness modeling in robotic additive manufacturing. Journal of Manufacturing Processes, 70, 543–552. https://doi.org/10.1016/J.JMAPRO.2021.08.056
Yusuf, S. M., & Gao, N. (2017). Influence of energy density on metallurgy and properties in metal additive manufacturing. Materials Science and Technology, 33(11), 1269–1289. https://doi.org/10.1080/02670836.2017.1289444
Zhang, X., Le, X., Panotopoulou, A., Whiting, E., & Wang, C. C. L. (2015). Perceptual models of preference in 3D printing direction. ACM Transactions on Graphics (TOG), 34(6), 1–12. https://doi.org/10.1145/2816795.2818121
Zhang, Y., Harik, R., Fadel, G., & Bernard, A. (2019). A statistical method for build orientation determination in additive manufacturing. Rapid Prototyping Journal, 25(1), 187–207. https://doi.org/10.1108/RPJ-04-2018-0102/FULL/PDF
Zhou, Z., Shen, H., Liu, B., Du, W., & Jin, J. (2021). Thermal field prediction for welding paths in multi-layer gas metal arc welding-based additive manufacturing: A machine learning approach. Journal of Manufacturing Processes, 64, 960–971. https://doi.org/10.1016/J.JMAPRO.2021.02.033
Zhou, Z., Shen, H., Liu, B., Du, W., Jin, J., & Lin, J. (2022). Residual thermal stress prediction for continuous tool-paths in wire-arc additive manufacturing: A three-level data-driven method. Virtual and Physical Prototyping, 17(1), 105–124. https://doi.org/10.1080/17452759.2021.1997259
Zhu, Q., Liu, Z., & Yan, J. (2021). Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Computational Mechanics, 67(2), 619–635. https://doi.org/10.1007/S00466-020-01952-9/TABLES/5
Zhu, Z., Anwer, N., Huang, Q., & Mathieu, L. (2018). Machine learning in tolerancing for additive manufacturing. CIRP Annals, 67(1), 157–160. https://doi.org/10.1016/J.CIRP.2018.04.119
Zhu, Z., Ferreira, K., Anwer, N., Mathieu, L., Guo, K., & Qiao, L. (2020). Convolutional neural network for geometric deviation prediction in additive manufacturing. Procedia CIRP, 91, 534–539. https://doi.org/10.1016/J.PROCIR.2020.03.108
Acknowledgements
Arvind Agarwal acknowledges the financial support of DEVCOM—Army Research Laboratory (ARL) Grant W911NF2020256.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hamrani, A., Agarwal, A., Allouhi, A. et al. Applying machine learning to wire arc additive manufacturing: a systematic data-driven literature review. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02171-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10845-023-02171-8