A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

• Abu-Mostafa, Y. S. (1995). Hints. Neural Computation, 7(4), 639–671.

  Article  Google Scholar 

• Adnan, M., Lu, Y., Jones, A., Cheng, F.-T., & Yeung, H. (2020). A new architectural approach to monitoring and controlling AM processes. Applied Sciences, 10(18), 6616.

  Article  Google Scholar 

• Aladesanmi, V., Fatoba, O., Akinlabi, E., & Ikumapayi, O. (2021). Regression analysis of hardness property of laser additive manufactured (LAM) Ti and TiB2 metal matrix composite. Materials Today: Proceedings, 44, 1249–1253.

  Google Scholar 

• Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102–4107.

  Google Scholar 

• Alejandrino, J. D., Concepcion, R. S., II., Lauguico, S. C., Tobias, R. R., Venancio, L., Macasaet, D., Bandala, A. A., & Dadios, E. P. (2020). A machine learning approach of lattice infill pattern for increasing material efficiency in additive manufacturing processes. International Journal of Mechanical Engineering and Robotics Research, 9(9), 1253–1263.

  Article  Google Scholar 

• Aljarrah, O., Li, J., Heryudono, A., Huang, W., & Bi, J. (2022). Predicting part distortion field in additive manufacturing: A data-driven framework. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01902-z

  Article  Google Scholar 

• Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28, 167–182.

  Article  Google Scholar 

• Amini, M., & Chang, S. I. (2018). MLCPM: A process monitoring framework for 3D metal printing in industrial scale. Computers & Industrial Engineering, 124, 322–330.

  Article  Google Scholar 

• Aminzadeh, M., & Kurfess, T. R. (2019). Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30(6), 2505–2523.

  Article  Google Scholar 

• Ayers, M. (2008). DOE data explorer: The data. Issues in Science and Technology Librarianship. https://doi.org/10.29173/istl2466

  Article  Google Scholar 

• Bai, M., Liu, J., Chai, J., Zhao, X., & Yu, D. (2020). Anomaly detection of gas turbines based on normal pattern extraction. Applied Thermal Engineering, 166, 114664.

  Article  Google Scholar 

• Bandyopadhyay, A., Zhang, Y., & Bose, S. (2020). Recent developments in metal additive manufacturing. Current Opinion in Chemical Engineering, 28, 96–104.

  Article  Google Scholar 

• Bartlett, J. L., Jarama, A., Jones, J., & Li, X. (2020). Prediction of microstructural defects in additive manufacturing from powder bed quality using digital image correlation. Materials Science and Engineering: A, 794, 140002.

  Article  Google Scholar 

• Bastani, K., Rao, P. K., & Kong, Z. (2016). An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data. IIE Transactions, 48(7), 579–598.

  Article  Google Scholar 

• Baturynska, I. (2019). Application of machine learning techniques to predict the mechanical properties of polyamide 2200 (PA12) in additive manufacturing. Applied Sciences, 9(6), 1060.

  Article  Google Scholar 

• Baturynska, I., & Martinsen, K. (2021). Prediction of geometry deviations in additive manufactured parts: Comparison of linear regression with machine learning algorithms. Journal of Intelligent Manufacturing, 32(1), 179–200.

  Article  Google Scholar 

• Baumgartl, H., Tomas, J., Buettner, R., & Merkel, M. (2020). A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Progress in Additive Manufacturing. https://doi.org/10.1007/s40964-019-00108-3

  Article  Google Scholar 

• Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.

  Article  Google Scholar 

• Bessa, M. A., Glowacki, P., & Houlder, M. (2019). Bayesian machine learning in metamaterial design: Fragile becomes supercompressible. Advanced Materials, 31(48), 1904845.

  Article  Google Scholar 

• Bhoi, N. K. (2018). Mendeley data repository as a platform for research data management. In B. Rautaray, D. K. Swain, & C. Swain (Eds.), Marching beyond libraries: Managerial skills and technological competencies (pp. 481–487). KIIT Deemed to be University, Bhubaneswar in association with Overseas Press India Pvt. Ltd.

• Bisheh, M. N., Chang, S. I., & Lei, S. (2021). A layer-by-layer quality monitoring framework for 3D printing. Computers & Industrial Engineering, 157, 107314.

  Article  Google Scholar 

• Brase, J. (2009). DataCite—A global registration agency for research data. In 2009 fourth international conference on cooperation and promotion of information resources in science and technology (pp. 257–261). IEEE.

• Caggiano, A., Teti, R., Alfieri, V., & Caiazzo, F. (2021). Automated laser polishing for surface finish enhancement of additive manufactured components for the automotive industry. Production Engineering, 15(1), 109–117.

  Article  Google Scholar 

• Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., & Teti, R. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals, 68(1), 451–454.

  Article  Google Scholar 

• Caiazzo, F., & Caggiano, A. (2018). Laser direct metal deposition of 2024 Al alloy: Trace geometry prediction via machine learning. Materials, 11(3), 444.

  Article  Google Scholar 

• Chaki, J., & Dey, N. (2018). A beginner’s guide to image preprocessing techniques. CRC Press.

  Book  Google Scholar 

• Chan, S. L., Lu, Y., & Wang, Y. (2018). Data-driven cost estimation for additive manufacturing in cyber manufacturing. Journal of Manufacturing Systems, 46, 115–126.

  Article  Google Scholar 

• Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., & Su, H. (2015). Shapenet: An information-rich 3D model repository. arXiv Preprint. https://arxiv.org/abs/1512.03012

• Chang, T.-W., Liao, K.-W., Lin, C.-C., Tsai, M.-C., & Cheng, C.-W. (2021). Predicting magnetic characteristics of additive manufactured soft magnetic composites by machine learning. The International Journal of Advanced Manufacturing Technology, 114(9), 3177–3184.

  Article  Google Scholar 

• Chen, L., Yao, X., Xu, P., Moon, S. K., & Bi, G. (2021). Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning. Virtual and Physical Prototyping, 16(1), 50–67.

  Article  Google Scholar 

• Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. (2015). How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv Preprint. https://arxiv.org/abs/1511.06348

• Coatanéa, E., Nagarajan, H. P., Panicker, S., Prod’hon, R., Mokhtarian, H., Chakraborti, A., Paris, H., Ituarte, I. F., & Haapala, K. R. (2020). Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: A case study for additive manufacturing. The International Journal of Advanced Manufacturing Technology, 115, 715–731.

  Article  Google Scholar 

• Costello, C., Anderson, S., Bishop, C., Mayfield, J., & McNamee, P. (2020). Dragonfly: Advances in non-speaker annotation for low resource languages. In Proceedings of the 12th language resources and evaluation conference (pp. 6983–6987).

• DebRoy, T., Mukherjee, T., Wei, H., Elmer, J., & Milewski, J. (2021). Metallurgy, mechanistic models and machine learning in metal printing. Nature Reviews Materials, 6(1), 48–68.

  Article  Google Scholar 

• DebRoy, T., Wei, H., Zuback, J., Mukherjee, T., Elmer, J., Milewski, J., Beese, A. M., Wilson-Heid, A., De, A., & Zhang, W. (2018). Additive manufacturing of metallic components–process, structure and properties. Progress in Materials Science, 92, 112–224.

  Article  Google Scholar 

• DeCost, B. L., Jain, H., Rollett, A. D., & Holm, E. A. (2017). Computer vision and machine learning for autonomous characterization of AM powder feedstocks. JOM Journal of the Minerals Metals and Materials Society, 69(3), 456–465.

  Article  Google Scholar 

• Deng, L. (2012). The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6), 141–142.

  Article  Google Scholar 

• Desai, P. S., & Higgs, C. F. (2019). Spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning. Metals, 9(11), 1176.

  Article  Google Scholar 

• Després, N., Cyr, E., Setoodeh, P., & Mohammadi, M. (2020). Deep learning and design for additive manufacturing: A framework for microlattice architecture. JOM Journal of the Minerals Metals and Materials Society, 72(6), 2408–2418.

  Article  Google Scholar 

• 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, 23, 100218.

  Article  Google Scholar 

• Ding, L., DiFranzo, D., Graves, A., Michaelis, J. R., Li, X., McGuinness, D. L., & Hendler, J. (2010). Data-gov wiki: Towards linking government data. In 2010 AAAI spring symposium series.

• Donegan, S. P., Schwalbach, E. J., & Groeber, M. A. (2020). Zoning additive manufacturing process histories using unsupervised machine learning. Materials Characterization, 161, 110123.

  Article  Google Scholar 

• Dutta, B., & Froes, F. H. S. (2015). The additive manufacturing (AM) of titanium alloys. In Titanium powder metallurgy (pp. 447–468). Elsevier.

• Elbadawi, M., Castro, B. M., Gavins, F. K., Ong, J. J., Gaisford, S., Pérez, G., Basit, A. W., Cabalar, P., & Goyanes, A. (2020). M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines. International Journal of Pharmaceutics, 590, 119837.

  Article  Google Scholar 

• Erdmann, M., Maedche, A., Schnurr, H.-P., & Staab, S. (2000). From manual to semi-automatic semantic annotation: About ontology-based text annotation tools. In Proceedings of the COLING-2000 workshop on semantic annotation and intelligent content (pp. 79–85).

• Ernst, M., Kang, K. B., Caraballo-Rodríguez, A. M., Nothias, L.-F., Wandy, J., Chen, C., Wang, M., Rogers, S., Medema, M. H., & Dorrestein, P. C. (2019). MolNetEnhancer: Enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites, 9(7), 144.

  Article  Google Scholar 

• Gaikwad, A., Chang, T., Giera, B., Watkins, N., Mukherjee, S., Pascall, A., Stobbe, D., & Rao, P. (2022). In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01977-2

  Article  Google Scholar 

• Gaikwad, A., Giera, B., Guss, G. M., Forien, J.-B., Matthews, M. J., & Rao, P. (2020). Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion—A single-track study. Additive Manufacturing, 36, 101659.

  Article  Google Scholar 

• García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (Vol. 72). Ny: Springer.

  Google Scholar 

• García-Moreno, A.-I., Alvarado-Orozco, J.-M., Ibarra-Medina, J., & Martínez-Franco, E. (2020). Image-based porosity classification in Al-alloys by laser metal deposition using random forests. The International Journal of Advanced Manufacturing Technology, 110(9), 2827–2845.

  Article  Google Scholar 

• García-Moreno, A.-I., Alvarado-Orozco, J.-M., Ibarra-Medina, J., & Martínez-Franco, E. (2021). Ex-situ porosity classification in metallic components by laser metal deposition: A machine learning-based approach. Journal of Manufacturing Processes, 62, 523–534.

  Article  Google Scholar 

• Gardner, J. M., Hunt, K. A., Ebel, A. B., Rose, E. S., Zylich, S. C., Jensen, B. D., Wise, K. E., Siochi, E. J., & Sauti, G. (2019). Machines as craftsmen: Localized parameter setting optimization for fused filament fabrication 3D printing. Advanced Materials Technologies, 4(3), 1800653.

  Article  Google Scholar 

• Garland, A. P., White, B. C., Jared, B. H., Heiden, M., Donahue, E., & Boyce, B. L. (2020). Deep convolutional neural networks as a rapid screening tool for complex additively manufactured structures. Additive Manufacturing, 35, 101217.

  Article  Google Scholar 

• Garland, A. P., White, B. C., Jensen, S. C., & Boyce, B. L. (2021). Pragmatic generative optimization of novel structural lattice metamaterials with machine learning. Materials & Design, 203, 109632.

  Article  Google Scholar 

• Gobert, C., Kudzal, A., Sietins, J., Mock, C., Sun, J., & McWilliams, B. (2020). Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning. Additive Manufacturing, 36, 101460.

  Article  Google Scholar 

• Goh, G. D., Sing, S. L., & Yeong, W. Y. (2021). A review on machine learning in 3D printing: Applications, potential, and challenges. Artificial Intelligence Review, 54(1), 63–94.

  Article  Google Scholar 

• Greitemeier, D., Dalle Donne, C., Schoberth, A., Jürgens, M., Eufinger, J., & Melz, T. (2015). Uncertainty of additive manufactured Ti–6Al–4V: Chemistry, microstructure and mechanical properties. Applied Mechanics and Materials, 807, 169–180.

  Article  Google Scholar 

• Gu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J. (2018). Bioinspired hierarchical composite design using machine learning: Simulation, additive manufacturing, and experiment. Materials Horizons, 5(5), 939–945.

  Article  Google Scholar 

• Guo, Y., Lu, W. F., & Fuh, J. Y. H. (2021). Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process. Journal of Intelligent Manufacturing, 32(2), 347–359.

  Article  Google Scholar 

• Guyon, I., Gunn, S., Nikravesh, M., & Zadeh, L. A. (2008). Feature extraction: Foundations and applications (Vol. 207). Springer.

  Google Scholar 

• Haghighi, A., & Li, L. (2020). A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing. Additive Manufacturing, 36, 101399.

  Article  Google Scholar 

• Hajializadeh, F., & Ince, A. (2021). Integration of artificial neural network with finite element analysis for residual stress prediction of direct metal deposition process. Materials Today Communications, 27, 102197.

  Article  Google Scholar 

• Han, Y., Griffiths, R. J., Hang, Z. Y., & Zhu, Y. (2020). Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning. Journal of Materials Research, 35(15), 1936–1948.

  Article  Google Scholar 

• Hassanin, H., Alkendi, Y., Elsayed, M., Essa, K., & Zweiri, Y. (2020). Controlling the properties of additively manufactured cellular structures using machine learning approaches. Advanced Engineering Materials, 22(3), 1901338.

  Article  Google Scholar 

• Haykin, S. (2010). Neural networks and learning machines, 3/E. Pearson Education India.

• He, H., Yang, Y., & Pan, Y. (2019). Machine learning for continuous liquid interface production: Printing speed modelling. Journal of Manufacturing Systems, 50, 236–246.

  Article  Google Scholar 

• Herriott, C., & Spear, A. D. (2020). Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Computational Materials Science, 175, 109599.

  Article  Google Scholar 

• Hertlein, N., Deshpande, S., Venugopal, V., Kumar, M., & Anand, S. (2020). Prediction of selective laser melting part quality using hybrid Bayesian network. Additive Manufacturing, 32, 101089.

  Article  Google Scholar 

• Hong, K., Huang, H., Fu, Y., & Zhou, J. (2016). A vibration measurement system for health monitoring of power transformers. Measurement, 93, 135–147.

  Article  Google Scholar 

• Hu, C., Hau, W. N. J., Chen, W., & Qin, Q.-H. (2021). The fabrication of long carbon fiber reinforced polylactic acid composites via fused deposition modelling: Experimental analysis and machine learning. Journal of Composite Materials, 55(11), 1459–1472.

  Article  Google Scholar 

• Huang, D., & Li, H. (2018). Review of machine learning applications in powder bed fusion technology for part production. In Proceedings of the international conference on progress in additive manufacturing (pp. 709–716).

• Huang, D. J., & Li, H. (2021). A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Materials & Design, 203, 109606.

  Article  Google Scholar 

• IEEE. (n.d.). IEEEDataPort. Institute of Electrical and Electronics Engineers. https://ieee-dataport.org/

• Imani, F., Gaikwad, A., Montazeri, M., Rao, P., Yang, H., & Reutzel, E. (2018). Process mapping and in-process monitoring of porosity in laser powder bed fusion using layerwise optical imaging. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4040615

  Article  Google Scholar 

• Jain, A. K., & Chandrasekaran, B. (1982). 39 Dimensionality and sample size considerations in pattern recognition practice. Handbook of Statistics, 2, 835–855.

  Article  Google Scholar 

• Jiang, J., Xiong, Y., Zhang, Z., & Rosen, D. W. (2020a). Machine learning integrated design for additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01715-6

  Article  Google Scholar 

• Jiang, J., Yu, C., Xu, X., Ma, Y., & Liu, J. (2020b). Achieving better connections between deposited lines in additive manufacturing via machine learning. Mathematical Biosciences and Engineering, 17(4), 3382–3394.

  Article  Google Scholar 

• Jin, Z., Zhang, Z., Demir, K., & Gu, G. X. (2020). Machine learning for advanced additive manufacturing. Matter, 3(5), 1541–1556.

  Article  Google Scholar 

• Jin, Z., Zhang, Z., Ott, J., & Gu, G. X. (2021). Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning. Additive Manufacturing, 37, 101696.

  Article  Google Scholar 

• Johnson, A. E., Pollard, T. J., Shen, L., Li-Wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3(1), 1–9.

  Article  Google Scholar 

• Johnson, N., Vulimiri, P., To, A., Zhang, X., Brice, C., Kappes, B., & Stebner, A. (2020). Invited review: Machine learning for materials developments in metals additive manufacturing. Additive Manufacturing, 36, 101641.

  Article  Google Scholar 

• Joshi, M., Flood, A., Sparks, T., & Liou, F. W. (2019). Applications of supervised machine learning algorithms in additive manufacturing: A review. In 2019 international solid freeform fabrication symposium. University of Texas at Austin.

• Kapusuzoglu, B., & Mahadevan, S. (2020). Physics-informed and hybrid machine learning in additive manufacturing: Application to fused filament fabrication. JOM Journal of the Minerals Metals and Materials Society, 72(12), 4695–4705.

  Article  Google Scholar 

• Kavzoglu, T., & Mather, P. (2003). The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24(23), 4907–4938.

  Article  Google Scholar 

• Khan, M. F., Alam, A., Siddiqui, M. A., Alam, M. S., Rafat, Y., Salik, N., & Al-Saidan, I. (2021). Real-time defect detection in 3D printing using machine learning. Materials Today: Proceedings, 42, 521–528.

  Google Scholar 

• Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M. A., & Bian, L. (2018a). Porosity prediction: Supervised-learning of thermal history for direct laser deposition. Journal of Manufacturing Systems, 47, 69–82.

  Article  Google Scholar 

• Khanzadeh, M., Rao, P., Jafari-Marandi, R., Smith, B. K., Tschopp, M. A., & Bian, L. (2018b). Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4038598

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• Korneev, S., Wang, Z., Thiagarajan, V., & Nelaturi, S. (2020). Fabricated shape estimation for additive manufacturing processes with uncertainty. Computer-Aided Design, 127, 102852.

  Article  Google Scholar 

• Kumar, H. A., Kumaraguru, S., Paul, C., & Bindra, K. (2021). Faster temperature prediction in the powder bed fusion process through the development of a surrogate model. Optics & Laser Technology, 141, 107122.

  Article  Google Scholar 

• Kumke, M., Watschke, H., Hartogh, P., Bavendiek, A.-K., & Vietor, T. (2018). Methods and tools for identifying and leveraging additive manufacturing design potentials. International Journal on Interactive Design and Manufacturing (IJIDeM), 12(2), 481–493.

  Article  Google Scholar 

• Kuschmitz, S., Ring, T. P., Watschke, H., Langer, S. C., & Vietor, T. (2021). Design and additive manufacturing of porous sound absorbers—A machine-learning approach. Materials, 14(7), 1747.

  Article  Google Scholar 

• Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G.-H., Cho, J.-H., Kim, N. I., & Kim, K. (2020). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing, 31(2), 375–386.

  Article  Google Scholar 

• Lee, S., Peng, J., Shin, D., & Choi, Y. S. (2019). Data analytics approach for melt-pool geometries in metal additive manufacturing. Science and Technology of Advanced Materials, 20(1), 972–978.

  Article  Google Scholar 

• Lee, X. Y., Saha, S. K., Sarkar, S., & Giera, B. (2020). Automated detection of part quality during two-photon lithography via deep learning. Additive Manufacturing, 36, 101444.

  Article  Google Scholar 

• Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. ACM Computing Surveys (CSUR), 50(6), 1–45.

  Article  Google Scholar 

• Li, J., Sage, M., Guan, X., Brochu, M., & Zhao, Y. F. (2020a). Machine learning-enabled competitive grain growth behavior study in directed energy deposition fabricated Ti6Al4V. JOM Journal of the Minerals Metals and Materials Society, 72(1), 458–464.

  Article  Google Scholar 

• Li, J., Zhou, X., Brochu, M., Provatas, N., & Zhao, Y. F. (2019a). Solidification microstructure simulation of Ti–6Al–4V in metal additive manufacturing: A review. Additive Manufacturing, 31, 100989.

  Article  Google Scholar 

• Li, R., Jin, M., & Paquit, V. C. (2021). Geometrical defect detection for additive manufacturing with machine learning models. Materials & Design, 206, 109726.

  Article  Google Scholar 

• Li, X., Jia, X., Yang, Q., & Lee, J. (2020b). Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 31(8), 2003–2017.

  Article  Google Scholar 

• Li, Z., Zhang, Z., Shi, J., & Wu, D. (2019b). Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing, 57, 488–495.

  Article  Google Scholar 

• Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., & Bigot, S. (2020a). Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2020.05.010

  Article  Google Scholar 

• Liu, C., Wang, R. R., Ho, I., Kong, Z. J., Williams, C., Babu, S., & Joslin, C. (2022). Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01933-0

  Article  Google Scholar 

• Liu, Q., Wu, H., Paul, M. J., He, P., Peng, Z., Gludovatz, B., Kruzic, J. J., Wang, C. H., & Li, X. (2020b). Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms. Acta Materialia, 201, 316–328.

  Article  Google Scholar 

• Liu, R., Liu, S., & Zhang, X. (2021a). A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing. The International Journal of Advanced Manufacturing Technology, 113(7), 1943–1958.

  Article  Google Scholar 

• Liu, S., Stebner, A. P., Kappes, B. B., & Zhang, X. (2021b). Machine learning for knowledge transfer across multiple metals additive manufacturing printers. Additive Manufacturing, 39, 101877.

  Article  Google Scholar 

• Lu, Y., Witherell, P., & Donmez, A. (2017). A collaborative data management system for additive manufacturing. In International design engineering technical conferences and computers and information in engineering conference (p. V001T002A036). American Society of Mechanical Engineers.

• Mahato, V., Obeidi, M. A., Brabazon, D., & Cunningham, P. (2020). Detecting voids in 3D printing using melt pool time series data. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01694-8

  Article  Google Scholar 

• Mahmoud, D., Magolon, M., Boer, J., Elbestawi, M., & Mohammadi, M. G. (2021). Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: A review. Applied Sciences, 11(24), 11910.

  Article  Google Scholar 

• Mahmoudi, M., Tapia, G., Franco, B., Ma, J., Arroyave, R., Karaman, I., & Elwany, A. (2018). On the printability and transformation behavior of nickel–titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing. Journal of Manufacturing Processes, 35, 672–680.

  Article  Google Scholar 

• Manivannan, S. (2022). Automatic quality inspection in additive manufacturing using semi-supervised deep learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-02000-4

  Article  Google Scholar 

• Markl, M., & Körner, C. (2016). Multiscale modeling of powder bed-based additive manufacturing. Annual Review of Materials Research, 46(1), 93–123.

  Article  Google Scholar 

• Marmarelis, M. G., & Ghanem, R. G. (2020). Data-driven stochastic optimization on manifolds for additive manufacturing. Computational Materials Science, 181, 109750.

  Article  Google Scholar 

• Masinelli, G., Shevchik, S. A., Pandiyan, V., Quang-Le, T., & Wasmer, K. (2020). Artificial intelligence for monitoring and control of metal additive manufacturing. In International conference on additive manufacturing in products and applications (pp. 205–220). Springer.

• Mativo, T., Fritz, C., & Ismail, F. (2018). Cyber acoustic analysis of additively manufactured objects. The International Journal of Advanced Manufacturing Technology, 96(1–4), 581–586.

  Article  Google Scholar 

• McHenry, K., & Bajcsy, P. (2008). An overview of 3D data content, file formats and viewers. National Center for Supercomputing Applications, 1205, 22.

  Google Scholar 

• Meißner, P., Watschke, H., Winter, J., & Vietor, T. (2020). Artificial neural networks-based material parameter identification for numerical simulations of additively manufactured parts by material extrusion. Polymers, 12(12), 2949.

  Article  Google Scholar 

• Meng, L., McWilliams, B., Jarosinski, W., Park, H.-Y., Jung, Y.-G., Lee, J., & Zhang, J. (2020). Machine learning in additive manufacturing: A review. JOM Journal of the Minerals Metals and Materials Society, 72(6), 2363–2377.

  Article  Google Scholar 

• Michopoulos, J. G., Iliopoulos, A. P., Steuben, J. C., Birnbaum, A. J., & Lambrakos, S. G. (2018). On the multiphysics modeling challenges for metal additive manufacturing processes. Additive Manufacturing, 22, 784–799.

  Article  Google Scholar 

• Minnema, J., van Eijnatten, M., Kouw, W., Diblen, F., Mendrik, A., & Wolff, J. (2018). CT image segmentation of bone for medical additive manufacturing using a convolutional neural network. Computers in Biology and Medicine, 103, 130–139.

  Article  Google Scholar 

• Mitchell, J. A., Ivanoff, T. A., Dagel, D., Madison, J. D., & Jared, B. (2020). Linking pyrometry to porosity in additively manufactured metals. Additive Manufacturing, 31, 100946.

  Article  Google Scholar 

• Mojahed Yazdi, R., Imani, F., & Yang, H. (2020). A hybrid deep learning model of process-build interactions in additive manufacturing. Journal of Manufacturing Systems, 57, 460–468.

  Article  Google Scholar 

• Mondal, S., Gwynn, D., Ray, A., & Basak, A. (2020). Investigation of melt pool geometry control in additive manufacturing using hybrid modeling. Metals, 10(5), 683.

  Article  Google Scholar 

• Montazeri, M., Nassar, A. R., Dunbar, A. J., & Rao, P. (2020). In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy. IISE Transactions, 52(5), 500–515.

  Article  Google Scholar 

• Montazeri, M., Nassar, A. R., Stutzman, C. B., & Rao, P. (2019). Heterogeneous sensor-based condition monitoring in directed energy deposition. Additive Manufacturing, 30, 100916.

  Article  Google Scholar 

• Murray, J. D., & VanRyper, W. (1996). Encyclopedia of graphics file formats. O’Reilly.

  Google Scholar 

• Mycroft, W., Katzman, M., Tammas-Williams, S., Hernandez-Nava, E., Panoutsos, G., Todd, I., & Kadirkamanathan, V. (2020). A data-driven approach for predicting printability in metal additive manufacturing processes. Journal of Intelligent Manufacturing, 31(7), 1769–1781.

  Article  Google Scholar 

• Nagarajan, H. P., Mokhtarian, H., Jafarian, H., Dimassi, S., Bakrani-Balani, S., Hamedi, A., Coatanéa, E., Gary Wang, G., & Haapala, K. R. (2019). Knowledge-based design of artificial neural network topology for additive manufacturing process modeling: A new approach and case study for fused deposition modeling. Journal of Mechanical Design, 141(2), 021705.

  Article  Google Scholar 

• Nguyen, L., Buhl, J., & Bambach, M. (2020). 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.

  Article  Google Scholar 

• NIST. (n.d.). Additive Manufacturing Materials Database (AMMD). National Institute of Standards and Technology. https://ammd.nist.gov/

• Obaton, A.-F., Wang, Y., Butsch, B., & Huang, Q. (2021). A non-destructive resonant acoustic testing and defect classification of additively manufactured lattice structures. Welding in the World, 65(3), 361–371.

  Article  Google Scholar 

• Oehlmann, P., Osswald, P., Blanco, J. C., Friedrich, M., Rietzel, D., & Witt, G. (2021). Modeling fused filament fabrication using artificial neural networks. Production Engineering, 15(3), 467–478.

  Article  Google Scholar 

• Okaro, I. A., Jayasinghe, S., Sutcliffe, C., Black, K., Paoletti, P., & Green, P. L. (2019). Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Additive Manufacturing, 27, 42–53.

  Article  Google Scholar 

• Osswald, P. V., Mustafa, S. K., Kaa, C., Obst, P., Friedrich, M., Pfeil, M., Rietzel, D., & Witt, G. (2020). Optimization of the production processes of powder-based additive manufacturing technologies by means of a machine learning model for the temporal prognosis of the build and cooling phase. Production Engineering, 14(5), 677–691.

  Article  Google Scholar 

• Özel, T., Altay, A., Donmez, A., & Leach, R. (2018). Surface topography investigations on nickel alloy 625 fabricated via laser powder bed fusion. The International Journal of Advanced Manufacturing Technology, 94(9), 4451–4458.

  Article  Google Scholar 

• Park, H., Ko, H., Lee, Y.-T.T., Feng, S., Witherell, P., & Cho, H. (2021a). Collaborative knowledge management to identify data analytics opportunities in additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01811-1

  Article  Google Scholar 

• Park, H. S., Nguyen, D. S., Le-Hong, T., & Van Tran, X. (2021b). Machine learning-based optimization of process parameters in selective laser melting for biomedical applications. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01896-8

  Article  Google Scholar 

• Paulson, N. H., Gould, B., Wolff, S. J., Stan, M., & Greco, A. C. (2020). Correlations between thermal history and keyhole porosity in laser powder bed fusion. Additive Manufacturing, 34, 101213.

  Article  Google Scholar 

• Pham, T. Q. D., Hoang, T. V., Van Tran, X., Pham, Q. T., Fetni, S., Duchêne, L., Tran, H. S., & Habraken, A.-M. (2022). Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01896-8

  Article  Google Scholar 

• Pineau, J., Vincent-Lamarre, P., Sinha, K., Lariviere, V., Beygelzimer, A., d’Alché-Buc, F., Fox, E., & Larochelle, H. (2021). Improving reproducibility in machine learning research. Journal of Machine Learning Research, 22, 1–20.

  Google Scholar 

• 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.

  Article  Google Scholar 

• Rankouhi, B., Jahani, S., Pfefferkorn, F. E., & Thoma, D. J. (2021). Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters. Additive Manufacturing, 38, 101836.

  Article  Google Scholar 

• Razvi, S. S., Feng, S., Narayanan, A., Lee, Y.-T. T., & Witherell, P. (2019). A review of machine learning applications in additive manufacturing. In International design engineering technical conferences and computers and information in engineering conference (p. V001T002A040). American Society of Mechanical Engineers.

• Ren, K., Chew, Y., Liu, N., Zhang, Y., Fuh, J., & Bi, G. (2021). Integrated numerical modelling and deep learning for multi-layer cube deposition planning in laser aided additive manufacturing. Virtual and Physical Prototyping. https://doi.org/10.1080/17452759.2021.1922714

  Article  Google Scholar 

• Ren, K., Chew, Y., Zhang, Y., Fuh, J., & Bi, G. (2020a). 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.

  Article  Google Scholar 

• Ren, Y. M., Zhang, Y., Ding, Y., Wang, Y., & Christofides, P. D. (2020b). Computational fluid dynamics-based in-situ sensor analytics of direct metal laser solidification process using machine learning. Computers & Chemical Engineering, 143, 107069.

  Article  Google Scholar 

• Roach, D. J., Rohskopf, A., Hamel, C. M., Reinholtz, W. D., Bernstein, R., Qi, H. J., & Cook, A. W. (2021). Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures. Additive Manufacturing, 41, 101950.

  Article  Google Scholar 

• Rodríguez-Martín, M., Fueyo, J. G., Gonzalez-Aguilera, D., Madruga, F. J., García-Martín, R., Muñóz, Á. L., & Pisonero, J. (2020). Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods. Sensors, 20(14), 3982.

  Article  Google Scholar 

• Roy, M., & Wodo, O. (2020). Data-driven modeling of thermal history in additive manufacturing. Additive Manufacturing, 32, 101017.

  Article  Google Scholar 

• Saluja, A., Xie, J., & Fayazbakhsh, K. (2020). A closed-loop in-process warping detection system for fused filament fabrication using convolutional neural networks. Journal of Manufacturing Processes, 58, 407–415.

  Article  Google Scholar 

• Samie Tootooni, M., Dsouza, A., Donovan, R., Rao, P. K., Kong, Z. J., & Borgesen, P. (2017). Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. Journal of Manufacturing Science and Engineering, 139(9), 091005.

  Article  Google Scholar 

• Sanchez, S., Rengasamy, D., Hyde, C. J., Figueredo, G. P., & Rothwell, B. (2021). Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01785-0

  Article  Google Scholar 

• Schaechtl, P., Schleich, B., & Wartzack, S. (2021). Statistical tolerance analysis of 3D-printed non-assembly mechanisms in motion using empirical predictive models. Applied Sciences, 11(4), 1860.

  Article  Google Scholar 

• Schur, R., Ghods, S., Wisdom, C., Pahuja, R., Montelione, A., Arola, D., & Ramulu, M. (2021). Mechanical anisotropy and its evolution with powder reuse in Electron Beam Melting AM of Ti6Al4V. Materials & Design, 200, 109450.

  Article  Google Scholar 

• Scime, L., & Beuth, J. (2018a). Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing, 19, 114–126.

  Article  Google Scholar 

• Scime, L., & Beuth, J. (2018b). A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 24, 273–286.

  Article  Google Scholar 

• Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151–165.

  Article  Google Scholar 

• Scime, L., Siddel, D., Baird, S., & Paquit, V. (2020). Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Additive Manufacturing, 36, 101453.

  Article  Google Scholar 

• Shevchik, S. A., Kenel, C., Leinenbach, C., & Wasmer, K. (2018). Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing, 21, 598–604.

  Article  Google Scholar 

• Shevchik, S. A., Masinelli, G., Kenel, C., Leinenbach, C., & Wasmer, K. (2019). Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Transactions on Industrial Informatics, 15(9), 5194–5203.

  Article  Google Scholar 

• Shi, Z., Mamun, A. A., Kan, C., Tian, W., & Liu, C. (2022). An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01879-9

  Article  Google Scholar 

• Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48.

  Article  Google Scholar 

• Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.

  Article  Google Scholar 

• Snell, R., Tammas-Williams, S., Chechik, L., Lyle, A., Hernández-Nava, E., Boig, C., Panoutsos, G., & Todd, I. (2020). Methods for rapid pore classification in metal additive manufacturing. JOM Journal of the Minerals Metals and Materials Society, 72(1), 101–109.

  Article  Google Scholar 

• Snow, Z., Diehl, B., Reutzel, E. W., & Nassar, A. (2021). Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. Journal of Manufacturing Systems, 59, 12–26.

  Article  Google Scholar 

• Srinivasan, S., Swick, B., & Groeber, M. A. (2020). Laser powder bed fusion parameter selection via machine-learning-augmented process modeling. JOM Journal of the Minerals Metals and Materials Society, 72(12), 4393–4403.

  Article  Google Scholar 

• Stanisavljevic, D., Cemernek, D., Gursch, H., Urak, G., & Lechner, G. (2020). Detection of interferences in an additive manufacturing process: An experimental study integrating methods of feature selection and machine learning. International Journal of Production Research, 58(9), 2862–2884.

  Article  Google Scholar 

• Szilvśi-Nagy, M., & Matyasi, G. (2003). Analysis of STL files. Mathematical and Computer Modelling, 38(7–9), 945–960.

  Article  Google Scholar 

• Tennison, J., Kellogg, G., & Herman, I. (2015). Model for tabular data and metadata on the web. https://ir.cwi.nl/pub/23799

• Thomas, M., Schram, M., Fox, K., Strube, J., Oblath, N. S., Rallo, R., Kennedy, Z. C., Varga, T., Battu, A. K., & Barrett, C. A. (2020). Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. MRS Advances, 5(29), 1547–1555.

  Article  Google Scholar 

• Tian, C., Li, T., Bustillos, J., Bhattacharya, S., Turnham, T., Yeo, J., & Moridi, A. (2021). Data-driven approaches toward smarter additive manufacturing. Advanced Intelligent Systems, 3(12), 2100014.

  Article  Google Scholar 

• Tian, Q., Guo, S., & Guo, Y. (2020). A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. CIRP Annals, 69(1), 205–208.

  Article  Google Scholar 

• Vafadar, A., Guzzomi, F., Rassau, A., & Hayward, K. (2021). Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges. Applied Sciences, 11(3), 1213.

  Article  Google Scholar 

• Wang, C., Tan, X., Tor, S., & Lim, C. (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, 101538.

  Article  Google Scholar 

• Wang, T., Kwok, T.-H., Zhou, C., & Vader, S. (2018). In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing. Journal of Manufacturing Systems, 47, 83–92.

  Article  Google Scholar 

• Wang, Z., Cai, S., Chen, W., Abd Ali, R., & Jin, K. (2021). Analysis of critical velocity of cold spray based on machine learning method with feature selection. Journal of Thermal Spray Technology. https://doi.org/10.1007/s11666-021-01198-8

  Article  Google Scholar 

• Wasmer, K., Le-Quang, T., Meylan, B., & Shevchik, S. (2019). In situ quality monitoring in AM using acoustic emission: A reinforcement learning approach. Journal of Materials Engineering and Performance, 28(2), 666–672.

  Article  Google Scholar 

• Westphal, E., & Seitz, H. (2021). A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Additive Manufacturing, 41, 101965.

  Article  Google Scholar 

• Williams, G., Meisel, N. A., Simpson, T. W., & McComb, C. (2019). Design repository effectiveness for 3D convolutional neural networks: Application to additive manufacturing. Journal of Mechanical Design. https://doi.org/10.1115/1.4044199

  Article  Google Scholar 

• Wilson, J. M., Piya, C., Shin, Y. C., Zhao, F., & Ramani, K. (2014). Remanufacturing of turbine blades by laser direct deposition with its energy and environmental impact analysis. Journal of Cleaner Production, 80, 170–178.

  Article  Google Scholar 

• Wu, D., Wei, Y., & Terpenny, J. (2019a). Predictive modelling of surface roughness in fused deposition modelling using data fusion. International Journal of Production Research, 57(12), 3992–4006.

  Article  Google Scholar 

• Wu, H., Yu, Z., & Wang, Y. (2019b). Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission. Measurement, 136, 445–453.

  Article  Google Scholar 

• Wu, M., Song, Z., & Moon, Y. B. (2019c). Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. Journal of Intelligent Manufacturing, 30(3), 1111–1123.

  Article  Google Scholar 

• Wu, Q., Mukherjee, T., De, A., & DebRoy, T. (2020). Residual stresses in wire-arc additive manufacturing–hierarchy of influential variables. Additive Manufacturing, 35, 101355.

  Article  Google Scholar 

• Xames, M. D., Torsha, F. K., & Sarwar, F. (2022). A systematic literature review on recent trends of machine learning applications in additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01957-6

  Article  Google Scholar 

• Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S. (2021). Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing, 33(5), 1467–1482.

  Article  Google Scholar 

• Xiangyang, Y., Qi, Y., Junjun, L., & Lin, Z. (2020). Atomic simulations of melting behaviours for TiAl alloy nanoparticles during heating. Bulletin of Materials Science, 43(1), 1–9.

  Article  Google Scholar 

• Xie, J., Saluja, A., Rahimizadeh, A., & Fayazbakhsh, K. (2022). Development of automated feature extraction and convolutional neural network optimization for real-time warping monitoring in 3D printing. International Journal of Computer Integrated Manufacturing. https://doi.org/10.1080/0951192X.2022.2025621

  Article  Google Scholar 

• Yadav, P., Singh, V. K., Joffre, T., Rigo, O., Arvieu, C., Le Guen, E., & Lacoste, E. (2020). Inline drift detection using monitoring systems and machine learning in selective laser melting. Advanced Engineering Materials, 22(12), 2000660.

  Article  Google Scholar 

• Yanamandra, K., Chen, G. L., Xu, X., Mac, G., & Gupta, N. (2020). Reverse engineering of additive manufactured composite part by toolpath reconstruction using imaging and machine learning. Composites Science and Technology, 198, 108318.

  Article  Google Scholar 

• Yang, J., Chen, Y., Huang, W., & Li, Y. (2017). Survey on artificial intelligence for additive manufacturing. In 2017 23rd international conference on automation and computing (ICAC) (pp. 1–6). IEEE.

• Yang, S., Page, T., Zhang, Y., & Zhao, Y. F. (2020). Towards an automated decision support system for the identification of additive manufacturing part candidates. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01545-6

  Article  Google Scholar 

• Yang, Z., Jin, L., Yan, Y., & Mei, Y. (2018). Filament breakage monitoring in fused deposition modeling using acoustic emission technique. Sensors, 18(3), 749.

  Article  Google Scholar 

• Yeung, H., Yang, Z., & Yan, L. (2020). A meltpool prediction based scan strategy for powder bed fusion additive manufacturing. Additive Manufacturing, 35, 101383.

  Article  Google Scholar 

• You, S., Guan, J., Alido, J., Hwang, H. H., Yu, R., Kwe, L., Su, H., & Chen, S. (2020). Mitigating scattering effects in light-based three-dimensional printing using machine learning. Journal of Manufacturing Science and Engineering, 142(8), 081002.

  Article  Google Scholar 

• Yuan, B., Guss, G. M., Wilson, A. C., Hau-Riege, S. P., DePond, P. J., McMains, S., Matthews, M. J., & Giera, B. (2018). Machine-learning-based monitoring of laser powder bed fusion. Advanced Materials Technologies, 3(12), 1800136.

  Article  Google Scholar 

• Zhan, Z., & Li, H. (2021a). Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. International Journal of Fatigue, 142, 105941.

  Article  Google Scholar 

• Zhan, Z., & Li, H. (2021b). A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing. International Journal of Fatigue, 145, 106089.

  Article  Google Scholar 

• Zhang, H., Choi, J. P., Moon, S. K., & Ngo, T. H. (2021a). A knowledge transfer framework to support rapid process modeling in aerosol jet printing. Advanced Engineering Informatics, 48, 101264.

  Article  Google Scholar 

• Zhang, J., Wang, P., & Gao, R. X. (2019a). Deep learning-based tensile strength prediction in fused deposition modeling. Computers in Industry, 107, 11–21.

  Article  Google Scholar 

• Zhang, M., Sun, C.-N., Zhang, X., Goh, P. C., Wei, J., Hardacre, D., & Li, H. (2019b). High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach. International Journal of Fatigue, 128, 105194.

  Article  Google Scholar 

• Zhang, X., Saniie, J., Cleary, W., & Heifetz, A. (2020a). Quality control of additively manufactured metallic structures with machine learning of thermography images. JOM Journal of the Minerals Metals and Materials Society, 72(12), 4682–4694.

  Article  Google Scholar 

• Zhang, X., Saniie, J., & Heifetz, A. (2020b). Detection of defects in additively manufactured stainless steel 316L with compact infrared camera and machine learning algorithms. JOM Journal of the Minerals Metals and Materials Society, 72(12), 4244–4253.

  Article  Google Scholar 

• Zhang, Y., Yang, S., Dong, G., & Zhao, Y. F. (2021b). Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model. Additive Manufacturing, 41, 101946.

  Article  Google Scholar 

• Zhang, Y., & Zhao, Y. F. (2022). Hybrid sparse convolutional neural networks for predicting manufacturability of visual defects of laser powder bed fusion processes. Journal of Manufacturing Systems, 62, 835–845.

  Article  Google Scholar 

• Zhang, Z., Femi-Oyetoro, J., Fidan, I., Ismail, M., & Allen, M. (2021c). Prediction of dimensional changes of low-cost metal material extrusion fabricated parts using machine learning techniques. Metals, 11(5), 690.

  Article  Google Scholar 

• Zhang, Z., Fidan, I., & Allen, M. (2020c). Detection of material extrusion in-process failures via deep learning. Inventions, 5(3), 25.

  Article  Google Scholar 

• Zhang, Z., Poudel, L., Sha, Z., Zhou, W., & Wu, D. (2020d). Data-driven predictive modeling of tensile behavior of parts fabricated by cooperative 3D printing. Journal of Computing and Information Science in Engineering, 20(2), 021002.

  Article  Google Scholar 

• Zhang, Z., Shi, J., Yu, T., Santomauro, A., Gordon, A., Gou, J., & Wu, D. (2020e). Predicting flexural strength of additively manufactured continuous carbon fiber-reinforced polymer composites using machine learning. Journal of Computing and Information Science in Engineering, 20(6), 061015.

  Article  Google Scholar 

• Zhou, B., & Tian, T. (2021). A path planning method of lattice structural components for additive manufacturing. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-07092-5

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• Zhu, K., Fuh, J. Y. H., & Lin, X. (2021a). Metal-based additive manufacturing condition monitoring: A review on machine learning based approaches. IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/TMECH.2021.3110818

  Article  Google Scholar 

• Zhu, Q., Liu, Z., & Yan, J. (2021b). Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Computational Mechanics, 67(2), 619–635.

  Article  Google Scholar 

• Zhu, Y., Wu, Z., Hartley, W. D., Sietins, J. M., Williams, C. B., & Hang, Z. Y. (2020). Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning. Additive Manufacturing, 34, 101183.

  Article  Google Scholar 

• Zhu, Z., Anwer, N., Huang, Q., & Mathieu, L. (2018). Machine learning in tolerancing for additive manufacturing. CIRP Annals, 67(1), 157–160.

  Article  Google Scholar 

• Zohdi, T. (2019). Electrodynamic machine-learning-enhanced fault-tolerance of robotic free-form printing of complex mixtures. Computational Mechanics, 63(5), 913–929.

  Article  Google Scholar 

• Zouhri, W., Dantan, J., Häfner, B., Eschner, N., Homri, L., Lanza, G., Theile, O., & Schäfer, M. (2020). Optical process monitoring for Laser-Powder Bed Fusion (L-PBF). CIRP Journal of Manufacturing Science and Technology, 31, 607–617.

  Article  Google Scholar 

Download references