journal article
Mar 13, 2022
Casting Defects Detection in Aluminum Alloys Using Deep Learning: a Classification Approach
International Journal of Metalcasting
Vol. 17
No. 1
pp. 386-398
·
Springer Science and Business Media LLC
Topics
No keywords indexed for this article. Browse by subject →
References
44
[1]
E. Fiorese, F. Bonollo, G. Timelli, L. Arnberg, E. Gariboldi, New classification of defects and imperfections for aluminum alloy castings. Inter. Metalcast. 9(1), 55–66 (2015). https://doi.org/10.1007/BF03355602
10.1007/bf03355602
[2]
C. Pequet, M. Rappaz, M. Gremaud, Modeling of microporosity, macroporosity, and pipe-shrinkage formation during the solidification of alloys using a mushy-zone refinement method: applications to aluminum alloys. Metall. Mater. Transactions A 33(7), 2095–2106 (2002)
10.1007/s11661-002-0041-5
[3]
B. Yalçin, M. Koru, O. Ipek, A.E. Özgür, Effect of injection parameters and vacuum on the strength and porosity amount of die-casted a380 alloy. Inter. Metalcast. 11(2), 195–206 (2017). https://doi.org/10.1007/s40962-016-0046-2
10.1007/s40962-016-0046-2
[4]
M. Riestra, A. Bjurenstedt, T. Bogdanoff, E. Ghassemali, S. Seifeddine, Complexities in the assessment of melt quality. Inter. Metalcast. 12(3), 441–448 (2018). https://doi.org/10.1007/s40962-017-0179-y
10.1007/s40962-017-0179-y
[5]
S. Brusethaug, Y. Langsrud, Aluminum properties, a model for calculating mechanical properties in alsimgfe-foundry alloys Metall. Sci. Tecnol. 18(1) (2000)
[6]
S. Shabestari, F. Shahri, Influence of modification, solidification conditions and heat treatment on the microstructure and mechanical properties of a356 aluminum alloy. J. Mater. Sci. 39(6), 2023–2032 (2004)
10.1023/b:jmsc.0000017764.20609.0d
[7]
M.S. Kabir, A. Ashrafi, T.I. Minhaj, M.M. Islam, Effect of foundry variables on the casting quality of as-cast lm25 aluminium alloy. Int. J. Eng. Adv. Technol. 3(6), 115–120 (2014)
[8]
S. Seifeddine, M. Wessen, I. Svensson, Use of simulation to predict microstructure and mechanical properties in an as-cast aluminium cylinder head comparison-with experiments. Metall. Sci. Tecnol. 24(2) (2006)
[9]
A. Morri, Empirical models of mechanical behaviour of al-si-mg cast alloys for high performance engine applications. Metall. Sci. Tecnol. 28(2) (2010)
[10]
E. Vandersluis, C. Ravindran, Relationships between solidification parameters in a319 aluminum alloy. J. Mater. Eng. Perform. 27(3), 1109–1121 (2018)
10.1007/s11665-018-3184-2
[11]
X. Dong, X. Zhu, S. Ji, Effect of super vacuum assisted high pressure die casting on the repeatability of mechanical properties of al-si-mg-mn die-cast alloys. J. Mater. Process. Technol. 266, 105–113 (2019)
10.1016/j.jmatprotec.2018.10.030
[12]
H. Cao, M. Hao, C. Shen, P. Liang, The influence of different vacuum degree on the porosity and mechanical properties of aluminum die casting. Vacuum 146, 278–281 (2017)
10.1016/j.vacuum.2017.09.048
[13]
A.R. Adamane, L. Arnberg, E. Fiorese, G. Timelli, F. Bonollo, Influence of injection parameters on the porosity and tensile properties of high-pressure die cast al-si alloys: a review. Inter. Metalcast. 9(1), 43–53 (2015). https://doi.org/10.1007/BF03355601
10.1007/bf03355601
[14]
H. Ammar, A. Samuel, F. Samuel, Effect of casting imperfections on the fatigue life of 319-f and a356–t6 al-si casting alloys. Mater. Sci. Eng. A 473(1–2), 65–75 (2008)
10.1016/j.msea.2007.03.112
[15]
H. Mayer, M. Papakyriacou, B. Zettl, S. Stanzl-Tschegg, Influence of porosity on the fatigue limit of die cast magnesium and aluminium alloys. Int. J. Fatigue 25(3), 245–256 (2003)
10.1016/s0142-1123(02)00054-3
[16]
Z. Yang, J. Kang, D.S. Wilkinson, The effect of porosity on fatigue of die cast am60. Metall. Mater. Trans. A. 47(7), 3464–3472 (2016)
10.1007/s11661-016-3511-x
[17]
M. Srivastava, O. Lohne, Effects of heat treatment on the microstructure and mechanical properties of ductile AlSi9MgMn die castings. Inter. Metalcast. 10(4), 556–565 (2016). https://doi.org/10.1007/s40962-016-0049-z
10.1007/s40962-016-0049-z
[18]
D. Sui, Z. Cui, R. Wang, S. Hao, Q. Han, Effect of cooling process on porosity in the aluminum alloy automotive wheel during low-pressure die casting. Inter. Metalcast. 10(1), 32–42 (2016). https://doi.org/10.1007/s40962-015-0008-0
10.1007/s40962-015-0008-0
[19]
A. Agrawal, A. Choudhary, Deep materials informatics: applications of deep learning in materials science. MRS Commun. 9(3), 779–792 (2019)
10.1557/mrc.2019.73
[20]
D. Mery, Aluminum casting inspection using deep learning: a method based on convolutional neural networks. J. Nondestr. Eval. 39(1), 12 (2020)
10.1007/s10921-020-0655-9
[21]
C. Herriott, A.D. Spear, Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods. Comput. Mater. Sci. 175, 109599 (2020)
10.1016/j.commatsci.2020.109599
[22]
S.M. Azimi, D. Britz, M. Engstler, M. Fritz, F. Mücklich, Advanced steel microstructural classification by deep learning methods. Sci. Rep. 8(1), 1–14 (2018)
10.1038/s41598-018-20037-5
[23]
X. Li, Y. Zhang, H. Zhao, C. Burkhart, L.C. Brinson, W. Chen, A transfer learning approach for microstructure reconstruction and structure-property predictions. Sci. Rep. 8(1), 1–13 (2018)
[24]
B.L. DeCost, B. Lei, T. Francis, E.A. Holm, High throughput quantitative metallography for complex microstructures using deep learning: a case study in ultrahigh carbon steel. arXiv preprint arXiv:1805.08693 (2018)
10.1017/s1431927618015635
[25]
M.K. Ferguson, A. Ronay, Y.-T.T. Lee, K.H. Law, Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain. Manuf. Syst. 2, 137–164 (2018). https://doi.org/10.1520/SSMS20180033
10.1520/ssms20180033
[26]
B.S.S. Pokuri, S. Ghosal, A. Kokate, S. Sarkar, B. Ganapathysubramanian, Interpretable deep learning for guided microstructure property explorations in photovoltaics. Comput. Mater. 5(1), 1–11 (2019)
10.1038/s41524-018-0138-z
[27]
A. Agrawal, P.D. Deshpande, A. Cecen, G.P. Basavarsu, A.N. Choudhary, S.R. Kalidindi, Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr. Mater. Manuf. Innov. 3(1), 8 (2014)
10.1186/2193-9772-3-8
[28]
D. Cica, D. Kramar, Intelligent process modeling and optimization of porosity formation in high-pressure die casting. Inter. Metalcast. 12(4), 814–824 (2018). https://doi.org/10.1007/s40962-018-0213-8
10.1007/s40962-018-0213-8
[29]
F. Nikolic, I. Stajduhar, M. Canadija, Aluminum Microstructure Inspection Using Deep Learning: a Convolutional Neural Network Approach Toward Secondary Dendrite Arm Spacing Determination. in 4th Edition of My First Conference, University of Rijeka, Faculty of Engineering, Rijeka, Croatia (2020)
[30]
F. Nikolic, I. Stajduhar, M. Canadija, Casting microstructure inspection using computer vision: Dendrite spacing in aluminum alloys. Metals 11(5), 756 (2021)
10.3390/met11050756
[31]
Y. Chen, B. Chen, Y. Yao, C. Tan, J. Feng, A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification. NDT & E Int. 108, 102176 (2019)
10.1016/j.ndteint.2019.102176
[32]
D. Blondheim, Improving manufacturing applications of machine learning by understanding defect classification and the critical error threshold. Inter. Metalcast. 1–19 (2021). https://doi.org/10.1007/s40962-021-00637-0
10.1007/s40962-021-00637-0
[33]
L. Yang, H. Wang, B. Huo, F. Li, Y. Liu, An automatic welding defect location algorithm based on deep learning. NDT & E Int. 120, 102435 (2021)
10.1016/j.ndteint.2021.102435
[34]
W. Du, H. Shen, J. Fu, G. Zhang, Q. He, Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning. NDT & E Int. 107, 102144 (2019). https://doi.org/10.1016/j.ndteint.2019.102144
10.1016/j.ndteint.2019.102144
[35]
D. Mery, C. Arteta, Automatic defect recognition in x-ray testing using computer vision, pp. 1026–1035 (2017). IEEE
10.1109/wacv.2017.119
[36]
J. Lin, Y. Yao, M. Lin, Y. Wang, Detection of a casting defect tracked by deep convolution neural network. Int. J. Adv. Manuf. Technol. 97(1–4), 573–581 (2018)
10.1007/s00170-018-1894-0
[37]
M. Ferguson, R. Ak, Y.T. Lee, K.H. Law, Automatic localization of casting defects with convolutional neural networks, in Proceedings - 2017 5th IEEE International Conference on Big Data, Big Data 2017, Boston, 11 December 2017–14 December, pp. 1726–1735 (2017). https://doi.org/10.1109/BigData.2017.8258115
10.1109/bigdata.2017.8258115
[38]
Z. Tang, E. Tian, Y. Wang, L. Wang, T. Yang, Nondestructive defect detection in castings by using spatial attention bilinear convolutional neural network. IEEE Trans. Industr. Inf. 17(1), 82–89 (2020)
10.1109/tii.2020.2985159
[39]
L. Duan, K. Yang, L. Ruan, Research on automatic recognition of casting defects based on deep learning. IEEE Access 9, 12209–12216 (2020)
10.1109/access.2020.3048432
[40]
F. Riaz, K. Kamal, T. Zafar, R. Qayyum, An inspection approach for casting defects detection using image segmentation, in 2017 International Conference on Mechanical, System and Control Engineering, ICMSC 2017, St. Petersburg, 19 May 2017–21 May 2017, pp. 101–105 (2017). https://doi.org/10.1109/ICMSC.2017.7959451
10.1109/icmsc.2017.7959451
[41]
X. Li, S.K. Tso, X.-P. Guan, Q. Huang, Improving automatic detection of defects in castings by applying wavelet technique. IEEE Trans. Industr. Electron. 53(6), 1927–1934 (2006)
10.1109/tie.2006.885448
[42]
DIN EN 1706: Aluminium und Aluminiumlegierungen-Gussstücke-Chemische Zusammensetzung und mechanische Eigenschaften. Berlin: Beuth (1998)
[43]
B.L. DeCost, E.A. Holm, A computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126–133 (2015)
10.1016/j.commatsci.2015.08.011
[44]
A. Chowdhury, E. Kautz, B. Yener, D. Lewis, Image driven machine learning methods for microstructure recognition. Comput. Mater. Sci. 123, 176–187 (2016)
10.1016/j.commatsci.2016.05.034
Cited By
34
Decision support in investment casting manufacturing: a convolutional neural network-driven approach
The International Journal of Advanc...
Antonia Antoniadou, Amare Desalegn Fentaye · 2025
Metrics
34
Citations
44
References
Details
- Published
- Mar 13, 2022
- Vol/Issue
- 17(1)
- Pages
- 386-398
- License
- View
Funding
University of Rijeka
Award: uniri-tehnic-18-37
Cite This Article
Filip Nikolić, Ivan Štajduhar, Marko Čanađija (2022). Casting Defects Detection in Aluminum Alloys Using Deep Learning: a Classification Approach. International Journal of Metalcasting, 17(1), 386-398. https://doi.org/10.1007/s40962-022-00777-x
Related
You May Also Like
A Metallographic Study of Grain Refining of Sr-Modified 356 Alloy
A. M. Samuel, H. W. Doty · 2016
33 citations
Investigations on Mechanical Properties of Micro- and Nano-Particulates (Al2 O3/B4C) Reinforced in Al 7075 Matrix Composite
G. Kumaresan, B. Arul kumar · 2022
23 citations
Heat Transfer Coefficient Determination in a Gravity Die Casting Process with Local Air Gap Formation and Contact Pressure Using Experimental Evaluation and Numerical Simulation
T. Vossel, N. Wolff · 2021
18 citations
Effects of Mold Rotation Speed and Cast Thickness on the Microstructure and Mechanical Properties of AZ80 Prepared by Centrifugal Casting
M. Arabi-Nour, F. Fereshteh-Saniee · 2021
15 citations