Production
https://www.prod.org.br/article/doi/10.1590/0103-6513.20210097
Production
Systematic Review

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Blanka Bártová; Vladislav Bína; Lucie Váchová

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Abstract

Paper aims: This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations.

Originality: Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing.

Research method: In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004).

Main findings: The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used.

Implications for theory and practice: This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

Keywords

Data mining, Defect, Detection, Classification, Manufacturing

References

Baly, R., & Hajj, H. (2012). Wafer classification using support vector machines. IEEE Transactions on Semiconductor Manufacturing, 25(3), 373-383. http://dx.doi.org/10.1109/TSM.2012.2196058.

Bartova, B., & Bina, V. (2020). Data mining methods used for quality management: a bibliometric analysis. In The 4th International Conference on Digital Technology in Education. New York: Association for Computing Machinery. http://dx.doi.org/10.1145/3429630.3429646

Batool, U., Shapiai, M. I., Fauzi, H., & Fong, J. X. (2020). Convolutional neural network for imbalanced data classification of silicon wafer defects. In 16th IEEE International Colloquium on Signal Processing & its Applications. USA: IEEE. http://dx.doi.org/10.1109/CSPA48992.2020.9068669

Bella, R., Carrera, D., Rossi, B., Fragneto, P., & Boracchi, G. (2019). Wafer defect map classification using sparse convolutional networks. In E. Ricci, S. R. Bulò, C. Snoek, O. Lanz, S. Messelodi & N. Sebe (Eds.), International Conference on Image Analysis and Processing (pp. 125-136). Trento, Italy: Springer. http://dx.doi.org/10.1007/978-3-030-30645-8_12.

Bowers, K., & Pickerel, T. V. (2019). Vox Populi 4.0: big data tools zoom in on the voice of the customer. Quality Progress, 52(3), 32-39.

Bumrungkun, P. (2019). Defect detection in textile fabrics with snake active contour and support vector machines. Journal of Physics: Conference Series, 1195, 012006. http://dx.doi.org/10.1088/1742-6596/1195/1/012006.

Cerezci, F., Kazan, S., Oz, M. A., Oz, C., Tasci, T., Hizal, S., & Altay, Ç. (2020). Online metallic surface defect detection using deep learning. Emerging Materials Research, 9(4), 1266-1273. http://dx.doi.org/10.1680/jemmr.20.00197.

Chang, C., Li, C., Chang, Y., & Jeng, M. (2011). Wafer defect inspection by neural analysis of region features. Journal of Intelligent Manufacturing, 22(6), 953-964. http://dx.doi.org/10.1007/s10845-009-0369-4.

Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks, 11(8), 431047. http://dx.doi.org/10.1155/2015/431047.

Chen, H., Pang, Y., Hu, Q., & Liu, K. (2020a). Solar cell surface defect inspection based on multispectral convolutional neural network. Journal of Intelligent Manufacturing, 31(2), 453-468. http://dx.doi.org/10.1007/s10845-018-1458-z.

Chen, X., Chen, J., Han, X., Zhao, C., Zhang, D., Zhu, K., & Su, Y. (2020b). A light-weighted CNN model for wafer structural defect detection. IEEE Access: Practical Innovations, Open Solutions, 8, 24006-24018. http://dx.doi.org/10.1109/ACCESS.2020.2970461.

Chien, J., Wu, M., & Lee, J. (2020). Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks. Applied Sciences, 10(15), 5340. http://dx.doi.org/10.3390/app10155340.

Chondronasios, A., Popov, I., & Jordanov, I. (2016). Feature selection for surface defect classification of extruded aluminum profiles. International Journal of Advanced Manufacturing Technology, 83(1-4), 33-41. http://dx.doi.org/10.1007/s00170-015-7514-3.

Choudhary, A. K., Harding, J., & Tiwari, M. K. (2009). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501-521. http://dx.doi.org/10.1007/s10845-008-0145-x.

Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery: an overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining. Menlo Park: AAAI Press, p.41

Fayyad, U., & Stolorz, P. (1997). Data mining and KDD: promise and challenges. Future Generation Computer Systems, 13(2-3), 99-115. http://dx.doi.org/10.1016/S0167-739X(97)00015-0.

Gibert, K., Sànchez-Marrè, M., & Codina, V. (2010). Choosing the right data mining technique: classification of methods and intelligent recommendation. In International Environmental Modelling and Software Society Vol. 5. Ontario, Canadá: iEMSs.

Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. Academic Press, San Diego

Han, Y., & Yu, H. (2020). Fabric defect detection system using stacked convolutional denoising auto-encoders trained with synthetic defect data. Applied Sciences, 10(7), 2511. http://dx.doi.org/10.3390/app10072511.

Harding, J. A., Shahbaz, M., Srinivas, & Kusiak, A. (2006). Data mining in manufacturing: a review. Journal of Manufacturing Science and Engineering, 128(4), 969-976. http://dx.doi.org/10.1115/1.2194554.

Hoang, N., & Nguyen, Q. (2020). A novel approach for automatic detection of concrete surface voids using image texture analysis and history-based adaptive differential evolution optimized support vector machine. Advances in Civil Engineering, 2020, 1-15. http://dx.doi.org/10.1155/2020/4190682.

Hsu, C.-Y. (2015). Clustering ensemble for identifying defective wafer bin map in semiconductor manufacturing. Mathematical Problems in Engineering, 2015, 1-11. http://dx.doi.org/10.1155/2015/707358.

Huang, Y., Qiu, C., Wang, X., Wang, S., & Yuan, K. (2020). A compact convolutional neural network for surface defect inspection. Sensors, 20(7), 1974. http://dx.doi.org/10.3390/s20071974. PMid:32244764.

Ihar, V., Mujeeb, A., Wenting, D., Marius, E., & Alexei, S. (2019). Detection defect in printed circuit boards using unsupervised feature extraction upon transfer learning. In 2019 International Conference on Cyberworlds (pp. 101-108). USA: IEEE.

Imoto, K., Nakai, T., Ike, T., Haruki, K., & Sato, Y. (2019). A CNN-based transfer learning method for defect classification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 32(4), 455-459. http://dx.doi.org/10.1109/TSM.2019.2941752.

Jacob, D. (2017). Quality 4.0 impact and strategy handbook: getting digitally connected quality management. Retrieved in 2021, May 24, from https://generisgp.com/2018/02/15/the-quality-4-0-impact-and-strategy-handbook/.

Jeong, E., Kim, J., Jang, W., Lim, H., Noh, H., & Choi, J. (2021). A more reliable defect detection and performance improvement method for panel inspection based on artificial intelligence. Journal of Information Display, 22(3), 127-136. http://dx.doi.org/10.1080/15980316.2021.1876174.

Jeong, Y. (2017). Semiconductor wafer defect classification using support vector machine with weighted dynamic time warping kernel function. Industrial Engineering & Management Systems, 16(3), 420-426. http://dx.doi.org/10.7232/iems.2017.16.3.420.

Jiang, H., Hu, Q., Zhi, Z., Gao, J., Gao, Z., Wang, R., He, S., & Li, H. (2021). Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition. Welding in the World, 65(4), 731-744. http://dx.doi.org/10.1007/s40194-020-01027-6.

Jiang, Y., Wu, J., & Zong, C. (2014). An effective diagnosis method for single and multiple defects detection in gearbox based on nonlinear feature selection and kernel-based extreme learning machine. Journal of Vibroengineering, 16(1), 499-512.

Ji-Deok, S., Young-Gyu, K., & Tae-Hyoung, P. (2018). SMT defect classification by feature extraction region optimization and machine learning. International Journal of Advanced Manufacturing Technology, 101(5-8), 1303-1313.

Jiju, A. (2020). Quality 4.0: taking quality to its next level. Industrial and Systems Engineering at Work, 52(6), 46-47. Retrieved in 2021, April 8, from https://www.proquest.com/docview/2411183836/abstract/CD8BFC5C54304FFEPQ/1?accountid=17203

Jingzhong, H., Kewen, X., Fan, Y., & Baokai, Z. (2018). Strip steel surface defects recognition based on socp optimized multiple kernel RVM. Mathematical Problems in Engineering, 2018, 1-8. http://dx.doi.org/10.1155/2018/9298017.

Kholief, E. A., Darwish, S. H., & Fors, M. N. (2017). Detection of steel surface defect based on machine learning using deep auto-encoder network. In Proceedings of the International Conference on Industrial Engineering and Operations Management. Rabat, Morocco, IEOM.

Kitchenham B. (2004). Procedure for undertaking systematic reviews: technical report. Keele; Eveleigh: Keele University; National ICT Australia Ltd.

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele; Durham: Keele University; University of Durham.

Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering: a tertiary study. Information and Software Technology, 52(8), 792-805. http://dx.doi.org/10.1016/j.infsof.2010.03.006.

Konovalenko, I., Maruschak, P., Brezinova, J., Viňáš, J., & Brezina, J. (2020). Steel surface defect classification using deep residual neural network. Metals, 10(6), 846. http://dx.doi.org/10.3390/met10060846.

Kusiak, A. (2001). Feature transformation methods in data mining. IEEE Transactions on Electronics Packaging Manufacturing, 24(3), 214-221. http://dx.doi.org/10.1109/6104.956807.

Lee, C. K. H., Choy, K. L., Ho, G. T. S., Chin, K. S., Law, K. M. Y., & Tse, Y. K. (2013). A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Systems with Applications, 40(7), 2435-2446. http://dx.doi.org/10.1016/j.eswa.2012.10.057.

Lee, H., & Ryu, K. (2020). Dual-kernel-based aggregated residual network for surface defect inspection in injection molding processes. Applied Sciences, 10(22), 8171. http://dx.doi.org/10.3390/app10228171.

Lee, J., & Lee, J. (2019). A reliable defect detection method for patterned wafer image using convolutional neural networks with the transfer learning. IOP Conference Series: Materials Science and Engineering, 647, 012010. http://dx.doi.org/10.1088/1757-899X/647/1/012010

Lee, S. Y., Tama, B. A., Moon, S. J., & Lee, S. (2019). Steel surface defect diagnostics using deep convolutional neural network and class activation map. Applied Sciences, 9(24), 5449. http://dx.doi.org/10.3390/app9245449.

Li, H., Cui, C., & Guo, H. (2019). Identification and classification of surface defects in polycrystalline diamond compact. In International Symposium on Precision Mechanical Measurements, Vol. 11343. Chongqing, China: SPIE. http://dx.doi.org/10.1117/12.2547602.

Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing, 30(6), 2525-2534. http://dx.doi.org/10.1007/s10845-018-1415-x.

Lin, Z., Ye, H., Zhan, B., & Huang, X. (2020). An efficient network for surface defect detection. Applied Sciences, 10(17), 6085. http://dx.doi.org/10.3390/app10176085.

Liu, T., Huang, L., & Chen, B. (2019). Real-time defect detection of laser additive manufacturing based on support vector machine. Journal of Physics: Conference Series, 1213(5), 052043. http://dx.doi.org/10.1088/1742-6596/1213/5/052043.

Liyun, X., Boyu, L., Hong, M., & Xingzhong, L. (2020). Improved faster R-CNN algorithm for defect detection in powertrain assembly line. Procedia CIRP, 93, 479-484. http://dx.doi.org/10.1016/j.procir.2020.04.031.

Ma, L., Xie, W., & Zhang, Y. (2019). Blister defect detection based on convolutional neural network for polymer lithium-ion battery. Applied Sciences, 9(6), 1085. http://dx.doi.org/10.3390/app9061085.

Mao, Q., Ma, H., Zhang, X., & Zhang, G. (2018). An improved skewness decision tree svm algorithm for the classification of steel cord conveyor belt defects. Applied Sciences, 8(12), 2574. http://dx.doi.org/10.3390/app8122574.

Mei, S., Wang, Y., & Wen, G. (2018). Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors, 18(4), 1064. http://dx.doi.org/10.3390/s18041064. PMid:29614813.

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Journal of Clinical Epidemiology, 62(10), 1006-1012. http://dx.doi.org/10.1016/j.jclinepi.2009.06.005. PMid:19631508.

Oliff, H., & Liu, Y. (2017). Towards Industry 4.0 utilizing data-mining techniques: a case study on quality improvement. Procedia CIRP, 63, 167-172. http://dx.doi.org/10.1016/j.procir.2017.03.311.

Ooi, M., Sok, H. K., Kuang, Y. C., Demidenko, S., & Chan, C. (2013). Defect cluster recognition system for fabricated semiconductor wafers. Engineering Applications of Artificial Intelligence, 26(3), 1029-1043. http://dx.doi.org/10.1016/j.engappai.2012.03.016.

Patel, H. P., & Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computers and Applications, 95(9), 6-8. http://dx.doi.org/10.5120/16620-6472.

Perzyk, M. (2006). Data mining in foundry production. In K. Świątkowski (Ed.), Research in Polish metallurgy at the beginning of XXI century (pp. 255–275). Poland: Committee of Metallurgy of the Polish Academy of Sciences.

Perzyk, M., Biernacki, R., & Kozlowski, J. (2007). Data mining in manufacturing: methods, potentials, limitations. In Proceedings of the 2007 Advances in Production Engineering Conference (pp. 147-156). Poland: Akapit.

ProQuest. (2021). Retrieved in 14 March 2021, from https://www.proquest.com/.

Radhika, N., Senapathi, S. B., Subramaniam, R., Subramany, R., & Vishnu, K. N. (2013). Pattern recognition based surface roughness prediction in turning hybrid metal matrix composite using random forest algorithm. Industrial Lubrication and Tribology, 65(5), 311-319. http://dx.doi.org/10.1108/ILT-02-2011-0015.

Raluca, D. (2021). Knowledge management systems in Quality 4.0. MATEC Web of Conferences, 342, 09003.

Romli, I., Pardamean, T., Butsianto, S., Wiyatno, T. N., & Mohamad, E. (2021). Naive bayes algorithm implementation based on particle swarm optimization in analyzing the defect product. Journal of Physics: Conference Series, 1845(1), 012020. http://dx.doi.org/10.1088/1742-6596/1845/1/012020.

Scopus. (2021). Retrieved in 12 March 2021, from http://www.scopus.com/.

Shi, J., Li, Z., Zhu, T., Wang, D., & Ni, C. (2020a). Defect detection of industry wood veneer based on NAS and multi-channel Mask R-CNN. Sensors, 20(16), 4398. http://dx.doi.org/10.3390/s20164398. PMid:32781740.

Shi, W., Zhang, L., Li, Y., & Liu, H. (2020b). Adversarial semi-supervised learning method for printed circuit board unknown defect detection. Journal of Engineering, 2020(13), 505-510. http://dx.doi.org/10.1049/joe.2019.1181.

Shin, S., Jin, C., Yu, J., & Rhee, S. (2020). Real-time detection of weld defects for automated welding process base on deep neural network. Metals, 10(3), 389. http://dx.doi.org/10.3390/met10030389.

Shon, H. S., Batbaatar, E., Cho, W., & Choi, S. G. (2021). Unsupervised pre-training of imbalanced data for identification of wafer map defect patterns. IEEE Access: Practical Innovations, Open Solutions, 9, 52352-52363. http://dx.doi.org/10.1109/ACCESS.2021.3068378.

Song, J., Kim, Y., & Park, T. (2019). SMT defect classification by feature extraction region optimization and machine learning. International Journal of Advanced Manufacturing Technology, 101(5-8), 1303-1313. http://dx.doi.org/10.1007/s00170-018-3022-6.

Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31(3), 759-776. http://dx.doi.org/10.1007/s10845-019-01476-x.

Taha, K., Salah, K., & Yoo, P. D. (2018). Clustering the dominant defective patterns in semiconductor wafer maps. IEEE Transactions on Semiconductor Manufacturing, 31(1), 156-165. http://dx.doi.org/10.1109/TSM.2017.2768323.

Takada, Y., Shiina, T., Usami, H., Iwahori, Y., & Bhuyan, M. K. (2017). Defect detection and classification of electronic circuit boards using keypoint extraction and CNN features. In The Ninth International Conferences on Pervasive Patterns and Applications. Athens, Greece: Iaria.

Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37(2), 517-527. http://dx.doi.org/10.1016/j.jmsy.2015.04.008.

Wang, T., Chen, Y., Qiao, M., & Snoussi, H. (2018). Afast and robust convolutional neural network-based defect detection model in product quality control. International Journal of Advanced Manufacturing Technology, 94(9-12), 3465-3471. http://dx.doi.org/10.1007/s00170-017-0882-0.

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962. http://dx.doi.org/10.1080/00207543.2018.1444806.

Xu, L., Lv, S., Deng, Y., & Li, X. (2020). A weakly supervised surface defect detection based on convolutional neural network. IEEE Access: Practical Innovations, Open Solutions, 8, 42285-42296. http://dx.doi.org/10.1109/ACCESS.2020.2977821.

Yang, J., Li, S., Wang, Z., & Yang, G. (2019). Real-time tiny part defect detection system in manufacturing using deep learning. IEEE Access: Practical Innovations, Open Solutions, 7, 89278-89291. http://dx.doi.org/10.1109/ACCESS.2019.2925561.

Yang, S., Li, X., Jia, X., Wang, Y., Zhao, H., & Lee, J. (2020). Deep learning-based intelligent defect detection of cutting wheels with industrial images in manufacturing. Procedia Manufacturing, 48, 902-907. http://dx.doi.org/10.1016/j.promfg.2020.05.128.

Yapi, D., Allili, M. S., & Baaziz, N. (2017). Automatic fabric defect detection using learning-based local textural distributions in the contourlet domain. IEEE Transactions on Automation Science and Engineering, 15(3), 1017-1026.

Yu, L., Wang, Z., & Duan, Z. (2019). Detecting gear surface defects using background-weakening method and convolutional neural network. Journal of Sensors, 2019, 1-13. http://dx.doi.org/10.1155/2019/3140980.

Web of Science. (2021). Retrieved in 15 March 2021, from http://isiknowledge.com/.

Yuan, T., Bae, S. J., & Park, J. I. (2010). Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering. International Journal of Advanced Manufacturing Technology, 51(5-8), 671-683. http://dx.doi.org/10.1007/s00170-010-2647-x.

Yuan, T., Kuo, W., & Bae, S. J. (2011). Detection of spatial defect patterns generated in semiconductor fabrication processes. IEEE Transactions on Semiconductor Manufacturing, 24(3), 24. http://dx.doi.org/10.1109/TSM.2011.2154870.

Zhang, J., Lu, C., Wang, J., Wang, L., & Yue, X. (2019). Concrete cracks detection based on FCN with Dilated convolution. Applied Sciences, 9(13), 2686. http://dx.doi.org/10.3390/app9132686.

Zhang, K., & Shen, H. (2021). Solder joint defect detection in the connectors using improved faster‐RCNN algorithm. Applied Sciences, 11(2), 576. http://dx.doi.org/10.3390/app11020576.

Zhao, W., Huang, H., Li, D., Chen, F., & Cheng, W. (2020a). Pointer defect detection based on transfer learning and improved cascade-RCNN. Sensors, 20(17), 4939. http://dx.doi.org/10.3390/s20174939. PMid:32882801.

Zhao, S., Yin, L., Zhang, J., Wang, J., & Zhong, R. (2020b). Real-time fabric defect detection based onmulti-scale convolutional neural network. IET Collaborative Intelligent Manufacturing, 2(4), 189-196. http://dx.doi.org/10.1049/iet-cim.2020.0062.

Zhao, W., Chen, F., Huang, H., Li, D., & Cheng, W. (2021). A new steel defect detection algorithm based on deep learning. Computational Intelligence and Neuroscience, 2021, 5592878. http://dx.doi.org/10.1155/2021/5592878. PMid:33824656.

Zhu, Y., Yang, R., He, Y., Ma, J., Guo, H., Yang, Y., & Zhang, L. (2021). A Lightweight multiscale attention semantic segmentation algorithm for detecting laser welding defects on safety vent of power battery. IEEE Access : Practical Innovations, Open Solutions, 9, 39245-39254. http://dx.doi.org/10.1109/ACCESS.2021.3064180.
 


Submitted date:
08/03/2021

Accepted date:
12/08/2021

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