Abstract
Manufacturing and production processes have become more complicated and usually consist of multiple stages to meet customers' requirements. This poses big challenges for quality monitoring due to the vast amount of data and the interactive effects of many factors on the final product quality. This research introduces a smart real-time quality monitoring and inspection framework capable of predicting and determining the quality deviations for complex and multistage manufacturing systems as early as possible; introduces a hybrid quality inspection approach based on both predictive models and physical inspection in order to enhance the quality monitoring process, save resources, reduce inspection time and costs. Several supervised and unsupervised machine learning techniques such as support vector machine, random forest, artificial neural network, principal component analysis were used to build the quality monitoring model with considering the cumulative effects of different manufacturing stages and the unbalance and dynamic nature of the manufacturing processes. A complex semiconductor manufacturing dataset was used to verify and assess the performance of the proposed framework. The results prove the ability of the suggested framework to enhance the quality monitoring process in multistage manufacturing systems and the ability of the hybrid quality inspection approach to reduce the inspection volume and cost.
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References
Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley. Computational Statistics, 2(4), 433–459. https://doi.org/10.1002/wics.101
Al-Kharaz, M., Ananou, B., Ouladsine, M., Combal, M., & Pinaton, J. (2019). Quality prediction in semiconductor manufacturing processes using multilayer perceptron feedforward artificial neural network. In: 8th international confernece on systems and control (ICSC 2019), pp. 423–428. https://doi.org/10.1109/ICSC47195.2019.8950664
Amini, M., & Chang, S. I. (2018). MLCPM: A process monitoring framework for 3D metal printing in industrial scale. Computers and Industrial Engineering, 124, 322–330. https://doi.org/10.1016/j.cie.2018.07.041
Amini, M., & Chang, S. I. (2020). Intelligent data-driven monitoring of high dimensional multistage manufacturing processes. International Journal of Mechatronics and Manufacturing Systems, 13(4), 299–322. https://doi.org/10.1504/IJMMS.2020.112352
Arif, F., Suryana, N., & Hussin, B. (2013a). Cascade quality prediction method using multiple PCA+ID3 for multi-stage manufacturing system. IERI Procedia, 4, 201–207. https://doi.org/10.1016/j.ieri.2013.11.029
Arif, F., Suryana, N., & Hussin, B. (2013b). A data mining approach for developing quality prediction model in multi-stage manufacturing. International Journal of Computer Applications, 69(22), 35–40. https://doi.org/10.5120/12106-8375
Aydemir, G., & Acar, B. (2020). Anomaly monitoring improves remaining useful life estimation of industrial machinery. Journal of Manufacturing Systems, 56, 463–469. https://doi.org/10.1016/j.jmsy.2020.06.014
Bai, Y., Sun, Z., Zeng, B., Long, J., Li, L., de Oliveira, J. V., & Li, C. (2019). A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. Journal of Intelligent Manufacturing, 30, 2245–2256. https://doi.org/10.1007/s10845-017-1388-1
Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5–37. https://doi.org/10.1016/j.jsp.2009.10.001
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. https://doi.org/10.1007/s10845-020-01567-0
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Chawla, N. V. (2009). Data mining for imbalanced datasets: An overview. In O. Maimon & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook (pp. 875–886). Boston, MA: Springer. https://doi.org/10.1007/978-0-387-09823-4_45
Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1–13. https://doi.org/10.1016/j.jii.2017.08.001
Cohen, Y., & Singer, G. (2021). A smart process controller framework for Industry 40 settings. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01748-5
Colaco, S., Kumar, S., Tamang, A., & Biju, V. G. (2019). A review on feature selection algorithms. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (Eds.), Emerging research in computing, information, communication and applications. Advances in intelligent systems and computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_11
Cuartas, M., Ruiz, E., Ferreño, D., Setién, J., Arroyo, V., & Gutiérrez-Solana, F. (2020). Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01623-9
Cunningham, P., & Delany, S. J. (2007). k-Nearest neighbour classifiers. Multi Classification System, 34(8), 1–17.
Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263. https://doi.org/10.1016/j.asoc.2020.106263
Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50, 92–111. https://doi.org/10.1016/j.inffus.2018.10.005
Escobar, C. A., McGovern, M. E., & Morales-Menendez, R. (2021). Quality 4.0: a review of big data challenges in manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01765-4
Filz, M. A., Gellrich, S., Turetskyy, A., Wessel, J., Herrmann, C., & Thiede, S. (2020). Virtual quality gates in manufacturing systems: Framework, implementation and potential. Journal of Manufacturing and Materials Processing. https://doi.org/10.3390/jmmp4040106
Foumani, M., Razeghi, A., & Smith-Miles, K. (2020). Stochastic optimization of two-machine flow shop robotic cells with controllable inspection times: From theory toward practice. Robotics and Computer-Integrated Manufacturing, 61, 101822. https://doi.org/10.1016/j.rcim.2019.101822
Foumani, M., & Tavakkoli-Moghaddam, R. (2019). A scalarization-based method for multiple part-type scheduling of two-machine robotic systems with non-destructive testing technologies. Iranian Journal of Operations Research, 10(1), 1–17. https://doi.org/10.29252/iors.10.1.1
García, V., Sánchez, J. S., Rodríguez-Picón, L. A., Méndez-González, L. C., de Ochoa-Domínguez, H., & J. . (2019). Using regression models for predicting the product quality in a tubing extrusion process. Journal of Intelligent Manufacturing, 30, 2535–2544. https://doi.org/10.1007/s10845-018-1418-7
García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2010). Pattern classification with missing data: A review. Neural Computing and Applications, 19, 263–282. https://doi.org/10.1007/s00521-009-0295-6
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The Role of Machine Learning. IEEE Access, 5, 20590–20616. https://doi.org/10.1109/ACCESS.2017.2756872
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pp. 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969
Hirai, T., & Kano, M. (2015). Adaptive virtual metrology design for semiconductor dry etching process through locally weighted partial least squares. IEEE Transactions on Semiconductor Manufacturing, 28(2), 137–144. https://doi.org/10.1109/TSM.2015.2409299
Janitza, S., Strobl, C., & Boulesteix, A. L. (2013). An AUC-based permutation variable importance measure for random forests. BMC Bioinformatics, 14, 1. https://doi.org/10.1186/1471-2105-14-119
Kang, P., Lee, H.-J., Cho, S., Kim, D., Park, J., Park, C. K., & Doh, S. (2009). A virtual metrology system for semiconductor manufacturing. Expert Systems with Application, 36, 12554–12561. https://doi.org/10.1016/j.eswa.2009.05.053
Kang, S., & Kang, P. (2017). An intelligent virtual metrology system with adaptive update for semiconductor manufacturing. Journal of Process Control, 52, 66–74. https://doi.org/10.1016/j.jprocont.2017.02.002
Kao, H. A., Hsieh, Y. S., Chen, C. H., & Lee, J. (2017). Quality prediction modeling for multistage manufacturing based on classification and association rule mining. In: MATEC web of conferences, the 2nd international conference on precision machinery and manufacturing technology (ICPMMT 2017), 123, 00029. https://doi.org/10.1051/matecconf/201712300029
Kim, D., Kang, P., Cho, S., Lee, H. J., & Doh, S. (2012). Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Systems with Applications, 39, 4075–4083. https://doi.org/10.1016/j.eswa.2011.09.088
Köksal, G., Batmaz, I., & Testik, M. C. (2011). A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications, 38(10), 13448–13467. https://doi.org/10.1016/j.eswa.2011.04.063
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31(3), 249–268.
Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2006). Handling imbalanced datasets : A review. GESTS International Transactions on Computer Science and Engineering, 30, 25–36.
Lee, D. H., Yang, J. K., & Kim, K. J. (2020). Multiresponse optimization of a multistage manufacturing process using a patient rule induction method. Quality and Reliability Engineering International, 36(6), 1982–2002. https://doi.org/10.1002/qre.2669
Lee, D., Yang, J., Lee, C., & Kim, K. (2019). A data-driven approach to selection of critical process steps in the semiconductor manufacturing process considering missing and imbalanced data. Journal of Manufacturing Systems, 52, 146–156. https://doi.org/10.1016/j.jmsy.2019.07.001
Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157–170. https://doi.org/10.1016/j.bushor.2019.10.005
Lee, J. (2020). Industrial AI. Springer, Singapore. https://doi.org/10.1007/978-981-15-2144-7
Lee, J., Lapira, E., Yang, S., & Kao, A. (2013). Predictive manufacturing system - Trends of next-generation production systems. IFAC Proceedings, 46(7), 150–156. https://doi.org/10.3182/20130522-3-BR-4036.00107
Lee, W. J., Xia, K., Denton, N. L., Ribeiro, B., & Sutherland, J. W. (2021). Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery. Journal of Intelligent Manufacturing, 32, 393–406. https://doi.org/10.1007/s10845-020-01578-x
Li, F., Wu, J., Dong, F., Lin, J., Sun, G., Chen, H., & Shen, J. (2018). Ensemble machine learning systems for the estimation of steel quality control. IEEE International Conference on Big Data (big Data), 2018, 2245–2252. https://doi.org/10.1109/BigData.2018.8622583
Li, X., Jia, X., Yang, Q., & Lee, J. (2020). Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 31, 2003–2017. https://doi.org/10.1007/s10845-020-01549-2
Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP, 7, 193–198. https://doi.org/10.1016/j.procir.2013.05.033
Lipton, Z. C., & Steinhardt, J. (2019). Troubling trends in machine-learning scholarship. Queue, 17(1), 1–15. https://doi.org/10.1145/3317287.3328534
Malaca, P., Rocha, L. F., Gomes, D., Silva, J., & Veiga, G. (2019). Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry. Journal of Intelligent Manufacturing, 30, 351–361. https://doi.org/10.1007/s10845-016-1254-6
McCann, M., & Johnston, A. (2008). SECOM dataset UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/SECOM
Melhem, M., Ananou, B., Djeziri, M., Ouladsine, M., & Pinaton, J. (2015). Prediction of the wafer quality with respect to the production equipments data. IFAC-PapersOnLine, 48, 78–84.
Mineo, C., Pierce, S. G., Nicholson, P. I., & Cooper, I. (2016). Robotic path planning for non-destructive testing - A custom MATLAB toolbox approach. Robotics and Computer-Integrated Manufacturing, 37, 1–12. https://doi.org/10.1016/j.rcim.2015.05.003
Moldovan, D., Cioara, T., Anghel, I., & Salomie, I. (2017). Machine learning for sensor-based manufacturing processes. In: 2017 13th IEEE international conference on intelligent computer communication and processing (ICCP), pp. 147–54. https://doi.org/10.1109/ICCP.2017.8116997
Munirathinam, S., & Ramadoss, B. (2016). Predictive models for equipment fault detection in the semiconductor manufacturing process. International Journal of Engineering and Technology, 8(4), 273–285. https://doi.org/10.7763/ijet.2016.v8.898
Nakagawa, S. (2015). Missing data: mechanisms, methods, and messages. Ecological Statistics: Contemprary Theory and Application. https://doi.org/10.1093/acprof:oso/9780199672547.003.0005
Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko-Boateng, O. (2021). Applications of artificial intelligence in engineering and manufacturing: a systematic review. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01771-6
Ou, X., Huang, J., Chang, Q., Hucker, S., & Lovasz, J. G. (2020). First time quality diagnostics and improvement through data analysis: A study of a crankshaft line. Procedia Manufacturing, 49, 2–8. https://doi.org/10.1016/j.promfg.2020.06.003
Peres, R. S., Barata, J., Leitao, P., & Garcia, G. (2019). Multistage quality control using machine learning in the automotive industry. IEEE Access, 7, 79908–79916. https://doi.org/10.1109/ACCESS.2019.2923405
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56, 117–132. https://doi.org/10.1016/j.jmsy.2020.05.013
Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. arXiv: 1811.12808
Rezaei-Malek, M., Mohammadi, M., Dantan, J. Y., Siadat, A., & Tavakkoli-Moghaddam, R. (2019). A review on optimisation of part quality inspection planning in a multi-stage manufacturing system. International Journal of Production Research, 57, 4880–4897. https://doi.org/10.1080/00207543.2018.1464231
RStudio Team. (2020). RStudio: Integrated Development Environment for R
Salem, M., Taheri, S., & Yuan, J. S. (2018). An experimental evaluation of fault diagnosis from imbalanced and incomplete data for smart semiconductor manufacturing. Big Data and Cognitive Computing, 2(4), 30. https://doi.org/10.3390/bdcc2040030
Sarkar, B., & Saren, S. (2016). Product inspection policy for an imperfect production system with inspection errors and warranty cost. European Journal of Operational Research, 248(1), 263–271. https://doi.org/10.1016/j.ejor.2015.06.021
Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing. Advanced Engineering Informatics, 45, 101101. https://doi.org/10.1016/j.aei.2020.101101
Tadist, K., Najah, S., Nikolov, N. S., Mrabti, F., & Zahi, A. (2019). Feature selection methods and genomic big data: a systematic review. Journal of Big Data. https://doi.org/10.1186/s40537-019-0241-0
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006
Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
Thiede, S., Turetskyy, A., Kwade, A., Kara, S., & Herrmann, C. (2019). Data mining in battery production chains towards multi-criterial quality prediction. CIRP Annals, 68, 463–466. https://doi.org/10.1016/j.cirp.2019.04.066
Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36, 11994–12000. https://doi.org/10.1016/j.eswa.2009.05.029
Turetskyy, A., Thiede, S., Thomitzek, M., von Drachenfels, N., Pape, T., & Herrmann, C. (2020). Toward data-driven applications in lithium-ion battery cell manufacturing. Energy Technology, 8(2), 1900136. https://doi.org/10.1002/ente.201900136
Venkatesh, B., & Anuradha, J. (2019). A review of feature selection and its methods. Cybernetics and Information Technologies, 19(1), 3–26. https://doi.org/10.2478/CAIT-2019-0001
Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. (2019). A review of machine learning for the optimization of production processes. The International Journal of Advanced Manufacturing Technology, 104, 1889–1902. https://doi.org/10.1007/s00170-019-03988-5
Wuest, T., Irgens, C., & Thoben, K. D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25, 1167–1180. https://doi.org/10.1007/s10845-013-0761-y
Xu, S., Lu, B., Baldea, M., Edgar, T. F., Wojsznis, W., Blevins, T., & Nixon, M. (2015). Data cleaning in the process industries. Reviews in Chemical Engineering, 31(5), 453–490. https://doi.org/10.1515/revce-2015-0022
Yin, X., He, Z., Niu, Z., & Li, Z. (2018). A hybrid intelligent optimization approach to improving quality for serial multistage and multi-response coal preparation production systems. Journal of Manufacturing Systems, 47, 199–216. https://doi.org/10.1016/j.jmsy.2018.05.006
Yugma, C., Blue, J., Dauzère-Pérès, S., & Obeid, A. (2015). Integration of scheduling and advanced process control in semiconductor manufacturing: review and outlook. Journal of Scheduling, 18, 195–205. https://doi.org/10.1007/s10951-014-0381-1
Zhai, J., Xu, X., Xie, C., & Luo, M. (2004). Fuzzy control for manufacturing quality based on variable precision rough set. In: Fifth world congress on intelligent control and automation (IEEE Cat. No.04EX788), Vol. 3, pp. 2347–2351. https://doi.org/10.1109/wcica.2004.1342013
Zhang, W., Li, X., & Ding, Q. (2019). Deep residual learning-based fault diagnosis method for rotating machinery. ISA Transactions, 95, 295–305. https://doi.org/10.1016/j.isatra.2018.12.025
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Ismail, M., Mostafa, N.A. & El-assal, A. Quality monitoring in multistage manufacturing systems by using machine learning techniques. J Intell Manuf 33, 2471–2486 (2022). https://doi.org/10.1007/s10845-021-01792-1
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DOI: https://doi.org/10.1007/s10845-021-01792-1