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Research Article

Smart seru production system for Industry 4.0: a conceptual model based on deep learning for real-time monitoring and controlling

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Pages 385-407 | Received 23 Oct 2021, Accepted 02 May 2022, Published online: 29 May 2022

References

  • Abobakr, A., D. Nahavandi, M. Hossny, J. Iskander, M. Attia, S. Nahavandi, and M. Smets. 2019. “RGB-D Ergonomic Assessment System of Adopted Working Postures.” Applied Ergonomics 80: 75–88. doi:10.1016/j.apergo.2019.05.004.
  • Ahn, J., D. Shin, K. Kim, and J. Yang. 2017. “Indoor Air Quality Analysis Using Deep Learning with Sensor Data.” Sensors 17 (11): 2476. doi:10.3390/s17112476.
  • Akyol, K. 2020. “Comparing of Deep Neural Networks and Extreme Learning Machines Based on Growing and Pruning Approach.” Expert Systems with Applications 140: 112875. doi:10.1016/j.eswa.2019.112875.
  • Amira, L., S. Nejib, J. Fadhel, C. Yassine, and C. Morched. 2017. “Ergonomic Analysis in a Company of Clothing and Evaluation of an Ergonomic Index Related to MSDs.” International Journal of Recent Research and Applied Studies 31 (2): 46–53.
  • Asghari, V., Y. F. Leung, and S. C. Hsu. 2020. “Deep Neural Network Based Framework for Complex Correlations in Engineering Metrics.” Advanced Engineering Informatics 44: 101058. doi:10.1016/j.aei.2020.101058.
  • Athira, V., P. Geetha, R. Vinayakumar, and K. P. Soman. 2018. “DeepAirNet: Applying Recurrent Networks for Air Quality Prediction.” Procedia Computer Science 132: 1394–1403. doi:10.1016/j.procs.2018.05.068.
  • Bay, M., and E. Çiçek. 2007.“Tam Zamanında Üretim Sistemlerinde Hata Önleyiciler: Poka.” Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi 3: 53–62.
  • Bianchi, V. M. B., I. D. Munari, I. D. Munari, I. D. Munari, I. D. Munari, and I. De Munari. 2019. “IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment.” IEEE Internet of Things Journal 6 (5): 8553–8562. doi:10.1109/JIOT.2019.2920283.
  • Bilen, H., B. Fernando, E. Gavves, A. Vedaldi, and S. Gould. 2016. Dynamic image networks for action recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 17-19 June 1997. San Juan, PR, USA. IEEE. 3034–3042.
  • Chen, J., G. Zeng, W. Zhou, W. Du, and K. Lu. 2018. “Wind Speed Forecasting Using Nonlinear-Learning Ensemble of Deep Learning Time Series Prediction and Extremal Optimization.” Energy Conversion and Management 165: 681–695. doi:10.1016/j.enconman.2018.03.098.
  • Dairi, A., F. Harrou, M. Senouci, and Y. Sun. 2018. “Unsupervised Obstacle Detection in Driving Environments Using Deep-Learning-Based Stereovision.” Robotics and Autonomous Systems 100: 287–301. doi:10.1016/j.robot.2017.11.014.
  • Diro, A. A., and N. Chilamkurti. 2018. “Distributed Attack Detection Scheme Using Deep Learning Approach for Internet of Things.” Future Generation Computer Systems 82: 761–768. doi:10.1016/j.future.2017.08.043.
  • Dong, Y. 2018. “An Application of Deep Neural Networks to the In-Flight Parameter Identification for Detection and Characterization of Aircraft Icing.” Aerospace Science and Technology 77: 34–49. doi:10.1016/j.ast.2018.02.026.
  • ElMaraghy, M., and W. ElMaraghy. 2016. “Smart Adaptable Assembly Systems.” Procedia CIRP 44: 4–13. doi:10.1016/j.procir.2016.04.107.
  • Estrada, G., C. Riba, and J. Lloveras. 2007. “An Approach to Avoid Quality Assembly Issues since Product Design Stage.” International Conference on Engineering Design, Paris, August 28–31.
  • Giuliano, I., and T. Taurino. 2014. “Augmented-Reality Application for a Seru-Type Manufacturing as Lean as Possible.” 15th Working Conference on Virtual Enterprises (PROVE), The Netherlands.
  • Guo, Y., Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew. 2016. “Deep Learning for Visual Understanding: A Review.” Neurocomputing 187: 27–48. doi:10.1016/j.neucom.2015.09.116.
  • Hegelich, S. 2017. “Deep Learning and Punctuated Equilibrium Theory.” Cognitive Systems Research 45: 59–69. doi:10.1016/j.cogsys.2017.02.006.
  • Huang, K., Y. Shi, F. Zhao, Z. Zhang, and S. Tu. 2020. “Multiple Instance Deep Learning for Weakly-Supervised Visual Object Tracking.” Signal Processing: Image Communication 84: 1–6.
  • Jagdale, K. N., S. A. Patil, and S. K. Parchandekar. 2015. “A Smart Manufacturing Execution System.” IOSR Journal of Electrical and Electronics Engineering 10 (3): 14–28.
  • Ji, S., W. Xu, M. Yang, and K. Yu. 2013. “3D Convolutional Neural Networks for Human Action Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1): 221–231. doi:10.1109/TPAMI.2012.59.
  • Karim, M. M., D. Doell, R. Lingard, Z. Yin, M. C. Leu, and R. Qin. 2019. “A Region-Based Deep Learning Algorithm for Detecting and Tracking Objects in Manufacturing Plants.” Procedia Manufacturing 39: 168–177. doi:10.1016/j.promfg.2020.01.289.
  • Kraus, M., and S. Feuerriegel. 2017. “Decision Support from Financial Disclosures with Deep Neural Networks and Transfer Learning.” Decision Support Systems 104: 38–48. doi:10.1016/j.dss.2017.10.001.
  • Kurushima, K. 2017. “Basic Information about Seru Production Method.” Tech Note [ in Japanese]. Accessed 29 February 2020. https://www.ipros.jp/technote/basic-cell-production/
  • LeCun, Y., Y. Bengio, and G. E. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
  • Leng, J., Q. Chen, N. Mao, and P. Jiang. 2018. “Combining Granular Computing Technique with Deep Learning for Service Planning under Social Manufacturing Contexts.” Knowledge-Based Systems 143: 295–306. doi:10.1016/j.knosys.2017.07.023.
  • Li, X., D. Li, X. Wu, H. Zhang, and Y. Yin. 2017. A Cooperative Co-Evolution Approach for A Line-Seru Conversion Problem, 1406–1411. San Sebastian: IEEE Congress on Evolutionary Computation (CEC), IEEE.
  • Lian, J., C. Liu, W. Li, and Y. Yin. 2018. “A Multi-Skilled Worker Assignment Problem in Seru Production Systems considering the Worker Heterogeneity.” Computers & Industrial Engineering 118: 366–382. doi:10.1016/j.cie.2018.02.035.
  • Liu, C., J. Lian, Y. Yin, and W. Li. 2010. “Seru Seisan‐ An Innovation of the Production Management Mode in Japan.” Asian Journal of Technology Innovation 18 (2): 89–113. doi:10.1080/19761597.2010.9668694.
  • Liu, C., W. Li, J. Lian, and Y. Yin. 2012. “Reconfiguration of Assembly Systems: From Conveyor Assembly Line to Serus.” Journal of Manufacturing Systems 31 (3): 312–325. doi:10.1016/j.jmsy.2012.02.003.
  • Liu, C., N. Yang, W. Li, J. Li, S. Evans, and Y. Yin. 2013. “Training and Assignment of Multi-Skilled Workers for Implementing Seru Production Systems.” The International Journal of Advanced Manufacturing Technology 69 (5–8): 937–959. doi:10.1007/s00170-013-5027-5.
  • Liu, C., K. E. Stecke, J. Lian, and Y. Yin. 2014. “An Implementation Framework for Seru Production.” International Transactions in Operational Research 21 (1): 1–19. doi:10.1111/itor.12014.
  • Lore, K. G., D. Stoecklein, M. Davies, B. Ganapathysubramanian, and S. Sarkar. 2018. “A Deep Learning Framework for Causal Shape Transformation.” Neural Networks 98: 305–317. doi:10.1016/j.neunet.2017.12.003.
  • Ma, W., Y. Wu, F. Cen, and G. Wang. 2020. “MDFN: Multi-Scale Deep Feature Learning Network for Object Detection.” Pattern Recognition 100: 1–13. doi:10.1016/j.patcog.2019.107149.
  • Martín, A., R. Lara-Cabrera, F. Fuentes-Hurtedo, V. Naranjo, and D. Camacho. 2018. “EvoDeep: A New Evolutionary Approach for Automatic Deep Neural Networks Parametrisation.” Journal of Parallel and Distributed Computing 117: 180–191. doi:10.1016/j.jpdc.2017.09.006.
  • Mhallaa, A., T. Chateau, and N. E. B. Amara. 2019. “Spatio-Temporal Object Detection by Deep Learning: Video-Interlacing to Improve Multi-Object Tracking.” Image and Vision Computing 88: 120–131. doi:10.1016/j.imavis.2019.03.002.
  • Moews, B., J. M. Herrmann, and G. Ibikunle. 2019. “Lagged Correlation-Based Deep Learning for Directional Trend Change Prediction in Financial Time Series.” Expert Systems with Applications 120: 197–206. doi:10.1016/j.eswa.2018.11.027.
  • Mohsen, H., E. A. El-Dahshan, E. M. El-Horbaty, and A. M. Salem. 2018. “Classification Using Deep Learning Neural Networks for Brain Tumors.” Future Computing and Informatics Journal 3 (1): 68–71. doi:10.1016/j.fcij.2017.12.001.
  • Nath, N. D. 2017. “Construction Ergonomic Risk and Productivity Assessment Using Mobile Technology and Machine Learning.” Graduate Thesis, Missouri State University.
  • Nishanth, R., M. V. Muthukumar, and A. Arivanantham. 2015. “Ergonomic Workplace Evaluation for Assessing Occupational Risks in Multistage Pump Assembly.” International Journal of Computer Applications 113 (9): 9–13. doi:10.5120/19852-1764.
  • Oishi A and Yagawa G. (2017). Computational mechanics enhanced by deep learning. Computer Methods in Applied Mechanics and Engineering, 327: 327–351. doi:10.1016/j.cma.2017.08.040.
  • Pérez-Hernández, F., S. Tabik, A. Lamas, R. Olmos, H. Fujita, and F. Herrera. 2020. “Object Detection Binary Classifiers Methodology Based on Deep Learning to Identify Small Objects Handled Similarly: Application in Video Surveillance.” Knowledge-Based Systems 194: 1–10. doi:10.1016/j.knosys.2020.105590.
  • Pi, Y., N. D. Nath, and A. H. Behzadan. 2020. “Convolutional Neural Networks for Object Detection in Aerial Imagery for Disaster Response and Recovery.” Advanced Engineering Informatics 43: 1–14. doi:10.1016/j.aei.2019.101009.
  • Ren, H., and D. Wang. 2019. “Analysis of the Effect of the Line-Seru Conversion on the Waiting Time with Batch Arrival.” Hindawi Mathematical Problems in Engineering 2019: 1–13.
  • Rodrigues, P., I. Markou, and F. C. Pereira. 2019. “Combining Time-Series and Textual Data for Taxi Demand Prediction in Event Areas: A Deep Learning Approach.” Information Fusion 49: 120–129. doi:10.1016/j.inffus.2018.07.007.
  • Ronao, C. A., and S. B. Cho. 2016. “Human Activity Recognition with Smartphone Sensors Using Deep Learning Neural Networks.” Expert Systems with Applications 59: 235–244. doi:10.1016/j.eswa.2016.04.032.
  • Shi, H., M. Xu, Q. Ma, C. Zhang, R. Li, and F. Li. 2017. “A Whole System Assessment of Novel Deep Learning Approach on Short-Term Load Forecasting.” Energy Procedia 142: 2791–2796. doi:10.1016/j.egypro.2017.12.423.
  • Siegel, N. G. 2019. Engineering Project Management. Hoboken, NJ, USA: Wiley.
  • Sing, R., J. K. Dhillon, A. K. S. Kushwaha, and R. Srivastava. 2019. “Depth Based Enlarged Temporal Dimension of 3D Deep Convolutional Network for Activity Recognition.” Multimedia Tools and Applications 78 (21): 30599–30614. doi:10.1007/s11042-018-6425-3.
  • Sing, T., and D. K. Vishwakarma. 2019. Human activity recognition in video benchmarks: A survey. In Advances in Signal Processing and Communication, edited by Rawat, B., Trivedi, A., Manhas, S., Karwal, V. 526, 247–259, Singapore: Springer. doi:10.1007/978-981-13-2553-3_24.
  • Singh, S. 2017. “A Study on Seru Production System.” International Journal of Applied Research in Science and Engineering: 83–89. http://www.ijarse.org/images/scripts/201719.pdf.
  • Soah, P. W., J. W. Chang, and J. W. Huang. 2018. “Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations.” IEEE Access 6: 38186–38199. doi:10.1109/ACCESS.2018.2849820.
  • Stecke, K. E., Y. Yin, I. Kaku, and Y. Murase. 2012. “Seru: The Organizational Extension of JIT for a Super-Talent Factory.” International Journal of Strategic Decision Sciences 3 (1): 105–118. doi:10.4018/jsds.2012010104.
  • Stecke, K. E., Y. Yin, and I. Kaku. 2014. “Seru Production: An Extension of Just-in-Time Approach for Volatile Business Environments.” In Analytical Approaches to Strategic Decision-Making: Interdisciplinary Considerations, edited by M. Tavana, 45–58, USA.
  • Sun, W., Q. Li, C. Huo, Y. Yu, and K. Ma. 2016. “Formulations, Features of Solution Space, and Algorithms for Line-Pure Seru System Conversion.” Mathematical Problems in Engineering 2016: 1–14.
  • Tabata, M. 2017. “What is the Seru Production Method? -Knows the Characteristics of the Seru Production and a Successful Introduction Mechanism-.” Kaizen Base [ in Japanese]. Accessed 29 February 2020. https://www.kaizen-base.com/contents/kall-42930/
  • Villa, A., and T. Taurino. 2013. “From JIT to Seru, for a Production as Lean as Possible.” Procedia Engineering 63: 956–965. doi:10.1016/j.proeng.2013.08.172.
  • Wang, C., and P. Jiang. 2017. “Deep Neural Networks Based Order Completion Time Prediction by Using Real-Time Job Shop Rfid Data.” Journal of Intelligent Manufacturing 30 (3): 1303–1318. doi:10.1007/s10845-017-1325-3.
  • Wang, Y., and J. Tang. 2017. “Multi-Objective Optimization Model for Seru Production System Formation under Uncertain Condition.” 14th International Conference on Service Systems and Service Management (ICSSSM), China, June 16–18.
  • Wang, Y., and J. Tang. 2018. “Cost and Service-Level-Based Model for a Seru Production System Formation Problem with Uncertain Demand.” Journal of Systems Science and Systems Engineering 27 (4): 519–537. doi:10.1007/s11518-018-5379-3.
  • Wang, J., Y. Chen, S. Hao, X. Peng, and L. Hu. 2019. “Deep Learning for Sensor-Based Activity Recognition: A Survey.” Pattern Recognition Letters 119: 3–11.
  • Wei, L., Y. Ding, R. Su, J. Tang, and Q. Zou. 2018. “Prediction of Human Protein Subcellular Localization Using Deep Learning.” Journal of Parallel and Distributed Computing 117: 212–217. doi:10.1016/j.jpdc.2017.08.009.
  • Wu, L., F. T. S. Chan, B. Niu, and L. Li. 2018. “Cross-Trained Worker Assignment and Comparative Analysis on Throughput of Divisional and Rotating Seru.” Industrial Management & Data Systems 118 (5): 1114–1136. doi:10.1108/IMDS-07-2017-0303.
  • Xiao, C., N. Chen, C. Hu, K. Wang, Z. Xu, Y. Cai, L. Xu, Z. Chen, and J. Gong. 2019. “A Spatiotemporal Deep Learning Model for Sea Surface Temperature Field Prediction Using Time-Series Satellite Data.” Environmental Modelling and Software 120: 1–20. doi:10.1016/j.envsoft.2019.104502.
  • Xu, C., D. Chai, J. He, X. Zhang, and S. Duan. 2019. “InnoHAR: A Deep Neural Network for Complex Human Activity Recognition.” IEEE Access 7: 9893–9902. doi:10.1109/ACCESS.2018.2890675.
  • Yin, Y. I. K., and K. Stecke. 2008. “The Evolution of Seru Production Systems Throughout Canon.” Operations Management Education Review 2: 27–40.
  • Yin, Y., K. E. Stecke, M. Swink, and I. Kaku. 2017. “Lessons from Seru Production on Manufacturing Competitively in a High Cost Environment.” Journal of Operations Management 49–51 (1): 67–76. doi:10.1016/j.jom.2017.01.003.
  • Yin, Y., K. E. Stecke, and D. Li. 2018. “The Evolution of Production Systems from Industry 2.0 through Industry 4.0.” International Journal of Production Research 56 (1–2): 848–861. doi:10.1080/00207543.2017.1403664.
  • Ying, K., and Y. Tsai. 2017. “Minimising Total Cost for Training and Assigning Multiskilled Workers in Seru Production Systems.” International Journal of Production Research 55 (10): 2978–2989. doi:10.1080/00207543.2016.1277594.
  • Yu, Y., J. Tang, W. Sun, Y. Yin, and I. Kaku. 2013. “Reducing Worker(s) by Converting Assembly Line into a Pure Cell System.” International Journal of Production Economics 145 (2): 799–806. doi:10.1016/j.ijpe.2013.06.009.
  • Yu, Y., J. Tang, J. Gong, Y. Yin, and I. Kaku. 2014. “Mathematical Analysis and Solutions for Multi-Objective Line-Cell Conversion Problem.” European Journal of Operational Research 236 (2): 774–786. doi:10.1016/j.ejor.2014.01.029.
  • Yu, Y., S. Wang, J. Tang, I. Kaku, and W. Sun. 2016. “Complexity of Line‑seru Conversion for Different Scheduling Rules and Two Improved Exact Algorithms for the Multi‑objective Optimization.” SpringerPlus 5 (1): 1–26. doi:10.1186/s40064-016-2445-5.
  • Yu, Y., W. Sun, J. Tang, and J. Wang. 2017. “Line-Hybrid Seru System Conversion: Models, Complexities, Properties, Solutions and Insights.” Computers & Industrial Engineering 103: 282–299. doi:10.1016/j.cie.2016.11.035.
  • Yu, Y., and J. Tang. 2019. “Review of Seru Production.” Frontiers of Engineering Management 6 (2): 183–192. doi:10.1007/s42524-019-0028-1.
  • Zhang, X., C. Liu, W. Li, S. Evans, and Y. Yin. 2017. “Effects of Key Enabling Technologies for Seru Production on Sustainable Performance.” Omega 66: 290–307. doi:10.1016/j.omega.2016.01.013.
  • Zhang, H., X. Yan, and H. Li. 2018. “Ergonomic Posture Recognition Using 3D View-Invariant Features from Single Ordinary Camera.” Automation in Construction 94: 1–10. doi:10.1016/j.autcon.2018.05.033.
  • Zhang, Q., L. T. Yang, Z. Chen, and P. Li. 2018. “A Survey on Deep Learning for Big Data.” Information Fusion 42: 146–157. doi:10.1016/j.inffus.2017.10.006.

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