The Methodology of Sequences of Facilities using Production history information

  • Authors

    • Sung Hyun Ryu
    • Sang Hyun Choi
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.18108
  • Smart factory, Naive Bayes, Conditional probability, Production traceability, Algorithm development
  • Background/Objectives: In the 4th Industrial Revolution, innovation has been taking place in various fields in Korea. In particular, the concept of ‘Smart factory’ has come into being as a result of innovation in the manufacturing that is the foundation of the nation’s industry. However, small and meddle-sized companies don’t know how to set up an automated system and are reluctant to introduce it due to limitations of cost and so on.

    Methods/Statistical analysis: Therefore, this paper proposes a sequences of facilities in the process that makes it easy for small and middle-sized enterprises (SMEs) to introduce 'Smart factory'. In this study, we choose the semiconductor process and use the processed production history data to derive the algorithm applying Conditional probability and Naive Bayesian. We utilized simulation data that have similar data type. We assumed the process of semiconductor and make the sample function using it.

    Findings: We assume that the sequences of facilities in the processes may impact on the product qualities. So, we found out that the particular machines produced low-quality products through basic statistical graph. Moreover, it is possible to design the sequences of facilities that avoids producing low-quality products. As we apply the conditional probability and Naive Bayesian to the scheduling, performance is simple and fast to calculate by checking which equipment has passed and what quality of final product is. First, when one equipment is passed through the process, the equipment with the most defects is filtered, and the probability of a lot of defective products coming from the next equipment is sought. We can suggest the various scheduling methodologies proposed in the existing research. And compared to the existing research, less variables is needed to calculate further scheduling.

    Improvements/Applications: For now, we suggest the algorithm. In the further study, we will apply the equation to the future research to construct the system. Also, we will systemize and apply the algorithm into it. It can show the entire process and we will distinguish which machine can produce high quality product.

     

  • References

    1. [1] Shin Hyun-Joon, Ru Jae-Pil. (2010). An Adaptive Scheduling Algorithm for Manufacturing Process with Non-stationary Rework Probabilities, Korea Academy Industrial Cooperation Society, 11(11), 4174-4181

      [2] So Byeong-Eob, Shin Sung-Sik. (2017). The Built of Smart Factory Using Sensors and Virtual Process Design, Journal of the KIECS, 12(6), 1071-1080

      [3] T. Park, K. Park. (2012). Effect of IT Manufacturing Firms’ Technological Innovation Factors - From Government Support Level, J. of Digital Convergence, 10(6), 17-22.

      [4] Yield Management System. (2015). Retrieved from http://www.dbguide.net/knowledge.db?cmd=view&boardUid=186140&boardConfigUid=22&boardStep=&categoryUid=197.

      [5] Application of Big Data in the Manufacturing Industry. (2016). Jo Seong-Jun, Shin Hun-Sik, Kim Seong-Hyeon.

      [6] Jo Yong-Cheol, Jo Hyeon-Chan, Kim Jong-Won, Jang Ryang, Jeon Hong-Tae. (2008). A Study on Optimal Scheduling of Multi-Spinner's Manufacturing Process Using Artificial Neural Network, Journal of Korean institute of intelligent systems, 18(1), 157-160

      [7] Kim Wook, Park Young-Moon. (1991). An Artificial Neural Network for the Optimal Path Planning, The proceedings of KIEE, 333-336.

      [8] Ryu Sun-Joong. (2015). Development of Visual Inspection Process Adapting Naive Bayes Classifiers, JOURNAL OF THE KOREAN INSTITUTE OF GAS, 19(2), 45-53

      [9] Semiconductor 8 Process 8th. First gate to acceptance EDS (Electrical Die Sorting) process. (2012). Retrieved from http://www.samsungsemiconstory.com/201

      [10] Zhang, Harry. (2004). The Optimality of Naive Bayes, FLAIRS2004 conference.

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  • How to Cite

    Hyun Ryu, S., & Hyun Choi, S. (2018). The Methodology of Sequences of Facilities using Production history information. International Journal of Engineering & Technology, 7(2.33), 1229-1232. https://doi.org/10.14419/ijet.v7i2.33.18108