TY - GEN
T1 - A fast large-size production data transformation scheme for supporting smart manufacturing in semiconductor industry
AU - Suryajaya, Benny
AU - Chen, Chao Chun
AU - Hung, Min Hsiung
AU - Liu, Yu Yang
AU - Liu, Jia Xuan
AU - Lin, Yu Chuan
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported by Ministry of Science and Technology of Taiwan under Grants MOST 105-2218-E-006-014, 105-2221-E-006-141, 105-2221-E-034-002, and 106-2218-E-006-005.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In this paper, we propose a novel data transformation scheme over a big data platform, aiming at injecting production data from the local database in the factory side and transforming them into the workpiece-centric form that many manufacturing analytics systems need. The key idea is to blend big data processing techniques, including table composition with external distributed files, columnar storage, partition, massively parallel processing into the data transformation scheme for minimizing the data processing time. Our proposed scheme brings two main impacts to the smart manufacturing. First, our scheme plays the key component to develop data-driven manufacturing decision systems, since large-volume production data sources can be efficiently transformed into the workpiece-centric form that other smart manufacturing services require. Second, our proposed scheme provides a development exemplar to assist a manufacturing factory toward the Industry-4.0 realm, since big data techniques are ingeniously blended in building data-intensive manufacturing services. We finally implement the prototype of the proposed scheme on the Hadoop platform and apply the prototype to a semiconductor factory for conducting integrated tests. Testing results of a case study physically applying the proposed scheme to a semiconductor factory demonstrate the success of our work.
AB - In this paper, we propose a novel data transformation scheme over a big data platform, aiming at injecting production data from the local database in the factory side and transforming them into the workpiece-centric form that many manufacturing analytics systems need. The key idea is to blend big data processing techniques, including table composition with external distributed files, columnar storage, partition, massively parallel processing into the data transformation scheme for minimizing the data processing time. Our proposed scheme brings two main impacts to the smart manufacturing. First, our scheme plays the key component to develop data-driven manufacturing decision systems, since large-volume production data sources can be efficiently transformed into the workpiece-centric form that other smart manufacturing services require. Second, our proposed scheme provides a development exemplar to assist a manufacturing factory toward the Industry-4.0 realm, since big data techniques are ingeniously blended in building data-intensive manufacturing services. We finally implement the prototype of the proposed scheme on the Hadoop platform and apply the prototype to a semiconductor factory for conducting integrated tests. Testing results of a case study physically applying the proposed scheme to a semiconductor factory demonstrate the success of our work.
UR - http://www.scopus.com/inward/record.url?scp=85044961175&partnerID=8YFLogxK
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U2 - 10.1109/COASE.2017.8256114
DO - 10.1109/COASE.2017.8256114
M3 - Conference contribution
AN - SCOPUS:85044961175
T3 - IEEE International Conference on Automation Science and Engineering
SP - 275
EP - 281
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Y2 - 20 August 2017 through 23 August 2017
ER -