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Data-driven process planning for shipbuilding

Published online by Cambridge University Press:  31 January 2017

Jinsong Bao ,
Xiaohu Zheng ,
Jianguo Zhang ,
Xia Ji  and
Jie Zhang
Jinsong Bao
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China Shanghai Engineering Center of Process and Equipment for Aerospace Devices Manufacturing, Shanghai, China
Xiaohu Zheng*
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China Shanghai Engineering Center of Process and Equipment for Aerospace Devices Manufacturing, Shanghai, China
Jianguo Zhang
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
Xia Ji
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
Jie Zhang
Affiliation:
School of Mechanical Engineering, Donghua University, Shanghai, China
*
Reprint requests to: Xiaohu Zheng, School of Mechanical Engineering, Donghua University, Number 2999 North Renmin Road 201620, Songjiang District, Shanghai, China. E-mail: xhzheng@dhu.edu.cn
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Abstract

Erection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.

Keywords

Anomaly DetectionIndustrial Big DataOptimizationRescheduleShipbuilding Plan
Type
Technical Brief Paper
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 122 - 130
Copyright
Copyright © Cambridge University Press 2017 

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