TY - JOUR
T1 - A proactive material handling method for CPS enabled shop-floor
AU - Wang, W.
AU - Zhang, Yingfeng
AU - Zhong, R. Y.
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/2
Y1 - 2020/2
N2 - Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.
AB - Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.
KW - Cyber physical system (CPS)
KW - Large-size product
KW - Material handling
KW - Prediction model
KW - Remaining processing time
KW - Shop-floor
UR - http://www.scopus.com/inward/record.url?scp=85070405066&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2019.101849
DO - 10.1016/j.rcim.2019.101849
M3 - 文章
AN - SCOPUS:85070405066
SN - 0736-5845
VL - 61
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 101849
ER -
