Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

2019 ◽  
Vol 58 (9) ◽  
pp. 2751-2766 ◽  
Author(s):  
Weiguang Fang ◽  
Yu Guo ◽  
Wenhe Liao ◽  
Karthik Ramani ◽  
Shaohua Huang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Manufacturing System ◽  
Data Driven ◽  
Time Prediction ◽  
System A ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing ◽  
Remaining Time

Energy efficiency quantitative analysis method of discrete manufacturing system

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740071 ◽  
Author(s):  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Energy Efficiency ◽  
Index System ◽  
Manufacturing System ◽  
Evaluation Method ◽  
Prediction Method ◽  
Evaluation Index System ◽  
Index Structure ◽  
Evaluation Index ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing

The difficulty in the energy efficiency analysis of discrete manufacturing system is the lack of evaluation index system. In this paper, a novel evaluation index system with three layers and 10 indexes was presented to analyze the overall energy consumption level of the discrete manufacturing system. Then, with the consideration of the difficulties in directly obtaining machine energy efficiency, a prediction method based on recursive variable forgetting factor identification was put forward to calculate it. Furthermore, a comprehensive quantitative evaluation method of rough set and attribute hierarchical model was designed based on the index structure to evaluate the energy efficiency level. Finally, an experiment was used to illustrate the effectiveness of our evaluation index system and method.

Study On Simulation Of The Main Shaft Production Line

2012 ◽  
Vol 472-475 ◽  
pp. 2076-2079 ◽  
Author(s):  
Shu Feng Chai ◽  
Su Jun Luo ◽  
Li Jie Zhang
Keyword(s):  
Production System ◽  
Production Line ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Main Shaft ◽  
Building Model ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing ◽  
Improve Production Efficiency

Since modern production system is a highly complicated discrete manufacturing system, it is very difficult to design the production line by traditional means. However, through building model of production system in virtual environment, analyzing and evaluating production system performance based on system simulating technology, the production system’s parameter and configuration can be optimized ahead of production plan to optimize production process and improve production efficiency. In this paper, the main shaft production line simulation model is constructed based on the object oriented discrete system software eM-Plant. The production line throughput, utilization and bottleneck operations are analyzed. Based on this, it can support the configuration of production line optimized. The example verifies that the modeling and simulation technology could be successfully used in manufacturing industry.

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