scholarly journals A maintenance time prediction method considering ergonomics through virtual reality simulation

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Dong Zhou ◽  
Xin-xin Zhou ◽  
Zi-yue Guo ◽  
Chuan Lv
Author(s):  
Jin Li ◽  
Xinsheng Jiang ◽  
Zituo Wang ◽  
Chunhui Wang ◽  
Yunxiong Cai

Aim: To predict the mechanical product maintenance time is difficult in the situation of lack of physical prototype or similar products’ statistics in stage of design Method: According to the theory of time accumulative estimation method, a product maintenance time prediction method framework based on virtual prototype was constructed, which described the prediction process. The virtual maintenance environment which contains virtual prototype, virtual human and maintenance tools was developed. The virtual human’s position and posture information during the maintenance process was obtained by implementing VBScript language. Result: Basic maintenance motions that constitute the whole maintenance process were classified into 4 categories: body movement, upper limb movement, grasp/replace and operation. Based on MODAPTS (Modular arrangement of predetermined time standard) method and virtual maintenance simulation, corresponding time prediction methods for each categories were proposed. Discussion: Take a maintenance dissassembly and assembly task of engine as an example, through the comparison between the measured actual maintenance time and predicted time of several methods, feasibility and effectiveness of proposed method are verified


Author(s):  
Hamed Azarnoush ◽  
Gmaan Alzhrani ◽  
Alexander Winkler-Schwartz ◽  
Fahad Alotaibi ◽  
Nicholas Gelinas-Phaneuf ◽  
...  

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.


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