A surface-temperature prediction model for porous asphalt pavement and its validation

2007 ◽  
Vol 1 (2) ◽  
pp. 129-134 ◽  
Author(s):  
J Shao ◽  
P J Lister ◽  
A McDonald
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Asphalt Pavement ◽  
Temperature Prediction ◽  
Porous Asphalt

Heat flux distribution and temperature prediction model for dry and wet cylindrical plunge grinding

2018 ◽  
Vol 233 (10) ◽  
pp. 2047-2060 ◽  
Author(s):  
Jingzhu Pang ◽  
Chongjun Wu ◽  
Yiming Shen ◽  
Siqi Liu ◽  
Qingxia Wang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Prediction Model ◽  
Surface Temperature ◽  
Contact Zone ◽  
Flux Distribution ◽  
Grinding Process ◽  
Grinding Temperature ◽  
Heat Flux Distribution ◽  
Temperature Prediction ◽  
Workpiece Surface

The grinding heat is generally partitioned into the workpiece, wheel, chips and fluid in grinding process. The total amount of heat flux entering into the workpiece greatly affects the final workpiece surface temperature, which may cause undesirable workpiece burn. Moreover, the variable grinding chip thickness and fluid injection speed along the grinding contact zone could substantially change the specific energy and the shape of the heat source correspondingly. In this article, a Weibull heat flux distribution model for both dry and wet grinding temperature prediction was proposed by analyzing two key parameters: energy partition Rw and shape parameter k. The value of Rw was obtained by considering the real contact length, the active grits number and the average grit radius r0 on the basis of traditional formulas. The relationship between shape parameters k and useful flow was established by a FLUENT simulation of the convective grinding fluid applied in grinding contact zone with wheel-workpiece minimum clearance. The grinding temperature and grinding force experiments were conducted on a grinding machine MGKS1332/H to validate the proposed heat flux model. The calculated workpiece surface temperature distribution was obtained by using the experimental heat flux obtained by the reverse algorithm, and the error between calculated temperature and experimental temperature was analyzed. With the monitored force signals and the proposed temperature prediction model, the grinding temperature for both dry and wet grinding can be predicted, which will be helpful to the optimization and control of temperature in grinding process.

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