Heat flux distribution and temperature prediction model for dry and wet cylindrical plunge grinding. (August 2019)

• Record Type: Journal Article
• Authors: Pang, Jingzhu; Wu, Chongjun; Shen, Yiming; Liu, Siqi; Wang, Qingxia; Li, Beizhi
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 10(2019)
• Page Start: 2047
• Page End: 2060
• Publication Date: 2019-08
• Subjects: Weibull heat flux distribution model -- grinding temperature -- energy partition -- grinding force -- flow rate; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405418815365
• Languages: English
• ISSNs: 0954-4054

Abstract

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 r 0 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.