Analytical Model for Temperature Prediction in Milling AISI D2 with Minimum Quantity Lubrication

Metals

Tsung-Pin Hung¹  Steven Y. Liang²  Yixuan Feng²  Linger Cai²  Yu-Ting Lu³  Yu-Fu Lin³  Fu-Chuan Hsu³ 

[1] Center for Environmental Toxin and Emerging-Contaminant Research, Super Micro Mass Research & Technology Center, Department of Mechanical Engineering, Cheng Shiu University, Kaohsiung83347, Taiwan;Georgia Institute of Technology, Woodruff School of Mechanical Engineering, 801 Ferst Drive, Atlanta, GA 30332, USA;Metal Industries Research and Development Centre (MIRDC), Kaohsiung 81160, Taiwan;

关键词: cutting temperature;    analytical modeling;    Johnson–Cook;    minimum quantity lubrication;   

DOI  :  10.3390/met12040697

来源: DOAJ

【 摘 要 】

Milling with minimum quantity lubrication (MQL) is now a commonly used machining technique in industry. The application of the MQL significantly reduces the temperature on the machined surface, while the cost of the lubricants is limited and the pollution caused by the lubricants is better controlled. However, the fast prediction of the milling temperature during the process has not been well developed. This paper proposes an analytical model for milling temperature prediction at the workpiece flank surface with MQL application. Based on the modified orthogonal cutting model and boundary layer lubrication effect, the proposed model takes in the process parameters and can generate the temperature profile at the workpiece surface within 1 min. The model is validated with experimental data in milling AISI D2 steel. With an average absolute error of 10.38%, the proposed model provides a reasonable temperature prediction compared to the experimental results. Based on the proposed model, this paper also investigates the effect of different cutting parameters on the cutting temperature. It is found that the application of the MQL decreases the temperature at the cutting zone, especially at the flank surface of the workpiece, which is due to the heat loss led by air-oil flow.

【 授权许可】

Unknown