TY - JOUR
T1 - 2D FEM estimate of tool wear in turning operation
AU - Xie, L. J.
AU - Schmidt, J.
AU - Schmidt, C.
AU - Biesinger, F.
PY - 2005/5
Y1 - 2005/5
N2 - Finite element method (FEM) is a powerful tool to predict cutting process variables, which are difficult to obtain with experimental methods. In this paper, modelling techniques on continuous chip formation by using the commercial FEM code ABAQUS are discussed. A combination of three chip formation analysis steps including initial chip formation, chip growth and steady-state chip formation, is used to simulate the continuous chip formation process. Steady chip shape, cutting force, and heat flux at tool/chip and tool/work interface are obtained. Further, after introducing a heat transfer analysis, temperature distribution in the cutting insert at steady state is obtained. In this way, cutting process variables e.g. contact pressure (normal stress) at tool/chip and tool/work interface, relative sliding velocity and cutting temperature distribution at steady state are predicted. Many researches show that tool wear rate is dependent on these cutting process variables and their relationship is described by some wear rate models. Through implementing a Python-based tool wear estimate program, which launches chip formation analysis, reads predicted cutting process variables, calculates tool wear based on wear rate model and then updates tool geometry, tool wear progress in turning operation is estimated. In addition, the predicted crater wear and flank wear are verified with experimental results.
AB - Finite element method (FEM) is a powerful tool to predict cutting process variables, which are difficult to obtain with experimental methods. In this paper, modelling techniques on continuous chip formation by using the commercial FEM code ABAQUS are discussed. A combination of three chip formation analysis steps including initial chip formation, chip growth and steady-state chip formation, is used to simulate the continuous chip formation process. Steady chip shape, cutting force, and heat flux at tool/chip and tool/work interface are obtained. Further, after introducing a heat transfer analysis, temperature distribution in the cutting insert at steady state is obtained. In this way, cutting process variables e.g. contact pressure (normal stress) at tool/chip and tool/work interface, relative sliding velocity and cutting temperature distribution at steady state are predicted. Many researches show that tool wear rate is dependent on these cutting process variables and their relationship is described by some wear rate models. Through implementing a Python-based tool wear estimate program, which launches chip formation analysis, reads predicted cutting process variables, calculates tool wear based on wear rate model and then updates tool geometry, tool wear progress in turning operation is estimated. In addition, the predicted crater wear and flank wear are verified with experimental results.
KW - Chip formation
KW - FEM
KW - Heat transfer
KW - Orthogonal cutting
KW - Tool wear
KW - Turning operation
UR - http://www.scopus.com/inward/record.url?scp=14644432393&partnerID=8YFLogxK
U2 - 10.1016/j.wear.2004.11.004
DO - 10.1016/j.wear.2004.11.004
M3 - Article
AN - SCOPUS:14644432393
SN - 0043-1648
VL - 258
SP - 1479
EP - 1490
JO - Wear
JF - Wear
IS - 10
ER -