A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy

Productivity and profitability in machining operations are known to be affected by three different interconnected aspects: state of health of the tool, state of health of the workpiece, and state of health of the machine. The interconnection of these factors is vital since there is high dependence between them. Due to the high strength, material strengthening mechanisms under loading, and low thermal conductivity of nickel-based alloys, cutting tool health has significant influence on the quality of the machined part. The objective of this work is to study the underlying effects of tool flank wear on surface integrity metrics of Inconel 718 (IN718) nickel-based alloys in a turning operation. Three parameters are taken together to represent the workpiece state of health: surface roughness, dimensional integrity and residual stress; and the tool wear effect on each is investigated. It is shown that unlike the common belief of the detrimental effects of wear on surface roughness, tool flank wear does not necessarily have a significant effect on the roughness profile evolution of the workpiece. To study the effect of wear on dimensional integrity, the geometrical relationship of the tool-tip and flank wear width is derived, and the estimated results of tool wear from the previous work of the authors in the use of a probabilistic Extended Kalman Filter are utilized for classification of dimensional deviation. The results are also compared with naïve Bayes and Support Vector Machine (SVM) classification strategies, and it is shown that probabilistic-based methods outperform the deterministic classifier. For studying the wear effect on machining-induced residual stresses, a 3D finite element model is developed. The results of the finite element model for the sharp tool are first validated with the experimental results, with good agreement. Next, the sharp tool geometry is updated to represent a wear land and found to have deviation from the validation tests in the compression zone; the sources of error in the finite element numerical model are discussed. The combination of this work on monitoring state of workpiece health alongside the proposed wear estimation strategy for monitoring state of cutting tool health provides a complete platform for in-process monitoring of machining health in cutting hard-to-machine alloys.