On Predicting Softening Effects in Hard Turned Surfaces—Part I: Construction of Material Softening Model

2004 ◽  
Vol 127 (3) ◽  
pp. 476-483 ◽  
Author(s):  
Jing Shi ◽  
C. Richard Liu
Keyword(s):  
Thermal History ◽  
Hard Turning ◽  
Hardness Measurement ◽  
Average Error ◽  
Suitable Model ◽  
Material Microstructure ◽  
Average Prediction Error ◽  
Aisi 52100 ◽  
Finishing Process ◽  
Aisi 52100 Steel

The need for predicting material microstructure and hardness in hard turned surfaces becomes very urgent in that hard turning is adopted by industries as a finishing process, and the produced surface integrity, including microstructure and hardness, is well known to be a determining factor for part service performance. This study focuses on the prediction of material softening and is composed of two parts, namely, the construction of material softening model based on thermal history and the prediction of thermal history by finite element modeling of hard turning. In this part of the research, three material softening models based on thermal activation concept are proposed and compared. The most suitable model is selected for the work material, hardened AISI 52100 steel. The model prediction demonstrates excellent agreement with the hardness measurement on the specimens with isothermal or anisothermal treatments. For the isothermal treatments, the average prediction error, compared with the measured hardness, is 10.78kg∕mm2. As for the anisothermal treatments, the average error is 13.79kg∕mm2. The softening model provides a fundamental for the final prediction of material softening in hard turned surfaces.

scholarly journals Machining force comparison for surface defect hard turning and conventional hard turning of AISI 52100 steel

2021 ◽  
Vol 13 (3) ◽  
pp. 205-214
Author(s):  
P. U MAMAHESWARRAO ◽  
D. RANGARAJU ◽  
K. N. S. SUMAN ◽  
B. RAVISANKAR
Keyword(s):  
Hard Turning ◽  
Surface Defect ◽  
Cutting Speed ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel ◽  
The Impact

In this article, a recently developed method called surface defect machining (SDM) for hard turning has been adopted and termed surface defect hard turning (SDHT). The main purpose of the present study was to explore the impact of cutting parameters like cutting speed, feed, depth of cut, and tool geometry parameters such as nose radius and negative rake angle of the machining force during surface defect hard turning (SDHT) of AISI 52100 steel in dry condition with Polycrystalline cubic boron nitride (PCBN) tool; and results were compared with conventional hard turning (CHT). Experimentation is devised and executed as per Central Composite Design (CCD) of Response Surface Methodology (RSM). Results reported that an average machining force was decreased by 22% for surface defect hard turning (SDHT) compared to conventional hard turning (CHT).

Effect of Cutting-Edge Geometry and Workpiece Hardness on Surface Residual Stresses in Finish Hard Turning of AISI 52100 Steel

1999 ◽  
Author(s):  
Jeffrey D. Thiele ◽  
Shreyes N. Melkote ◽  
Roberta A. Peascoe ◽  
Thomas R. Watkins
Keyword(s):  
Residual Stresses ◽  
Hard Turning ◽  
Cutting Edge ◽  
Phase Changes ◽  
Edge Geometry ◽  
Surface Residual Stresses ◽  
Aisi 52100 ◽  
High Feed ◽  
Aisi 52100 Steel ◽  
Finish Hard Turning

Abstract An experimental investigation was conducted to determine the effects of tool cutting-edge geometry and workpiece hardness on surface residual stresses for finish hard turning of through-hardened AISI 52100 steel. Polycrystalline cubic boron nitride (PCBN) inserts with representative types of edge geometry including “up-sharp” edges, edge hones, and chamfers, were used as the cutting tools in this study. This study shows that tool edge geometry is highly influential with respect to surface residual stresses, which were measured using x-ray diffraction. In general, compressive surface residual stresses in the axial and circumferential directions were generated by large edge hone tools, for longitudinal turning operations. Residual stresses in the axial and circumferential directions generated by small edge hone tools are typically more tensile than stresses produced by large edge hone tools. Microstructural analysis shows that thermal effects are significant at high feed rates, based on the presence of phase changes on the workpiece surface. At high feed rates, compressive stresses correlate with continuous white layers and tensile stresses correlate with over-tempered regions on the surface of the workpiece. Mechanical effects play a larger role at low feed rates, where phase changes are not observed to a significant degree. For these cases, large edge hone tools generally produce more compressive values of residual stress than small edge hone tools.

Effect of Cutting-Edge Geometry and Workpiece Hardness on Surface Residual Stresses in Finish Hard Turning of AISI 52100 Steel

1999 ◽  
Vol 122 (4) ◽  
pp. 642-649 ◽  
Author(s):  
Jeffrey D. Thiele ◽  
Shreyes N. Melkote ◽  
Roberta A. Peascoe ◽  
Thomas R. Watkins
Keyword(s):  
Residual Stresses ◽  
Hard Turning ◽  
Cutting Edge ◽  
Edge Geometry ◽  
Surface Residual Stresses ◽  
Aisi 52100 ◽  
Aisi 52100 Steel ◽  
Finish Hard Turning ◽  
Hardness Values ◽  
Compressive Residual Stresses

An experimental investigation was conducted to determine the effects of tool cutting-edge geometry (edge preparation) and workpiece hardness on surface residual stresses for finish hard turning of through-hardened AISI 52100 steel. Polycrystalline cubic boron nitride (PCBN) inserts with representative types of edge geometry including “up-sharp” edges, edge hones, and chamfers were used as the cutting tools in this study. This study shows that tool edge geometry is highly influential with respect to surface residual stresses, which were measured using x-ray diffraction. In general, compressive surface residual stresses in the axial and circumferential directions were generated by large edge hone tools in longitudinal turning operations. Residual stresses in the axial and circumferential directions generated by large edge hone tools are typically more compressive than stresses produced by small edge hone tools. Microstructural analysis shows that thermally-induced phase transformation effects are present at all feeds and workpiece hardness values with the large edge hone tools, and only at high feeds and hardness values with the small edge hone tools. In general, continuous white layers on the workpiece surface correlate with compressive residual stresses, while over-tempered regions correlate with tensile or compressive residual stresses depending on the workpiece hardness. [S1087-1357(00)00304-X]

Tool wear effects on white and dark layer formation in hard turning of AISI 52100 steel

Wear ◽  
2012 ◽  
Vol 286-287 ◽  
pp. 98-107 ◽  
Author(s):  
A. Attanasio ◽  
D. Umbrello ◽  
C. Cappellini ◽  
G. Rotella ◽  
R. M'Saoubi
Keyword(s):  
Tool Wear ◽  
Hard Turning ◽  
Layer Formation ◽  
Dark Layer ◽  
Aisi 52100 ◽  
Aisi 52100 Steel

A Regression Model for Force and Surface Roughness Estimation during Hard Turning

2011 ◽  
Vol 299-300 ◽  
pp. 1167-1170 ◽  
Author(s):  
Gaurav Bartarya ◽  
S.K. Choudhury
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Goodness Of Fit ◽  
Cutting Parameters ◽  
Anova Analysis ◽  
Aisi 52100 ◽  
Aisi 52100 Steel ◽  
Factorial Design Of Experiments ◽  
Full Factorial ◽  
Surface Roughness Estimation

Forces in Hard turning can be used to evaluate the performance of the process. Cutting parameters have their own influence on the cutting forces on the tool. The present work is an attempt to develop a force prediction model based on full factorial design of experiments for machining EN31 steel (equivalent to AISI 52100 steel) using uncoated CBN tool. The force and surface roughness regression models were developed using the data from various set of experiments with in the range of parameters selected. The predictions from the models were compared with the measured force and surface roughness values. The ANOVA analysis was undertaken to test the goodness of fit of data.

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