Thermo-Mechanical Simulation of Hard Turning with Macroscopic Models

2017 ◽  
pp. 95-132 ◽  
Author(s):  
E. Uhlmann ◽  
R. Mahnken ◽  
I. M. Ivanov ◽  
C. Cheng
Keyword(s):  
Hard Turning ◽  
Macroscopic Models ◽  
Mechanical Simulation

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Stochastic homogenization and macroscopic modelling of composites and flow through porous media

2002 ◽  
pp. 337-378 ◽  
Author(s):  
Jozef Telega ◽  
Wlodzimierz Bielski
Keyword(s):  
Porous Medium ◽  
Random Distribution ◽  
Flow Through Porous Media ◽  
Macroscopic Models ◽  
Linear Elastic ◽  
Multi Scale ◽  
Convergence Method ◽  
Flow Through ◽  
The Mean ◽  
Random Porous Medium

The aim of this contribution is mainly twofold. First, the stochastic two-scale convergence in the mean developed by Bourgeat et al. [13] is used to derive the macroscopic models of: (i) diffusion in random porous medium, (ii) nonstationary flow of Stokesian fluid through random linear elastic porous medium. Second, the multi-scale convergence method developed by Allaire and Briane [7] for the case of several microperiodic scales is extended to random distribution of heterogeneities characterized by separated scales (stochastic reiterated homogenization). .

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