Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. A second order mathematical model, in terms of machining parameters, was developed for surface roughness prediction using RSM. This model gives the factor effects of the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.

A genetic algorithmic approach for optimization of surface roughness prediction model

The machinability of inconel 718

Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel

An experimental investigation was conducted to determine the effects of cutting conditions and tool geometry on the surface roughness in the finish hard turning of the bearing steel (AISI 52100). Mixed ceramic inserts made up of aluminium oxide and titanium carbonitride (SNGA), having different nose radius and different effective rake angles, were used as the cutting tools. This study shows that the feed is the dominant factor determining the surface finish followed by nose radius and cutting velocity. Though, the effect of the effective rake angle on the surface finish is less, the interaction effects of nose radius and effective rake angle are considerably significant. Mathematical models for the surface roughness were developed by using the response surface methodology.

A surface roughness prediction model for hard turning process

Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network

Surface finish data were generated for aluminium alloy 390, ductile cast iron, medium carbon leaded steel 10L45, medium carbon alloy steel 4130, and inconel 718 for a wide range of machining conditions defined by cutting speed, feed and tool nose radius. These data were used to develop surface finish prediction models, as a function of cutting speed, feed, and tool nose radius, for each individual metal. A general purpose surface finish prediction model is also proposed for ductile cast iron, medium carbon leaded steel, and alloy steel. Statistical analysis of experimental data indicated that surface finish is strongly influenced by the type of metal, speed and feed of cut, and tool nose radius. While the effects of feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved with speed for ductile cast iron, medium carbon leaded steel, medium carbon alloy steel, and aluminium alloy, but it deteriorated with speed...

Manish Mehta

Papers

Surface finish prediction models for fine turning