scholarly journals Prediction of Surface Roughness and Optimization of Process Parameters for Slow Tool Servo Turning

2021 ◽  
Author(s):  
Hangyan Guo ◽  
Min Kang ◽  
Wei Zhou
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Optimization Of Process Parameters ◽  
Slow Tool Servo

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

scholarly journals Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting

2019 ◽  
Vol 9 (6) ◽  
pp. 1256 ◽  
Author(s):  
Amal Charles ◽  
Ahmed Elkaseer ◽  
Lore Thijs ◽  
Veit Hagenmeyer ◽  
Steffen Scholz
Keyword(s):  
Surface Roughness ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Process Parameters ◽  
Process Models ◽  
Average Error ◽  
Lead Times ◽  
Laser Melting ◽  
Optimization Of Process Parameters ◽  
The Difference

Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind conventional manufacturing in terms of surface quality. This proves a hindrance for many companies considering investment in AM. The aim of this work is to investigate the effect of varying process parameters on the resultant roughness of the down-facing surfaces in selective laser melting (SLM). A systematic experimental study was carried out and the effects of the interaction of the different parameters and their effect on the surface roughness (Sa) were analyzed. It was found that the interaction and interdependency between parameters were of greatest significance to the obtainable surface roughness, though their effects vary greatly depending on the applied levels. This behavior was mainly attributed to the difference in energy absorbed by the powder. Predictive process models for optimization of process parameters for minimizing the obtained Sa in 45° and 35° down-facing surface, individually, were achieved with average error percentages of 5% and 6.3%, respectively, however further investigation is still warranted.

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