scholarly journals Optimization of Material Removal Rate and Surface Roughness of AISI 316L under Dry Turning Process using Genetic Algorithm

2021 ◽  
Author(s):  
Sigit Yoewono Martowibowo ◽  
Bivynka Kemala Damanik
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Aisi 316L ◽  
Turning Process ◽  
Dry Turning

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.

Study of the Cutting Parameters on Surface Roughness and Material Removal Rate in Hard Turning of UHMWPE

2020 ◽  
Author(s):  
César Oswaldo Aguilera-Ojeda ◽  
Alberto Saldaña-Robles ◽  
Agustín Vidal-Lesso ◽  
Israel Martínez-Ramírez ◽  
Eduardo Aguilera-Gómez
Keyword(s):  
Surface Roughness ◽  
Predictive Model ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Turning Process

Abstract The surface finish of industrial components has an important role in their performance and lifetime. Therefore, it is crucial to find the cutting parameters that provide the best surface finish. In this work, an experimental study of the effect of cutting parameters on ultra-high molecular weight polyethylene (UHMWPE) by a turning process was carried out. Today, the UHMWPE polymer continues to find applications mainly in the automotive industry and biomechanics because it is resistant to impact and corrosive materials to use. A face-centered Central Composite Design (CCD) and Response Surface Methodology (RSM) were applied to evaluate the influence of the cutting speed (Vc), feed rate (f) and depth of cut (ap) of the turning operation on the Average Surface Roughness (Ra) and Material Removal Rate (MRR). Results allowed obtaining an adjusted multivariable regression model that describes the behavior of the Ra that depends on the cutting parameters in the turning process. The predictive model of Ra showed that it fits well with a correlation coefficient (R2) around 0.9683 to the experimental data for Ra. The ANOVA results for Ra showed that the feed is the most significant factor with a contribution of 42.3 % for the term f 2, while the speed and depth of cut do not affect Ra with contributions of 0.19% and 0.18%, respectively. A reduction of feed from 0.30 to 0.18 mm·rev−1 produces a decrease in surface roughness from 6.68 to 3.81 μm. However, if the feed continued to reduce an increase in surface roughness, a feed of 0.05 mm·rev−1 induces a surface roughness of 14.93 μm. Feeds less than 0.18 mm·rev−1 cause a heat generation during turning that increases the temperature in the process zone, producing surface roughness damage of the UHMWPE polymer. Also, the results for MRR demonstrated that all of the cutting parameters are significant with contributions of 31.4%, 27.4% and 15.4% to feed, speed, and depth of cut, respectively. The desirability function allowed optimizing the cutting parameters (Vc = 250 m·min−1, ap = 1.5 mm y f = 0.27 mm·rev−1) to obtain a minimum surface roughness (Ra = 4.3 μm) with a maximum material removal rate (MMR = 97.1 cm3·min−1). Finally, the predictive model of Ra can be used in the industry to obtain predictions on the experimental range analyzed, reducing the surface roughness and the manufacturing time of UHMWPE cylindrical components.

scholarly journals Optimization of Multiple Response Using Taguchi-WPCA In ST 60 Tool Steel Turning Process With Minimum Quantity Cooling Lubrication (MQCL) Method

2018 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Dian Ridlo Pamuji ◽  
Muhammad Abdul Wahid ◽  
Abdul Rohman ◽  
Achmad As’ad Sonief ◽  
Moch Agus Choiron
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Orthogonal Array ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Turning Process ◽  
Feeding Speed

A research was conducted for the optimization of the turning process st 60 tool steel with multiple performance characteristics based on the orthogonal array with Taguchi-WPCA method. Minimum Quantity Cooling Lubrication (MQCL) metode was applied as a coolant. The experimental studies were conducted under varying the cutting speed, feeding, depth of cut and type of coolant. The optimized multiple performance characteristics were surface roughness, and material removal rate. An orthogonal array, signal-to-noise ratio, grey relational analysis, weighted pricipal component analysis and analysis of variance were employed to study the multiple performance characteristics. Experimental results show that cutting speed gives the highest contribution for minimize of surface roughness and maximize of material removal rate, followed by feeding speed, type of coolant and depth of cut. The minimum of surface roughness and maximize of material removal rat could be obtained by using the values of cutting speed, feeding speed,  depth of cut and type of coolant of 172.95 m/minute, 0.053 mm/rev, 0.25 mm, and vegetable oil as a coolant respectively.

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