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 Based on Multi-Process and Multi-Evaluation Index for Function Ceramics in CMP

2013 ◽  
Vol 797 ◽  
pp. 266-272
Author(s):  
Zi Hua Hu ◽  
Jiao Peng ◽  
Man Ke Gao
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Orthogonal Experiment ◽  
Weight Ratio ◽  
Optimization Method ◽  
Evaluation Index ◽  
Optimization Of Process Parameters

To enhance polishing quality, polishing efficiency and improve the backward state based on single-process way for function ceramics in CMP, a new optimization method of process parameters based on multi-process and multi-evaluation theory is put forward. Firstly, based on experimental data obtained from orthogonal experiment, the optimal combinations of process parameters are got separately in term of surface roughness and material removal rate, which is optimized by Taguchi method in each process. Secondly, combining analysis of evaluation index weight ratio with analysis of variance, the final optimal combination of process parameters is received under the integrated evaluation of surface roughness and material removal rate. Finally, the contrast verification results show that the proposed optimization method is effective.

PREDICTIVE MODELING OF SURFACE ROUGHNESS, MATERIAL REMOVAL RATE AND KERF USING MULTIPLE REGRESSION ANALYIS IN PLASMA ARC CUTTING PROCESS OF HARDOX AND ABREX STEEL

2020 ◽  
Vol 27 (09) ◽  
pp. 1950206
Author(s):  
DEEPAK KUMAR NAIK ◽  
KALIPADA MAITY
Keyword(s):  
Surface Roughness ◽  
Multiple Regression ◽  
Material Removal Rate ◽  
Material Removal ◽  
Metal Cutting ◽  
Removal Rate ◽  
Cutting Speed ◽  
High Strength ◽  
Plasma Arc ◽  
Plasma Arc Cutting

Plasma arc cutting (PAC) process is widely used in metal cutting industries and modern fabrication units. Precise cutting of high strength material is still a challenging task to the industries. PAC process uses thermal energy to melt the material through highly energized plasma gas. Mostly, “hard-to-cut” type materials is used to cut through this process to meet the demands. The present work proposes an experimental investigation of PAC process of hardox 400 and abrex 400. Both the materials are high strength and high abrasion resistance in nature. Experiments were conducted based on Taguchi’s L[Formula: see text] orthogonal array design. The cutting parameters analyzed were arc current, cutting speed, stand-off distance and supply gas pressure whereas material removal rate, kerf and surface roughness were selected as responses. Also, a prediction model was developed to estimate the responses using multiple regression analysis. A comparison between experimental and predicted result shows the accuracy of the model. Analysis of variance (ANOVA) was used to verify the effect of each parameter on the surface quality to be assessed.

Fuzzy logic-based modeling and analysis of surface roughness, electrostatic charge, and material removal rate in ultrahigh precision diamond turning of rigid contact lens polymer

2019 ◽  
pp. 089270571985060
Author(s):  
Muhammad Mukhtar Liman ◽  
Khaled Abou-El-Hossein ◽  
Lukman Niyi Abdulkadir
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Material Removal Rate ◽  
Contact Lens ◽  
Material Removal ◽  
Removal Rate ◽  
Electrostatic Charge ◽  
High Accuracy ◽  
Diamond Turning ◽  
Optical Manufacturing

Due to increasing demand for high accuracy and high-quality surface finish in optical industry, contact lens manufacturing requires reliable models for predicting surface roughness (Ra) which plays a very important role in the optical manufacturing industry. In this study, a Nanoform 250 ultra-grind turning machine was used for machining, while cutting speed, feed rate, and the depth of cut (with values selected to cover a wide range based on the literature) were considered as the machining parameters for a diamond turned rigid polymethylmethacrylate (PMMA) contact lens polymer. Turning experiments were designed and conducted according to Box–Behnken design which is a response surface methodology technique. Fuzzy logic-based artificial intelligence method was employed to develop an electrostatic charge (ESC), Ra, and material removal rate (MRR) prediction models. The accuracy and predictive ability of the fuzzy logic model was then judged by considering an average percentage error between experimental values and fuzzy logic predictions. Further, a comparative evaluation of experiments and fuzzy logic approach showed that the average errors of ESC, Ra, and MRR using fuzzy logic system were in tandem with experimental results. Hence, the developed fuzzy logic rules can be effectively utilized to predict the ESC, Ra, and MRR of a rigid PMMA contact lens polymers in automated optical manufacturing environments for high accuracy and computational cost.

Multi Response Optimization of Process Parameters of Abrasive Water Jet Machining for Stainless Steel AISI 304 Using VIKOR Approach Coupled with Signal to Noise Ratio Methodology

2015 ◽  
Vol 14 (02) ◽  
pp. 107-121 ◽  
Author(s):  
Vedansh Chaturvedi ◽  
Diksha Singh
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Aisi 304 ◽  
Steel Aisi ◽  
Machining Characteristics ◽  
Stainless Steel Aisi ◽  
Experimental Runs

As the population of the world is continuously increasing, demand of the mechanical manufactured products is also increasing. Machining is the most important process in any mechanical manufacturing, and in machining two factors, i.e. material removal rate (MRR) and surface roughness (SR) are the most important responses. If the MRR is high, the product will get desired shape in minimum time so the production rate will be high, but we could not scarify with the surface finishing also because in close tolerance limit parts like in automobile industry, if the surface is rough exact fit cannot take place. The term optimization is intensively related to the field of quality engineering. Abrasive water jet machining is an important unconventional machining, in order to obtain better response, i.e. material removal rate and surface roughness. Various process parameters of AWJM need to be observed and selected to improve machining characteristics. Better machining characteristics can be achieved by optimizing various process parameters of AWJM. This study considers four process control parameters such as transverse speed, standoff distance, abrasive flow rate and water pressure. The response is taken to be material removal rate and surface roughness. The work piece for stainless steel AISI 304 material of size 15 cm × 10 cm × 2 cm is selected for experiments. Sixteen experimental runs (two trials for each experimental runs) were carried out for calculating MRR and SR and average value of these two trials have been taken for analysis. MRR is normalized according to higher-is-better and SR is normalized according to lower is better. The experiment data analysis is done and VIKOR index is found. Finally, the analysis of VIKOR index using S/N ratio is done and found the most significant factor for AWJM and predicted optimal parameters setting for higher material removal rate and lower surface roughness. Verification of the improvement in quality characteristics has been made through confirmation test with the predicted optimal parameters setting. It is found that the determined optimum combination of AWJM parameters gives the lowest VIKOR INDEX which shows the successful implementation of VIKOR Method coupled with S/N ratio in AWJM.

Abrasive Assisted Electro Chemical Machining of Aluminum-Boron Carbide-Graphite Hybrid Composite

2014 ◽  
Vol 591 ◽  
pp. 89-93 ◽  
Author(s):  
M. Sankar ◽  
R. Baskaran ◽  
K. Rajkumar ◽  
A. Gnanavelbabu
Keyword(s):  
Boron Carbide ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Medium Optimization ◽  
Removal Rate ◽  
Statistical Techniques ◽  
Graphite Composite ◽  
Abrasive Medium ◽  
Optimization Of Process Parameters

In this paper, attempts have been made to model and optimize process parameters in Abrasive assisted Electro-Chemical Machining (AECM) of Aluminium-Boron carbide-Graphite composite using cylindrical copper tool electrodes with SiC abrasive medium. Optimization of process parameters is based on the statistical techniques with four independent input parameters such as voltage, current, reinforcement and feed rate were used to assess the AECM process performance in terms of material removal rate. The obtained results are compared with without abrasive assisted electro chemical machining of Aluminium-Boron carbide-Graphite composite. Abrasive assisted ECM process exhibited higher material removal rate from composite material when compared with without abrasive assisted ECM.

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