scholarly journals Prediction and optimization of work-piece temperature during 2.5-D milling of Inconel 625 using regression and Genetic Algorithm

2020 ◽  
Vol 7 (1) ◽  
pp. 1731199 ◽  
Author(s):  
Satish Kumar ◽  
Pankaj Chandna ◽  
Gian Bhushan ◽  
Duc Pham
Keyword(s):  
Genetic Algorithm ◽  
Inconel 625 ◽  
Work Piece

Genetic Algorithm optimization of initial blank shape in deep drawing of a stepped work piece

2016 ◽  
Vol 231 (5) ◽  
pp. 1063-1074
Author(s):  
Yasser Rostamiyan ◽  
Mohammad Abbasi
Keyword(s):  
Genetic Algorithm ◽  
Rolling Direction ◽  
Work Piece ◽  
Low Carbon ◽  
Objective Functions ◽  
Algorithm Optimization ◽  
Initial Shape ◽  
Expansion Level ◽  
Blank Shape ◽  
St14 Steel

This study considers the effect of forging direction on the initial shape of sheet to create a stepped work piece. The purpose of this study is to consider rolling direction in 0°, decreasing the waste while producing workpieces and so decreasing total cost of process. To this end, the assumed workpiece was made of a low carbon and anisotropic st14 steel sheet. To find the most appropriate direction and the shortest modification steps for final shape, the expansion level of the sheet was first imaged in the rolling direction and then the piece was shaped by the geometry. This approach was based on the coupling between the simulation and Genetic Algorithm. A Genetic Algorithm based approach is developed to optimize dimensions through integrating a finite element code running to compute the objective functions for each generation. Those points with a few materials modified through Genetic Algorithm yielded better results.

Registration of 3-D Shape with Free-Form Surface Using Imageware and Genetic Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 317-322 ◽  
Author(s):  
Xiao Yi Wang ◽  
Jing Chen ◽  
Jiang Zhu ◽  
Yoshio Saito ◽  
Tomohisa Tanaka
Keyword(s):  
Genetic Algorithm ◽  
Point Cloud ◽  
Point Clouds ◽  
Free Form ◽  
Work Piece ◽  
Cad Model ◽  
Manufacturing Accuracy ◽  
Free Form Surface ◽  
Point Data ◽  
Fine Registration

Registration of 3-D shape is significant in quantizing the error between the part and its CAD model and evaluating the part's manufacturing accuracy. In the past, various improved methods of the iterative closest point (ICP) had been proposed in registration. However, without fine initial pose of point clouds, the ICP algorithm often could not converge to the best (or near best) solution. According to the characteristics of 3-D shape with free-form surface, a new method for registration of 3-D shape with free-form surface is given, by which there are not rigid requests in initial pose of point data and the 3-D shape model could be in arbitrary positions and orientations in space. To improve the efficiency and accuracy of solving, this method is divided into general registration and fine registration. General registration is to fit rapidly and roughly the measured point cloud to designing point cloud from CAD model by Imageware. Fine registration is to further accurately fit the two group points using genetic algorithm (GA). Case study is finally given for a work piece with free-form surface to show the effectiveness of the above method.

Optimization of Pre-Treatment Parameters before Diamond Coating Using Non-Dominated Sorting Genetic Algorithm (NSGA-II)

2012 ◽  
Vol 463-464 ◽  
pp. 399-405
Author(s):  
Abolfazl Golshan ◽  
Mostafa Rezazadeh Shirdar ◽  
Soheil Gohari ◽  
Mohammadfarid Alvansazyazdi
Keyword(s):  
Genetic Algorithm ◽  
Cobalt Content ◽  
Single Step ◽  
Etching Time ◽  
Work Piece ◽  
Nsga Ii ◽  
Quality Work ◽  
Short Period ◽  
Software Codes ◽  
Pre Treatment

In this study a single step of the chemical pre-treatment is implemented to tungsten carbide (WC 6 [%]) at the surface of the substrate in order to solve poor adhesion problem. During the pre-treatment process, numerous parameters such as etching time, acid temperature and concentration affect on the surface roughness and Cobalt content of WC-Co substrate are investigated. Optimal selection of these parameters is one of the significant issues to achieve high-quality work-piece in etching process. Thus, the statistical model based on nonlinear polynomial equations is developed for the different responses. Non-dominated Sorting Genetic Algorithm (NSGA-II) with the use of MATLAB Software codes is used to solve multi-objective optimization problem in order to provide a preferred solution for a process engineer in a short period of time.

scholarly journals Optimization of Cutting Parameters to Minimize Tooling Cost in High Speed Turning of SS304 using Coated Carbide Tool using Genetic Algorithm Method

2016 ◽  
Vol 1 (1) ◽  
pp. 11-15
Author(s):  
Muataz Hazza ◽  
Nur Amirah Najwa
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Work Piece ◽  
Carbide Tool ◽  
High Speed Turning ◽  
Coated Carbide Tool ◽  
Optimization Of Cutting Parameters ◽  
Coated Carbide

High speed turning (HST) is an approach that can be used to increase the material removal rate (MRR) by higher cutting speed. Increasing MRR will lead to shortening time to market. In contrast, increasing the cutting speed will lead to increasing the flank wear rate and then the tooling cost.  However, the main factor that will justify the best level of cutting speed is the tooling cost which merges all in one understandable measurable factor for manufacturer. The aim of this paper is to determine experimentally the optimum cutting levels that minimize the tooling cost in machining AISI 304 as a work piece machined by a coated carbide tool using one of the non-conventional methods: Genetic Algorithm (GA). The experiments were designed using Box Behnken Design (BBD) as part of Response Surface Methodology (RSM) with three input factors: cutting speed, feeding speed and depth of cut.

Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3050-3054
Author(s):  
Yong Zhi Pan ◽  
Jun Zhao ◽  
Xiu Li Fu ◽  
Xing Ai
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Linear Regression ◽  
High Speed ◽  
Metal Removal ◽  
Depth Of Cut ◽  
Work Piece ◽  
Helical Angle ◽  
Radial Depth ◽  
Multi Linear Regression

During the high-speed milling operations of 7050-T7451 aluminum alloy using solid carbide end mills, helical angle, axial and radial depth-of-cut have significant effects on the milling uniformity. A surface roughness predictive model of work-piece was developed by using a full-factorial experimental design and multi-linear regression technology. Genetic algorithm was utilized to optimize the helical angle and cutting parameters by means of a series of operations of selection, crossover and mutation based on genetics. The result shows that it is possible to select optimum axial depth-of-cut, radial depth-of-cut and helical angle for obtaining minimum cutting force and reasonably good metal removal rate.

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