Multi-objective Optimization of Cutting Parameters for Machining Process of Ni-Rich NiTiHf High-Temperature Shape Memory Alloy Using Genetic Algorithm

2021 ◽  
Author(s):  
A. O. Kabil ◽  
Y. Kaynak ◽  
H. Saruhan ◽  
O. Benafan
Keyword(s):  
Genetic Algorithm ◽  
High Temperature ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Cutting Parameters ◽  
Machining Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

scholarly journals Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 217 ◽  
Author(s):  
Yu Su ◽  
Guoyong Zhao ◽  
Yugang Zhao ◽  
Jianbing Meng ◽  
Chunxiao Li
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
Relational Analysis ◽  
304 Austenitic Stainless Steel ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

Genetic Algorithm Based Optimization of Cutting Parameters in Drilling of Composite Materials

2010 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Genetic Algorithm ◽  
Composite Materials ◽  
Removal Rate ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Machining Process ◽  
Graphical Form ◽  
Optimization Approach ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Composite materials provide distinctive advantages in manufacture of advanced products because of attractive features such as high strength and light weight, but on the other hand machining of composite materials is difficult to carry out due to the anisotropic and non-homogeneous structure of composites and to the high abrasiveness of their reinforcing constituents. This typically results in damage being introduced into the workpiece and in very rapid wear development in the cutting tool. Conventional machining process such as drilling can be applied to composite materials, provided proper tool design and operating conditions are adopted. In this paper, A genetic algorithm (GA) based optimization procedure has been developed to optimize two factors, material removal rate; and delamination factor, using multi-objective function model with a weighted approach for the productivity, and superficial quality. An a posteriori approach was used to obtain a set of optimal solutions. An application sample was developed and its results were analyzed for several different production conditions. Finally, the obtained outcomes were arranged in graphical form and analyzed to make the proper decision for different process preferences. This paper also remarks the advantages of multi-objective optimization approach over the single-objective one.

Research on Optimization of Cutting Parameters Based on Genetic Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4640-4645 ◽  
Author(s):  
Shu Ren Zhang ◽  
Xue Guang Li ◽  
Jun Wang ◽  
Hui Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Surface Quality ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Experiment Data ◽  
Optimization Of Cutting Parameters ◽  
Working Efficiency

Three elements of Cutting dosages have a great effect on parts surface quality and working efficiency, the best organization of cutting three elements for parts surface quality must be found before machining, in order to surface roughness and machining cost of parts, multi-objective optimization model is established in this paper, model is solved by using genetic algorithm. Based on the BP neural network of three layers, forecasting model of surface roughness is established. According to existing experiment data and optimized cutting dosages, analysis and prediction of surface roughness is done. Machining experiment is done by using optimized data. The experiment result verifies feasibility of this optimistic method and prediction method of surface roughness.

scholarly journals Prediction of Surface Roughness of 304 Stainless Steel and Multi-Objective Optimization of Cutting Parameters Based on GA-GBRT

2019 ◽  
Vol 9 (18) ◽  
pp. 3684 ◽  
Author(s):  
Tao Zhou ◽  
Lin He ◽  
Jinxing Wu ◽  
Feilong Du ◽  
Zhongfei Zou
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Quality ◽  
Regression Tree ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Gradient Boosting ◽  
Multi Objective Optimization ◽  
Multi Objective

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

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