Multi-objective optimization analysis of cutting parameters when drilling composite materials with special geometry drills

2019 ◽  
Vol 225 ◽  
pp. 111187 ◽  
Author(s):  
N. Feito ◽  
A. Muñoz-Sánchez ◽  
A. Díaz-Álvarez ◽  
M.H. Miguelez
Keyword(s):  
Composite Materials ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Optimization Analysis ◽  
Multi Objective ◽  
Special Geometry

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.

scholarly journals Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 64 ◽  
Author(s):  
Mahamudul Hasan Tanvir ◽  
Afzal Hussain ◽  
M. M. Towfiqur Rahman ◽  
Sakib Ishraq ◽  
Khandoker Zishan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Optimization Algorithm ◽  
Cutting Parameters ◽  
Whale Optimization Algorithm ◽  
Multi Objective Optimization ◽  
Turning Operation ◽  
Multi Objective ◽  
Whale Optimization ◽  
Optimum Cutting

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.

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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