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.

scholarly journals Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4693
Author(s):  
Feilong Du ◽  
Lin He ◽  
Haisong Huang ◽  
Tao Zhou ◽  
Jinxing Wu
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Multi Objective Optimization ◽  
Multi Objective

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nadim Rana ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi'i Muhammad Abdulhamid
Keyword(s):  
Conceptual Framework ◽  
Virtual Machine ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
Virtual Machine Scheduling

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.

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