A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA

Measurement ◽  
2015 ◽  
Vol 70 ◽  
pp. 100-109 ◽  
Author(s):  
Arif Gok
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Fuzzy Topsis ◽  
New Approach ◽  
Multi Objective

scholarly journals Multi-objective optimization of SKD11 Steel Milling Process by Reference Ideal Method

2021 ◽  
Vol 15 ◽  
pp. 1-16
Author(s):  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Milling Process ◽  
Multi Objective Optimization ◽  
Small Surface ◽  
Multi Objective ◽  
Input Parameters ◽  
Machining Productivity ◽  
Ideal Method

For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is SKD11 steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study.

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

scholarly journals Study on multi-objective optimization of X12M steel milling process by reference ideal method

2021 ◽  
pp. 89-104
Author(s):  
Khanh Nguyen Lam
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Milling Process ◽  
Face Milling ◽  
Multi Objective Optimization ◽  
Milling Cutter ◽  
Multi Objective ◽  
Input Parameters ◽  
Machining Productivity ◽  
Ideal Method

For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. Milling is a popular machining method in the machine building industry. This is considered to be one of the most productive machining methods, capable of machining many different types of surfaces. With the development of the cutting tool and machine tool manufacturing industries, this method is increasingly guaranteed with high precision, sometimes used as the final finishing method. Milling using a face milling cutter is more productive than using a cylindrical cutter because there are multiple cutter s involved at the same time. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is X12M steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. The Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study

scholarly journals Optimizing Multi-Response Parameters in Turning of AISI1040 Steel Using Desirability Approach

2019 ◽  
Vol 4 (4) ◽  
pp. 905-921
Author(s):  
Srinu Gugulothu ◽  
Vamsi Krishna Pasam
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Flank Wear ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Tool Flank Wear ◽  
Multi Objective ◽  
Single Objective

In this study, an attempt is made to examine the machining response parameters in turning of AISI 1040 steel under different lubrication environment. Subsequently, design of experiment technique Response surface methodology (RSM) is used for analyzing machining performance by varying cutting conditions with the use of 2wt% of CNT/MoS2(1:2) HNCF. Regression models are developed for multiple machining responses. Optimization is performed for these models by using desirability function, which converts multi-objective into single objective. Then the optimal setting parameters for single objective is found. Significant reduction in main cutting force (Fz), cutting temperature (T), surface roughness(Ra) and tool flank wear (Vb) are found with the use of 2wt% of CNT/MoS2(1:2) HNCF compared to other lubrication environment. Significant factors that affect the main cutting force (Fz), the temperature in the cutting zone are cutting speed, feed rate and depth of cut. Parameter depth of cut has an insignificant effect on tool flank wear and surface roughness (Ra). The optimal cutting conditions for four multi-objective optimization of main cutting force (Fz), cutting temperature, surface roughness (Ra) and tool flank wear are found to be cutting speed 70.25 m/min, feed 0.13 mm/rev and doc 0.5mm at desirability value of 0.907.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

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