Optimization of Cutting Parameters in Machining

Selection Of

The paper analyzes the reasonable selection of cutting parameters and states the relations among maximum profit-oriented cutting speed, minimum cost-oriented cutting speed and maximum productivity-oriented cutting speed. It establishes a mathematical model for the optimization of cutting parameters in machining.

Maximum Profit as the Criterion in the Determination of the Optimum Cutting Conditions

Journal of Engineering for Industry ◽

10.1115/1.3672678 ◽

1966 ◽

Vol 88 (4) ◽

pp. 435-442 ◽

Cited By ~ 24

Author(s):

S. M. Wu ◽

D. S. Ermer

Keyword(s):

Production Rate ◽

Cutting Speed ◽

Minimum Cost ◽

Practical Applications ◽

Maximum Profit ◽

Optimum Speed ◽

Optimum Cutting ◽

Maximum Production Rate ◽

Selection Of

Maximum profit is an appropriate criterion for the selection of the optimum machining conditions rather than the conventional criteria of minimum cost or maximum production rate. A simple example is presented to illustrate the determination of the maximum-profit cutting speed by application of a fundamental economic principle that maximum profit occurs at the production rate where the marginal revenue equals the marginal cost. The effects of the demand function, feed, and cost and time parameters on the determination of the maximum-profit cutting speed are analyzed. Emphasis is given to the investigation of a range of optimum cutting speeds, instead of the theoretical optimum speed, for practical applications.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

Engineering and Technology Journal ◽

10.30684/etj.v38i10a.1124 ◽

2020 ◽

Vol 38 (10A) ◽

pp. 1489-1503

Author(s):

Marwa Q. Ibraheem

Keyword(s):

Genetic Algorithm ◽

Tool Wear ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Cutting Speed ◽

Cutting Parameters ◽

Depth Of Cut ◽

Optimal Value ◽

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.36.28 ◽

2020 ◽

Vol 36 ◽

pp. 28-46

Author(s):

Youssef Touggui ◽

Salim Belhadi ◽

Salah Eddine Mechraoui ◽

Mohamed Athmane Yallese ◽

Mustapha Temmar

Keyword(s):

Surface Roughness ◽

Cutting Force ◽

Feed Rate ◽

Cutting Speed ◽

Cutting Parameters ◽

Depth Of Cut ◽

Aisi 316L ◽

Cutting Power ◽

Dry Turning ◽

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

Model for Optimization of the Tool Life and the Cutting Speed for Maximum Productivity at Drilling of the Steel 2NiCr185

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.760.433 ◽

2015 ◽

Vol 760 ◽

pp. 433-438 ◽

Cited By ~ 3

Author(s):

Ovidiu Blăjină ◽

Aurelian Vlase ◽

Marius Iacob

Keyword(s):

Mathematical Model ◽

Stainless Steel ◽

Tool Life ◽

Stainless Steels ◽

Cutting Tool ◽

Cutting Speed ◽

Research Directions ◽

Maximum Productivity ◽

Optimum Cutting ◽

New Research

The research in the last decade regarding their cutting machinability have highlighted the insufficiency of the data for establishing of the optimum cutting processing conditions and the optimum cutting regime. The purpose of this paper is the optimization of the tool life and the cutting speed at the drilling of the stainless steels in terms of the maximum productivity. A nonlinear programming mathematical model to maximize the productivity at the drilling of a stainless steel is developed in this paper. The optimum cutting tool life and the associated cutting tool speed are obtained by solving the proposed mathematical model. The use of this productivity model allows greater accuracy in the prediction of the productivity for the drilling of a certain stainless steel and getting the optimum tool life and the optimum cutting speed for the maximum productivity. The obtained results can be used in production activity, in order to increase the productivity of the stainless steels machining. Finally the paper suggests new research directions for the specialists interested in this field.

The Study of Bone Cutting Force with FEM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.974.389 ◽

2014 ◽

Vol 974 ◽

pp. 389-393 ◽

Cited By ~ 1

Author(s):

Sen Liu ◽

Dong Mei Wu ◽

Jun Zhao

Keyword(s):

Finite Element ◽

Cutting Force ◽

Cutting Speed ◽

Cutting Parameters ◽

Element Model ◽

Cutting Surface ◽

On Line ◽

Force Velocity ◽

In orthopedic surgery, it is easy to do harm to surrounding tissues, so the study of bone cutting is necessary. In this article, a finite element model (FEM) of orthogonal bone cutting is developed. Cutting force intra-operatively can provide the surgeon with additional on-line information to support him to control quality of cutting surface. The obtained cutting force decreased little with cutting speed increasing, but ascended evidently with cutting depth increasing. The results of finite element simulations are aimed at providing optimization of cutting parameters and the basic information for hybrid force-velocity control of a robot-assisted bone milling system.

Study on the Method for the Optimization of Cutting Parameters

Materials Science Forum ◽

10.4028/www.scientific.net/msf.532-533.325 ◽

2006 ◽

Vol 532-533 ◽

pp. 325-328

Author(s):

Jing Ying Zhang ◽

Si Qin Pang ◽

Qi Xun Yu

Keyword(s):

Production Rate ◽

Production Cost ◽

Cutting Parameters ◽

Computational Method ◽

Profit Rate ◽

Maximum Profit ◽

Rate Minimum ◽

Maximum Production Rate ◽

Very High

This article discusses the problem about the method for the optimization of cutting parameters. A newly developed computational method which is different from the former was used for the optimization of cutting parameters. This method has its advantages of the controllability of the precision and higher speed when the precision requirement of the system is not very high. It can optimize cutting parameters toward the objectives of maximum production rate, minimum production cost and maximum profit rate.

Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach

Periodica Polytechnica Mechanical Engineering ◽

10.3311/ppme.12241 ◽

2020 ◽

Vol 65 (1) ◽

pp. 10-26

Author(s):

Septi Boucherit ◽

Sofiane Berkani ◽

Mohamed Athmane Yallese ◽

Riad Khettabi ◽

Tarek Mabrouki

Keyword(s):

Stainless Steel ◽

Austenitic Stainless Steel ◽

Cutting Force ◽

Cutting Speed ◽

Cutting Parameters ◽

Depth Of Cut ◽

Machining Parameters ◽

Desirability Approach ◽

Coated Carbide

In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).

Tool Wear, Surface Topography, and Multi-Objective Optimization of Cutting Parameters during Machining AISI 304 Austenitic Stainless Steel Flange

Metals ◽

10.3390/met9090972 ◽

2019 ◽

Vol 9 (9) ◽

pp. 972 ◽

Cited By ~ 2

Author(s):

Xiaojun Li ◽

Zhanqiang Liu ◽

Xiaoliang Liang

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Tool Wear ◽

Surface Defects ◽

Cutting Speed ◽

Cutting Parameters ◽

Aisi 304 ◽

304 Austenitic Stainless Steel ◽

The application of AISI 304 austenitic stainless steel in various industrial fields has been greatly increased, but poor machinability classifies AISI 304 as a difficult-to-cut material. This study investigated the tool wear, surface topography, and optimization of cutting parameters during the machining of an AISI 304 flange component. The machining features of the AISI 304 flange included both cylindrical and end-face surfaces. Experimental results indicated that an increased cutting speed or feed aggravated tool wear and affected the machined surface roughness and surface defects simultaneously. The generation and distribution of surface defects was random. Tearing surface was the major defect in cylinder turning, while side flow was more severe in face turning. The response surface method (RSM) was applied to explore the influence of cutting parameters (e.g., cutting speed, feed, and depth of cut) on surface roughness, material removal rate (MRR), and specific cutting energy (SCE). The quadratic model of each response variable was proposed by analyzing the experimental data. The optimization of the cutting parameters was performed with a surface roughness less than the required value, the maximum MRR, and the minimum SCE as the objective. It was found that the desirable cutting parameters were v = 120 m/min, f = 0.18 mm/rev, and ap = 0.42 mm for the AISI 304 flange to be machined.

Based on the ANSYS of Small and Deep Hole a Bit Cutting Transient Dynamic Study

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.503-504.802 ◽

2012 ◽

Vol 503-504 ◽

pp. 802-804 ◽

Cited By ~ 1

Author(s):

Ping Wei ◽

Jiang Han ◽

Hui Lian Geng

Keyword(s):

Cutting Tool ◽

Cutting Parameters ◽

Empirical Method ◽

Deep Hole ◽

Processing Efficiency ◽

Element Analysis ◽

The Finite Element Analysis ◽

Scene Processing ◽

Selection Of

Introduces the general twist drill analysis, Through the continuous experiment and practice, obtained some processing key question method. As a result of small and deep hole parts of the scene processing efficiency is low, the improper selection of cutting quantity will cause premature wear of cutting tool. The traditional method is the empirical method and experimental method to solve the waste of material and time comparison. This article mainly is the finite element analysis software, the analysis of the ordinary drill thermal stress, transient dynamics, to solve the optimization of cutting parameters before processing, improve processing efficiency, save cost.

INVESTIGATION OF SURFACE MORPHOLOGY OF DRILLED CFRP PLATES AND OPTIMIZATION OF CUTTING PARAMETERS

Surface Review and Letters ◽

10.1142/s0218625x19502093 ◽

2020 ◽

Vol 27 (09) ◽

pp. 1950209

Author(s):

ÖMER ERKAN ◽

GÖKHAN SUR ◽

ENGIN NAS

Keyword(s):

Surface Roughness ◽

Feed Rate ◽

Design Method ◽

Cutting Speed ◽

Cutting Parameters ◽

Feed Speed ◽

Reinforced Polymer ◽

Cfrp Composite ◽

Full Factorial

In this study, the carbon fiber reinforced polymer (CFRP) composite material was drilled using different parameters ([Formula: see text] and [Formula: see text] Point Angle, 30, 60 and [Formula: see text] cutting speed and 0.06, 0.08 and [Formula: see text] feed rate). Experimental parameters were designed according to full factorial design method and the results were analyzed using Taguchi L18 experimental design. The results of the study show that the lowest surface roughness values are 0.1958 and [Formula: see text]m with the cutting speed of 90 [Formula: see text] and feed rate of [Formula: see text] in the Point angles of [Formula: see text] and [Formula: see text], respectively. When the results of Anova analysis were evaluated, parameters (feed speed, cutting speed and end point angle) according to the effect ratios on surface roughness were formed at the rates of 41.06%, 33.13% and 5.07%, respectively. The most suitable parameters according to S/N ratios were determined using A2B3C1 factors for the average surface roughness.