scholarly journals Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Adel T. Abbas ◽  
Karim Hamza ◽  
Mohamed F. Aly ◽  
Essam A. Al-Bahkali
Keyword(s):  
Multiobjective Optimization ◽  
Surface Quality ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Trade Off ◽  
Good Surface ◽  
Good Surface Quality ◽  
Steel Material

This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel) with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut) that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO) algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.

Lapping and Polishing of Sapphire Wafer with Fixed Abrasive Pad

2014 ◽  
Vol 1027 ◽  
pp. 68-71 ◽  
Author(s):  
Jian Bin Wang ◽  
Yong Wei Zhu ◽  
Jun Xu ◽  
Zhan Kui Wang ◽  
Ji Hua Miao
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Sapphire Wafer ◽  
Good Surface ◽  
Good Surface Quality ◽  
Fixed Abrasive Pad ◽  
Fixed Abrasive

The processing technology of sapphire with a high material removal rate a good surface quality is critical for its applications. The experiment of sapphire lapping and polishing was carried out by using three different fixed abrasive pad (FAP). Their material removal rate (MRR) and surface roughness (Ra) were measured and analyzed. Results indicate that a MRR of 5.6μm/min reaches in rough lapping and a MRR of 0.4μm/min in fine lapping. The average surface roughness Ra of rough lapping and fine lapping is 142nm and 1.2nm respectively. The processing efficiency of sapphire wafer is effectively improved and a good surface quality is obtained when FAP adopted.

scholarly journals Micromachining in Powder-Mixed Micro Electrical Discharge Machining

2020 ◽  
Vol 10 (11) ◽  
pp. 3795 ◽  
Author(s):  
Gunawan Setia Prihandana ◽  
Muslim Mahardika ◽  
Tutik Sriani
Keyword(s):  
Surface Quality ◽  
Material Removal Rate ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Process ◽  
Dielectric Fluid ◽  
Good Surface ◽  
Micro Electrical Discharge Machining ◽  
Powder Characteristics

Micromachining in the micro-electric discharge machining (μ-EDM) process requires high material-removal rate with good surface quality. Power-mixed μ-EDM, a modified machining process by introducing specific powder into the dielectric fluid, is among the key inventions to achieving these requirements. This article presents a review of the implementation of powder-mixed micro-EDM processes for microfabrication. Special attention was given to the influence of the powder characteristics, such as the concentration, electrical conductivity, shape and size of the powder. Subsequently, when describing the use of powder for obtaining a high material-removal rate and surface quality, other major applications in μ-EDM for surface modification and geometrical accuracy were also discussed. Finally, some of the varied methods that are used in powder-mixed μ-EDM and industrialization challenges are extensively elaborated.

The Experiment Research of Precision Grinding of Li-Ti Ferrite with Graphite Grinding Wheel

2010 ◽  
Vol 431-432 ◽  
pp. 322-325
Author(s):  
Bei Zhang ◽  
Hong Hua Su ◽  
Hong Jun Xu ◽  
Yu Can Fu
Keyword(s):  
Surface Quality ◽  
Material Removal ◽  
Removal Rate ◽  
High Surface ◽  
Ground Surface ◽  
Grinding Wheel ◽  
Precision Grinding ◽  
High Surface Quality ◽  
Good Surface ◽  
Good Surface Quality

Li-Ti ferrite used in aviation occasions needs good surface quality. In conventional grinding it is difficult to meet the surface demand. Accordingly, this paper proposed a new grinding process to change the situation. The process employed graphite grinding wheel which is always used in ultra-precision grinding of steel piece. The process can obtain good surface quality and ensure certain material removal rate. The ground surface appearance is nearly mirror-like. The lowest surface roughness of Ra value of the ground surface is 0.05μm in the experiment. The ground surface morphology is made up of spread glazed area and dispersed minute pits. The ductile regime dominates the material removal mechanism and no surface damage is induced in the process. In consideration of the results in the experiment it can be seen that grinding with graphite grinding wheel is a good finishing procedure in ferrite machining because of its obtained high surface quality.

Free and Fixed Abrasive Lapping of BK7 Glass

2016 ◽  
Vol 693 ◽  
pp. 780-787
Author(s):  
Jun Li ◽  
Y.K. Tang ◽  
Y.W. Zhu ◽  
Y. L. Sun ◽  
Dun Wen Zuo
Keyword(s):  
Surface Quality ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Surface Damage ◽  
Good Surface ◽  
Bk7 Glass ◽  
Fixed Abrasive ◽  
Damage Categories ◽  
Free Abrasive

Fixed abrasive technology which has many advantages is one of the future machining directions. Free and fixed abrasive lapping of BK7 glass was investigated and different material removal modes and surface damage categories by lapping were discussed. The results show that material removal rate is larger for free abrasive lapping than that of fixed abrasive lapping with four abrasive sizes and decreases with diamond size decreasing in two lapping processes. Surface quality is better for fixed abrasive lapping than that of free abrasive lapping at the same diamond size and gets better with the decreasing of diamond size. Fixed abrasive lapping can achieve simultaneously high MRR and good surface quality.

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

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 ◽  
Optimization Of Cutting Parameters

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.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

Modelling and Optimization of End Milling Process Using TLBO and TOPSIS Algorithm

2017 ◽  
pp. 54-81 ◽  
Author(s):  
Atul Tiwari ◽  
Mohan Kumar Pradhan
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Metal Cutting ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Depth Of Cut ◽  
Multi Objective ◽  
Order Preference

To assure desire quality of machined products at minimum machining costs and maximum material removal rate, it is very important to select optimum parameters when metal cutting machine tool are used. Minimum Surface Roughness (Ra) is commonly desirable for the component; however Material Removal Rate (MRR) should be maximized. This chapter presents an approach for determination of the best cutting parameters precede to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology with Multi-Objective Technique for Order Preference by Similarity to Ideal Solution and Teaching and learning based optimization algorithm in face milling of Al-6061 alloy. 30 experiments have been conducted based on RSM with 4 parameters, namely Speed, Feed, Depth of Cut and Coolant Speed and three levels each. ANOVA is performed to find the most influential input parameters for both MRR and Ra. Later the multi-objective attribution selection method TOPSIS and multi objective optimization method TLBO is used to optimize the responses.

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