scholarly journals Optimizing 3D Printed Metallic Object’s Postprocessing: A Case of Gamma-TiAl Alloys

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1246
Author(s):  
M. A. K. Chowdhury ◽  
AMM Sharif Ullah ◽  
Roberto Teti
Keyword(s):  
Surface Roughness ◽  
Additive Manufacturing ◽  
Cutting Force ◽  
Surface Finish ◽  
Intelligent Systems ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Powder Size ◽  
Tial Alloys ◽  
Possibility Distributions

Gamma-TiAl (γ-TiAl) alloys can be used in high-end products relevant to the aerospace, defense, biomedical, and marine industries. Fabricating objects made of γ-TiAl alloys needs an additive manufacturing process called Electron Beam Melting (EBM) or other similar processes because these alloys are difficult-to-cut materials. An object fabricated by EBM exhibits poor surface finish and must undergo postprocessing. In this study, cylindrical specimens were fabricated by EBM and post-processed by turning at different cutting conditions (cutting speed, depth of cut, feed rate, insert radius, and coolant flowrate). The EBM conditions were as follows: average powder size 110 μm, acceleration voltage 60 kV, beam current 10 mA, beam scanning speed 2200 mm/s, and beam focus offset 0.20 mm. The surface roughness and cutting force were recorded for each set of cutting conditions. The values of the cutting conditions were set by the L36 Design of Experiment approach. The effects of the cutting conditions on surface roughness and cutting force are elucidated by constructing the possibility distributions (triangular fuzzy numbers) from the experimental data. Finally, the optimal cutting conditions to improve the surface finish of specimens made of γ-TiAl alloys are determined using the possibility distributions. Thus, this study’s outcomes can be used to develop intelligent systems for optimizing additive manufacturing processes.

Effects of Cutting Conditions on the Machinability of Stainless Steel Formed by Laser Cladding

2013 ◽  
Vol 797 ◽  
pp. 166-171
Author(s):  
Bing Wang ◽  
Zhan Qiang Liu ◽  
Lun Chang Su ◽  
Lin Qing Zhang
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Force ◽  
Laser Cladding ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Corner Radius ◽  
1045 Steel

The paper investigates the effects of cutting conditions on the machinability of stainless steel coatings manufactured onto AISI 1045 steel by laser cladding technology. Two kinds of CBN (cubic boron nitride) tools with different corner radius and two different depths of cut were adopted in the experiments. Cutting force during machining, surface roughness and microhardness of machined surface were measured and analyzed. The results show that both the cutting force and surface roughness increase with the increase of depth of cut. When the other cutting parameters are identical, the surface roughness decreases with the increase of tools corner radius while the variations of different cutting force components present different tendencies. The microhardness of the machined surface and its varied gradient in the direction of depth of cut increase with the increase of tools corner radius. The experiment results will provide valuable suggestions for optimization of cutting performance for laser cladding coatings in order to obtain excellent surface quality.

Experimental Investigations of Cutting Conditions Influence on Main Cutting Force and Surface Roughness Based on Chip Form Types in Cast Nylon Turning

2011 ◽  
Vol 110-116 ◽  
pp. 3563-3569 ◽  
Author(s):  
Bandit Suksawat
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Causal Effect ◽  
Cutting Speed ◽  
High Speed Steel ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Path Coefficient ◽  
Cutting Depth

This paper aims to investigate cutting conditions influence on main cutting force and surface roughness based on considered chip form types in cast nylon turning operation with single-point high speed steel cutting tool. The 75 experiments were performed by average of three levels of cutting speed, five levels of cutting depth and five levels of feed rate. The results reveal that main cutting forces were increased by an increasing of cutting speed and cutting depth for all obtained chip form types for all chip form types. The surface roughness is affected by increasing of feed rate and reduction of cutting speed for 2.3 Snarled and 4.3 Snarled chip form types. The statistical path-coefficient analysis results are shown that the main cutting force affected by cutting speed, depth of cut and feed rate with total causal effect value of 0.5537, 0.4785 and 0.1718, respectively. The surface roughness is influenced by feed rate, cutting speed and depth of cut with 0.8400, -0.2419 and-0.0711 of total causal effect value, respectively. These results are useful to perform varying cutting conditions for high quality of workpiece in cast nylon turning by control the chip form type.

Modelling and optimization of the cutting conditions in hard turning of high-alloy white cast iron (Ni-Hard)

2012 ◽  
Vol 227 (10) ◽  
pp. 2280-2290 ◽  
Author(s):  
Emre Yücel ◽  
Mustafa Günay
Keyword(s):  
Surface Roughness ◽  
Cast Iron ◽  
Cutting Force ◽  
Analysis Of Variance ◽  
Feed Rate ◽  
Hard Turning ◽  
White Cast Iron ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
High Alloy

The aim of this study is to model and optimize the cutting conditions for the cutting force ( Fc) and average surface roughness ( Ra) that result from the machining of high-alloy white cast iron (Ni-Hard). The hard turning experiments were carried out on a CNC lathe with ceramic and CBN inserts. Cutting tool material, cutting speed, feed rate and depth of cut were chosen as the cutting conditions (control factors). Taguchi’s L18 orthogonal array was used for design of experiment. Optimum levels of the cutting conditions were determined using signal-to-noise ( S/N) ratio, which was calculated for machining output variables ( Fc and Ra) according to the ‘the-smaller-the-better’ approach. The effects of the cutting conditions on machining output variables were evaluated by the analysis of variance. The analysis of variance results showed that the depth of cut and feed rate were the most significant factors on Fc and Ra, respectively. Besides, the optimal cutting conditions for main cutting force and surface roughness were found at the different levels.

scholarly journals Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel

2012 ◽  
Vol 9 (1) ◽  
pp. 37 ◽  
Author(s):  
LB Abhang ◽  
M Hameedullah
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Engineering Sciences

 Due to the widespread use of highly automated machine tools in the metal cutting industry, manufacturing requires highly reliable models and methods for the prediction of output performance in the machining process. The prediction of optimal manufacturing conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the steel turning process the tool geometry and cutting conditions determine the time and cost of production which ultimately affect the quality of the final product. In the present work, experimental investigations have been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models are developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model has been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research etc. The LINGO solver program is global optimization software. It gives minimum values of surface roughness and their respective optimal conditions. 

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.

Nature instigated Grey wolf algorithm for parametric optimization during machining (Milling) of polymer nanocomposites

2021 ◽  
pp. 089270572199320
Author(s):  
Prakhar Kumar Kharwar ◽  
Rajesh Kumar Verma
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Polymer Nanocomposites ◽  
Feed Rate ◽  
Fitness Function ◽  
Depth Of Cut ◽  
Multiwall Carbon Nanotube ◽  
Grey Wolf ◽  
Milling Characteristics ◽  
The Impact

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering

2016 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
A. H. M. Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Wavelet Transforms ◽  
Fourier Transforms ◽  
End Milling ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machined Surface ◽  
Vibration Signals

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

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