scholarly journals Assessments of Process Parameters on Cutting Force and Surface Roughness during Drilling of AA7075/TiB2 In Situ Composite

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1726
Author(s):  
S. Parasuraman ◽  
I. Elamvazuthi ◽  
G. Kanagaraj ◽  
Elango Natarajan ◽  
A. Pugazhenthi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Basic Class ◽  
Tib2 Particles ◽  
The Impact ◽  
Loss Of Strength

Reinforced aluminum composites are the basic class of materials for aviation and transport industries. The machinability of these composites is still an issue due to the presence of hard fillers. The current research is aimed to investigate the drilling topographies of AA7075/TiB2 composites. The samples were prepared with 0, 3, 6, 9 and 12 wt.% of fillers and experiments were conducted by varying the cutting speed, feed, depth of cut and tool nose radius. The machining forces and surface topographies, the structure of the cutting tool and chip patterns were examined. The maximum cutting force was recorded upon increase in cutting speed because of thermal softening, loss of strength discontinuity and reduction of the built-up-edge. The increased plastic deformation with higher cutting speed resulted in the excess metal chip. In addition, the increase in cutting speed improved the surface roughness due to decrease in material movement. The cutting force was decreased upon high loading of TiB2 due to the deterioration of chips caused by fillers. Further introduction of TiB2 particles above 12 wt.% weakened the composite; however, due to the impact of the microcutting action of the fillers, the surface roughness was improved.

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%.

Intelligent Monitoring and Estimation of Surface Roughness on CNC Turning

2010 ◽  
Vol 443 ◽  
pp. 376-381 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Monitoring ◽  
Prediction Interval ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Roughness Model ◽  
Force Ratio

In order to realize an intelligent machine tool, an in-process monitoring system is developed to estimate the in-process surface roughness. The objective of this research is to propose a method to estimate the surface roughness during the in-process cutting by utilizing the in-process monitoring of cutting forces. The proposed in-process surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval of the in-process surface roughness model has been also presented to monitor and control the in-process estimated surface roughness at 95% confident level. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be effectively used to monitor and estimate the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy achieved.

Investigation on Cutting Force, Flank Wear, and Surface Roughness in Machining of the A356-TiB2/TiC in-situ Composites

2018 ◽  
Vol 5 (2) ◽  
pp. 45-77 ◽  
Author(s):  
Ismail Kakaravada ◽  
Arumugam Mahamani ◽  
V. Pandurangadu
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Cutting Force ◽  
Feed Rate ◽  
Flank Wear ◽  
Cutting Speed ◽  
Reinforcement Ratio ◽  
Depth Of Cut ◽  
In Situ Composites

This article reveals the experimental investigation on the machinability of A356-TiB2/TiC in-situ composites prepared by a mixed salt reaction system. The fabricated composites are characterized by Energy dispersive analysis (EDAX), X-ray Diffraction (XRD), scanning electron microscopy (SEM) and micro-hardness analysis. Multi-coated tungsten carbide tool was used to examine the influence of TiB2/TiC reinforcement ratio on machinability behaviour of composites. The variations in cutting speed, feed rate and depth of cut upon cutting force, surface roughness and flank wear were examined. The experimental results revealed that the enhancement of a reinforcement ratio causes the decrease in cutting force and increase in flank wear and surface roughness. Higher flank wear is observed, when machining the A356-TiB2/TiC composites at higher cutting speed due to the generation of high temperature at the machining interface. The increment in surface roughness, flank wear and cutting force is experienced at higher depth of cut and feed rate. Further, the mechanisms of chip formation and surface generation under different machining parameters are addressed. The outcome of this experimental investigation helps to utilize the turning process for machining the in-situ composites at economic machining rate without compromising the surface quality.

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.

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

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 ◽  
Optimization Of Cutting Parameters

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

scholarly journals Machining force comparison for surface defect hard turning and conventional hard turning of AISI 52100 steel

2021 ◽  
Vol 13 (3) ◽  
pp. 205-214
Author(s):  
P. U MAMAHESWARRAO ◽  
D. RANGARAJU ◽  
K. N. S. SUMAN ◽  
B. RAVISANKAR
Keyword(s):  
Hard Turning ◽  
Surface Defect ◽  
Cutting Speed ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel ◽  
The Impact

In this article, a recently developed method called surface defect machining (SDM) for hard turning has been adopted and termed surface defect hard turning (SDHT). The main purpose of the present study was to explore the impact of cutting parameters like cutting speed, feed, depth of cut, and tool geometry parameters such as nose radius and negative rake angle of the machining force during surface defect hard turning (SDHT) of AISI 52100 steel in dry condition with Polycrystalline cubic boron nitride (PCBN) tool; and results were compared with conventional hard turning (CHT). Experimentation is devised and executed as per Central Composite Design (CCD) of Response Surface Methodology (RSM). Results reported that an average machining force was decreased by 22% for surface defect hard turning (SDHT) compared to conventional hard turning (CHT).

The impact of cutting speed and depth of cut on cutting force during turning of austempered ductile iron

2019 ◽  
Vol 19 ◽  
pp. 663-669
Author(s):  
Prashant Parhad ◽  
Vinayak Dakre ◽  
Ajay Likhite ◽  
Jatin Bhatt
Keyword(s):  
Cutting Force ◽  
Ductile Iron ◽  
Cutting Speed ◽  
Austempered Ductile Iron ◽  
Depth Of Cut ◽  
The Impact

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

PREDICTION AND INVESTIGATION OF SURFACE ROUGHNESS WHILE TURNING SG IRON WITH CUBIC BORON NITRIDE (CBN) AND TUNGSTEN CARBIDE INSERTS

2017 ◽  
Vol 41 (1) ◽  
pp. 129-141 ◽  
Author(s):  
K.M. Kumar ◽  
P. Hariharan
Keyword(s):  
Surface Roughness ◽  
Boron Nitride ◽  
Tungsten Carbide ◽  
Cubic Boron Nitride ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Spheroidal Graphite ◽  
Surface Model ◽  
Sg Iron ◽  
The Impact

This work compares the effect of cubic boron nitride (CBN) and multilayer (TiCN+Al2O3+TiN) coated tungsten carbide (WC) tools during the turning of spheroidal graphite (SG) nodular iron. Nodular irons have more ductility which is required in mechanical components that demand high fatigue resistance like crankshafts, cam shafts, bearing caps and clutch housings. The impact of various process parameters like the depth of cut, cutting speed and feed on the surface roughness (Ra) of SG iron is studied and optimized using the response surface model. The chip morphology is also discussed for evaluation of the quality of the turned surface. The experimental outcomes reveal that the WC tool offers a high surface finish at the optimal combination of the cutting speed at 102 meter/minute, feed at 0.051 millimeter/revolution and depth of cut at 0.5 millimeter and that, for the CBN insert, at 245 meter/minute of cutting speed, 0.051 millimeter/revolution of feed and 0.75 millimeter of depth of cut.

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