Effect of Dressing Infeed on Alumina Wheel During Grinding Ti–6Al–4V Under Varying Depth of Cut

2019 ◽  
pp. 551-560
Author(s):  
Manish Mukhopadhyay ◽  
Souvik Chatterjee ◽  
Pranab Kumar Kundu ◽  
Santanu Das
Keyword(s):  
Depth Of Cut

Deformation Replication

1970 ◽  
Vol 28 ◽  
pp. 280-281
Author(s):  
J. Temple Black
Keyword(s):  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Rake Face ◽  
Orthogonal Machining ◽  
Aluminum Chips ◽  
Before And After ◽  
Materials Used ◽  
Glass Knife ◽  
Very High

Tool materials used in ultramicrotomy are glass, developed by Latta and Hartmann (1) and diamond, introduced by Fernandez-Moran (2). While diamonds produce more good sections per knife edge than glass, they are expensive; require careful mounting and handling; and are time consuming to clean before and after usage, purchase from vendors (3-6 months waiting time), and regrind. Glass offers an easily accessible, inexpensive material ($0.04 per knife) with very high compressive strength (3) that can be employed in microtomy of metals (4) as well as biological materials. When the orthogonal machining process is being studied, glass offers additional advantages. Sections of metal or plastic can be dried down on the rake face, coated with Au-Pd, and examined directly in the SEM with no additional handling (5). Figure 1 shows aluminum chips microtomed with a 75° glass knife at a cutting speed of 1 mm/sec with a depth of cut of 1000 Å lying on the rake face of the knife.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Possible examinations of stone machining focused on the relationship between technological parameters and surface quality

2013 ◽  
Vol 4 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Zs. Kun ◽  
I. G. Gyurika
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Theoretical Background ◽  
Motion Path ◽  
Depth Of Cut ◽  
Manufacturing Cost ◽  
Technological Parameters ◽  
The Relationship ◽  
Manufacturing Time

Abstract The stone products with different sizes, geometries and materials — like machine tool's bench, measuring machine's board or sculptures, floor tiles — can be produced automatically while the manufacturing engineer uses objective function similar to metal cutting. This function can minimise the manufacturing time or the manufacturing cost, in other cases it can maximise of the tool's life. To use several functions, manufacturing engineers need an overall theoretical background knowledge, which can give useful information about the choosing of technological parameters (e.g. feed rate, depth of cut, or cutting speed), the choosing of applicable tools or especially the choosing of the optimum motion path. A similarly important customer's requirement is the appropriate surface roughness of the machined (cut, sawn or milled) stone product. This paper's first part is about a five-month-long literature review, which summarizes in short the studies (researches and results) considered the most important by the authors. These works are about the investigation of the surface roughness of stone products in stone machining. In the second part of this paper the authors try to determine research possibilities and trends, which can help to specify the relation between the surface roughness and technological parameters. Most of the suggestions of this paper are about stone milling, which is the least investigated machining method in the world.

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

2020 ◽  
Vol 38 (12A) ◽  
pp. 1862-1870
Author(s):  
Safa M. Lafta ◽  
Maan A. Tawfiq
Keyword(s):  
Stainless Steel ◽  
Residual Stresses ◽  
Orthogonal Cutting ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Main Role ◽  
Turning Operation ◽  
Aisi 316

RS (residual stresses) represent the main role in the performance of structures and machined parts. The main objective of this paper is to investigate the effect of feed rate with constant cutting speed and depth of cut on residual stresses in orthogonal cutting, using Tungsten carbide cutting tools when machining AISI 316 in turning operation. AISI 316 stainless steel was selected in experiments since it is used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, food and beverage industry. Four feed rates were selected (0.228, 0.16, 0.08 and 0.065) mm/rev when cutting speed is constant 71 mm/min and depth of cutting 2 mm. The experimental results of residual stresses were (-15.75, 12.84, 64.9, 37.74) MPa and the numerical results of residual stresses were (-15, 12, 59, and 37) MPa. The best value of residual stresses is (-15.75 and -15) MPa when it is in a compressive way. The results showed that the percentage error between numerical by using (ABAQUS/ CAE ver. 2017) and experimental work measured by X-ray diffraction is range (2-15) %.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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 An Investigation of the Cutting Strategy for the Machining of Polar Microstructures Used in Ultra-Precision Machining Optical Precision Measurement

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 755
Author(s):  
Chen-Yang Zhao ◽  
Chi-Fai Cheung ◽  
Wen-Peng Fu
Keyword(s):  
Precision Measurement ◽  
Detection Algorithm ◽  
Surface Generation ◽  
Precision Machining ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Ultra Precision ◽  
Transition Points ◽  
Cutting Strategy

In this paper, an investigation of cutting strategy is presented for the optimization of machining parameters in the ultra-precision machining of polar microstructures, which are used for optical precision measurement. The critical machining parameters affecting the surface generation and surface quality in the machining of polar microstructures are studied. Hence, the critical ranges of machining parameters have been determined through a series of cutting simulations, as well as cutting experiments. First of all, the influence of field of view (FOV) is investigated. After that, theoretical modeling of polar microstructures is built to generate the simulated surface topography of polar microstructures. A feature point detection algorithm is built for image processing of polar microstructures. Hence, an experimental investigation of the influence of cutting tool geometry, depth of cut, and groove spacing of polar microstructures was conducted. There are transition points from which the patterns of surface generation of polar microstructures vary with the machining parameters. The optimization of machining parameters and determination of the optimized cutting strategy are undertaken in the ultra-precision machining of polar microstructures.

Sign in / Sign up

Export Citation Format

Share Document