Effect of Cutting Parameter on Material Removal Rate and Surface Roughness in Deep Drilling Process

2016 ◽  
Vol 2 (5) ◽  
pp. 274-285
Author(s):  
Mostafa A. Abdullah ◽  
Ahmed B. Abdulwahhab ◽  
Atheer R. Mohammed
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Drilling Process ◽  
Deep Drilling ◽  
Cutting Parameter

scholarly journals Effect of Cutting Parameter on Material Removal Rate and Surface Roughness in Deep Drilling Process

2016 ◽  
Vol 2 (5) ◽  
Author(s):  
Mostafa A. Abdullah  , Ahmed B. Abdulwahhab   ,   Atheer R.
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Spindle Speed ◽  
Cutting Conditions ◽  
Drilling Process ◽  
Tool Diameter ◽  
Twist Drills

In the curents study aimed to assess the effects of cutting conditions  (spindle speed, feed rate, tool diameter) parameters as input impact on material removal rate (MRR) and surface roughness (Ra) as output of steel (AISI 1015). A number of drilling experiments were conducted using the L9 orthogonal array on conventional drilling machine with use feed rate (0.038,0.076,0.203) mm/rev and spindle speed (132,550,930) rpm and tool diameter (11,15,20) mm HSS twist drills under dry cutting conditions. Analysis of variance (ANOVA) was employed to determine the most significant control factors affecting on surface roughness and MRR. The result shown the tool diameter the important factor effect with (64.08%) and (76.12%) on MRR and surface roughness respectively.

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.

Effect of SiC-Cu Electrode on Material Removal Rate, Tool Wear and Surface Roughness in EDM Process

2020 ◽  
Vol 38 (9A) ◽  
pp. 1406-1413
Author(s):  
Yousif Q. Laibia ◽  
Saad K. Shather
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
304 Stainless Steel ◽  
Tool Electrode ◽  
Pulse Time ◽  
Edm Process

Electrical discharge machining (EDM) is one of the most common non-traditional processes for the manufacture of high precision parts and complex shapes. The EDM process depends on the heat energy between the work material and the tool electrode. This study focused on the material removal rate (MRR), the surface roughness, and tool wear in a 304 stainless steel EDM. The composite electrode consisted of copper (Cu) and silicon carbide (SiC). The current effects imposed on the working material, as well as the pulses that change over time during the experiment. When the current used is (8, 5, 3, 2, 1.5) A, the pulse time used is (12, 25) μs and the size of the space used is (1) mm. Optimum surface roughness under a current of 1.5 A and the pulse time of 25 μs with a maximum MRR of 8 A and the pulse duration of 25 μs.

Enhancement of EDM Performance by Using Copper-Silver Composite Electrode

2020 ◽  
Vol 38 (9A) ◽  
pp. 1352-1358
Author(s):  
Saad K. Shather ◽  
Abbas A. Ibrahim ◽  
Zainab H. Mohsein ◽  
Omar H. Hassoon
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
High Speed ◽  
Composite Electrode ◽  
Removal Rate ◽  
Pure Copper ◽  
High Speed Steel ◽  
Pulse On Time ◽  
Pulse Off Time

Discharge Machining is a non-traditional machining technique and usually applied for hard metals and complex shapes that difficult to machining in the traditional cutting process. This process depends on different parameters that can affect the material removal rate and surface roughness. The electrode material is one of the important parameters in Electro –Discharge Machining (EDM). In this paper, the experimental work carried out by using a composite material electrode and the workpiece material from a high-speed steel plate. The cutting conditions: current (10 Amps, 12 Amps, 14 Amps), pulse on time (100 µs, 150 µs, 200 µs), pulse off time 25 µs, casting technique has been carried out to prepare the composite electrodes copper-sliver. The experimental results showed that Copper-Sliver (weight ratio70:30) gives better results than commonly electrode copper, Material Removal Rate (MRR) Copper-Sliver composite electrode reach to 0.225 gm/min higher than the pure Copper electrode. The lower value of the tool wear rate achieved with the composite electrode is 0.0001 gm/min. The surface roughness of the workpiece improved with a composite electrode compared with the pure electrode.

High-Throughput Picosecond Laser Machining of Aerospace Nickel Superalloy

2021 ◽  
pp. 095440542110288
Author(s):  
Sundar Marimuthu ◽  
Bethan Smith
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Thermal Damage ◽  
Removal Rate ◽  
Picosecond Laser ◽  
Nickel Superalloy ◽  
Laser Machining ◽  
Process Conditions ◽  
Machining Performance

This manuscript discusses the experimental results on 300 W picosecond laser machining of aerospace-grade nickel superalloy. The effect of the laser’s energetic and beam scanning parameters on the machining performance has been studied in detail. The machining performance has been investigated in terms of surface roughness, sub-surface thermal damage, and material removal rate. At optimal process conditions, a picosecond laser with an average power output of 300 W can be used to achieve a material removal rate (MRR) of ∼140 mm3/min, with thermal damage less than 20 µm. Shorter laser pulse widths increase the material removal rate and reduce the resultant surface roughness. High scanning speeds improve the picosecond laser machining performance. Edge wall taper of ∼10° was observed over all the picosecond laser machined slots. The investigation demonstrates that high-power picosecond lasers can be used for the macro-machining of industrial components at an acceptable speed and quality.

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.

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