scholarly journals Optimizing The Machining Parameters For Minimum Surface Roughness In Turning Al/6% SiC/6%RHA Hybrid Composites

2015 ◽  
Vol 10 ◽  
pp. 220-229 ◽  
Author(s):  
Chintada Shoba ◽  
Nallu Ramanaiah ◽  
Damera Nageswara Rao
Keyword(s):  
Surface Roughness ◽  
Hybrid Composites ◽  
Machining Parameters ◽  
Minimum Surface

Multi Objective Optimization of Cutting Parameters in Machining Cellulose Based Hybrid Composites

2015 ◽  
Vol 761 ◽  
pp. 287-292
Author(s):  
Raja Izamshah ◽  
Zainudin Zuraidah ◽  
Mohd Shahir Kasim ◽  
M. Hadzley ◽  
M. Amran
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Hybrid Composites ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Machining Performance ◽  
Machined Surface ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Cellulose based hybrid composites are gaining popularity in the growing green communities. With extensive studies and increasing applications for future advancement, the need for an accurate and reliable guidance in machining this type of composites has increased enormously. Smooth and defect free machined surface are always the ultimate objectives. The present work deals with the study of machining parameters (i.e. spindle speed, feed rate and depth of cut) and their effects on machining performance (i.e. surface roughness and delamination) to establish an optimized setup of machining parameters in achieving multi objective machining performance. Cellulose based hybrid composites consist of jute (a bast fiber) and glass fiber embedded in polyester resins. Response Surface Methodology (RSM) using Box-Behnken Design (BBD) was chosen as the design of experiment approach for this study. Based on that experimental approach, 17 experimental runs were conducted. Mathematical model for each response was developed based on the experimental data. Adequacy of the models were analyzed statistically using Analysis of Variance (ANOVA) in determining the significant input variables and possible interactions. The multi objective optimization was performed through numerical optimization, and the predicted results were validated. The agreement between the experimental and selected solution was found to be strong, between 95% to 96%, thus validating the solution as the optimal machining condition. The findings suggest that feed rate was the main factor affecting surface roughness and delamination .

scholarly journals Experimental Investigation of Cutting Parameters Effects on the Surface Roughness and Tools Wear during the Drilling of Fiber Reinforced Composite Materials

2019 ◽  
Vol 38 (3) ◽  
pp. 717-728 ◽  
Keyword(s):  
Surface Roughness ◽  
Composite Material ◽  
Tool Wear ◽  
Cutting Speed ◽  
Fiber Reinforced Polymer ◽  
Machining Parameters ◽  
Fiber Reinforced ◽  
Minimum Surface ◽  
Reinforced Polymer ◽  
Tools Wear

Optimization of the drilling parameters of the composite material is the key objective of this research, enhancing the surface roughness and minimizing the tool wear. In contrary to the other research, optimizing the machining parameters for a specific composite material for the mass productions, machining parameters are optimized for GFRP (Glass Fiber Reinforced Polymer), CFRP (Carbon Fiber Reinforced Polymer) and KFRP (Kevlar Fiber Reinforced Polymer) for the job shop production. In this research, the machining parameters are optimized for the enhanced surface roughness and minimum tool wear by varying the three types of composite material and three levels of the cutting speed. Nine experiments were performed, which were repeated twice in random manner to eliminate the biasness of the results. In these experiments, PVD (Physical Vapor Deposition) coated carbide inserts are used with the same geometry. Seventeen holes were machined in a single experiment, which surface roughness is measured by cutting the composite plate from middle of the hole and using the Countermatic surface roughness meter at different locations. Average surface roughness is determining for each set of varying parameters and plotted to observe the set of parameters for the minimum surface roughness. It has been observed that the minimum surface roughness are observed at; 1500 rpm in GFRP, 2000 rpm in CFRP and at 2500 rpm in KFRP. Finally, the wear patterns are also observed on the drill inserts using SEM (Scanning Electron Microscope) and it has been found that no prominent wear has been observed in the drill inserts, whereas, prominent depletion of coating are found at the higher cutting speed.

Study of Machining Parameter Optimization in Drilling of Magnesium Based Hybrid Composites Reinforced with SiC/CNT

2020 ◽  
Vol 846 ◽  
pp. 42-46 ◽  
Author(s):  
J.S.Suresh Babu ◽  
Min Heo ◽  
Chung Gil Kang
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Hybrid Composites ◽  
Spindle Speed ◽  
Influential Factor ◽  
Burr Formation ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Wide Range ◽  
Machining Parameter

Recently, researchers and engineers have been interested in the development of hybrid metal matrix composites (HMMCs) for the applications of automotive and aerospace industries owing to their superior properties due to the usage a wide range of material combinations in its manufacturing. The present study focuses on the machining of magnesium based hybrid composites reinforced with CNT (1vol.%) and SiC (2vol.%).The influence of machining parameters such as spindle speed, feed rate, drill diameter and point angle on burr formation and surface roughness on drilling the composites were investigated using Taguchi method. The drilling parameters were optimized by using ANOVA experimental design and also find out the percentage of contribution of each factor. Based on the results, the most influential factor for the burr thickness was spindle speed and point angle. While spindle speed and feed rate were the influencing factors for surface roughness. The analysis revealed that burr height, burr thickness, and surface roughness decreases significantly with an increase in spindle speed.

scholarly journals OPTIMIZATION OF MACHINING PARAMETERS WITH MINIMUM SURFACE ROUGHNESS FOR THREE-AXIS MILLING OF SCULPTURED PARTS

2017 ◽  
Vol 20 (2) ◽  
pp. 34-38
Author(s):  
Djordje Cica ◽  
◽  
Milan Zeljkovic ◽  
Branislav Sredanovic ◽  
Sasa Tesic ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Machining Parameters ◽  
Minimum Surface ◽  
Sculptured Parts

COMPARISON OF MECHANICAL PROPERTIES AND CALCULATION OF CUTTING FORCE BETWEEN UNCOATED TUNGSTEN CARBIDE & Ti COATED TIP TUNGSTEN CARBIDE WITH THE HELP OF LATHE TOOL DYNAMOMETER

2013 ◽  
Vol 22 ◽  
pp. 645-653 ◽  
Author(s):  
DEEPAK MEHRA ◽  
KHUSHWANT RAKHECHA
Keyword(s):  
Surface Roughness ◽  
Tungsten Carbide ◽  
High Speed ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Manufacturing Costs ◽  
Minimum Surface ◽  
Lathe Tool

The keyword for manufacturers of cutting tools and coatings for cutting tools is productivity: a 30%; reduction of tool costs, or a 50%; increase in tool lifetime results only in a 1%; reduction of manufacturing costs. But an increase in cutting data by 20%; reduces manufacturing costs by 15%;. In order to achieve higher productivity different approaches – High Performance Cutting (HPC) and High Speed Cutting (HSC) can be chosen. The performance of Carbide tools was studied to investigate the tool life and wear behavior at various machining parameters. This study presents tool wear characterization of carbide cutting tool inserts coated with titanium nitride (TiN) on a single point turning operation on copper, aluminum. A set of experiments with conditions of cutting speed, depth of cut and feed rate were performed on a lathe machine. Force analysis is done on Lathe Tool Dynamometer. From the result, cutting speed was found to be the main factor to have significant effect on surface roughness. At the end of this study, optimization was made by suggesting the most suitable sets of parameter settings to produce minimum surface roughness. Suggestion on parameter settings to obtain minimum surface roughness made.

scholarly journals Optimization of Process Control Parameters for WEDM of Al-LM25/Fly Ash/B4C Hybrid Composites Using Evolutionary Algorithms: A Comparative Study

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1105
Author(s):  
Nagarajan Lenin ◽  
Mahalingam Sivakumar ◽  
Gurusamy Selvakumar ◽  
Devaraj Rajamani ◽  
Vinothkumar Sivalingam ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Fly Ash ◽  
Process Control ◽  
Hybrid Composites ◽  
Removal Rate ◽  
Machining Parameters ◽  
Control Parameters ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process

In this work, wire electrical discharge machining (WEDM) of aluminum (LM25) reinforced with fly ash and boron carbide (B4C) hybrid composites was performed to investigate the influence of reinforcement wt% and machining parameters on the performance characteristics. The hybrid composite specimens were fabricated through the stir casting process by varying the wt% of reinforcements from 3 to 9. In the machinability studies, the WEDM process control parameters such as gap voltage, pulse-on time, pulse-off time, and wire feed were varied to analyze their effects on machining performance including volume removal rate and surface roughness. The WEDM experiments were planned and conducted through the L27 orthogonal array approach of the Taguchi methodology, and the corresponding volume removal rate and surface roughness were measured. In addition, the multi-parametric ANOVA was performed to examine the statistical significance of the process control parameters on the volume removal rate and surface roughness. Furthermore, the spatial distribution of the parameter values for both the responses were statistically analyzed to confirm the selection of the range of the process control parameters. Finally, the quadratic multiple linear regression models (MLRMs) were formulated based on the correlation between the process control parameters and output responses. The Grass–Hooper Optimization (GHO) algorithm was proposed in this work to identify the optimal process control parameters through the MLRMs, in light of simultaneously maximizing the volume removal rate and minimizing the surface roughness. The effectiveness of the proposed GHO algorithm was tested against the results of the particle swarm optimization and moth-flame optimization algorithms. From the results, it was identified that the GHO algorithm outperformed the others in terms of maximizing volume removal rate and minimizing the surface roughness values. Furthermore, the confirmation experiment was also carried out to validate the optimal combination of process control parameters obtained through the GHO algorithm.

scholarly journals Soft Computing Techniques for the Prediction of Hybrid Composites

2020 ◽  
Vol 9 (4) ◽  
pp. 2717-2720
Keyword(s):  
Surface Roughness ◽  
Soft Computing ◽  
Hybrid Composites ◽  
End Milling ◽  
Input Parameter ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Inference System ◽  
Soft Computing Techniques

Soft computing techniques such as Artificial Neural Networks and Fuzzy logic are widely used in application of manufacturing technology. Surface roughness plays a vital role for quality of the product using machining parameters. Soft computing techniques are applied to predict the surface roughness in an economical manner. In this paper, prediction of surface roughness is evaluated using ANFIS [Adaptive Neuro-Fuzzy Inference System] methodology for the cutting parameters of end-milling process for machining the halloysite nanotubes (HNTs) with aluminium reinforced epoxy hybrid composite material. Experimental datas are used to analyse the relationship between the input parameter such as depth of cut (d), cutting speed (S), feed-rate (f) and output parameters as surface roughness. Datas are classified into training and testing with different types of membership functions. The observed results accurately predict the output which was not used in training and it is almost very close to the actual output obtained in the experimental work. Moreover it was found that gbellmf is helpful for better prediction with minimum error.

scholarly journals The Optimization of Machining Parameters on Surface Roughness for AISI D3 Steel

2021 ◽  
Vol 1874 (1) ◽  
pp. 012063
Author(s):  
Khair Khalil ◽  
A. Mohd ◽  
C. O. C. Mohamad ◽  
Y. Faizul ◽  
S Zainal Ariffin
Keyword(s):  
Surface Roughness ◽  
Machining Parameters ◽  
Aisi D3 Steel
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