Effect of machining parameters for surface finish and material removal rate of Al-4032/SiC composite during end milling using TGRA and ANN

2021 ◽  
Author(s):  
Pardeep Saini ◽  
Pradeep K. Singh ◽  
Deepak Kumar
Keyword(s):  
Material Removal Rate ◽  
Surface Finish ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Machining Parameters

Optimization of Cutting Parameters in Machining GFRP Using End Mill Cutter through DoE and Verification through Genetic Algorithm

2017 ◽  
Vol 867 ◽  
pp. 134-147
Author(s):  
Shanmugasundaram Sankar ◽  
V. Kumaresan Manivarma
Keyword(s):  
Genetic Algorithm ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Parameters ◽  
End Mill ◽  
Mill Cutter ◽  
Maximum Material Removal Rate

This article discusses optimization of critical parameters such as cutting speed, feed, depth of cut and method of machining while machining Glass Fiber Reinforced Plastic (GFRP) in vertical machining center using standard end mill cutter made up of High Speed Steel (HSS) for lesser cutting load, maximum material removal rate for better surface finish and dimensional accuracy through design of experiments. In composite material machining, surface finish is the critical deciding factor in determining surface quality. In this study, as per Taguchi’s L9 orthogonal array, predictable and unpredictable parts are followed to evaluate the consequence of cutting parameters on the machined component. The study includes surface roughness measurement using surface profilometer continued by physical measurement of machined pocket dimension. The experimental results, suggest suitable machining parameters in order to achieve the above target goal. In addition, C++ program is developed to cross check the most favorable machining parameters for maximum material removal rate using genetic algorithm. It is inferred from the study that the genetic algorithm results coincides very closely with the result given by the method of design of experiments.

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.

A Mechanistic Approach to the Prediction of Material Removal Rates in Rotary Ultrasonic Machining

1995 ◽  
Vol 117 (2) ◽  
pp. 142-151 ◽  
Author(s):  
Z. J. Pei ◽  
D. Prabhakar ◽  
P. M. Ferreira ◽  
M. Haselkorn
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Diamond Particle ◽  
Machining Parameters ◽  
Ultrasonic Machining ◽  
Rotary Ultrasonic Machining ◽  
Stabilized Zirconia ◽  
Mechanistic Approach ◽  
Wide Range

An approach to modeling the material removal rate (MRR) during rotary ultrasonic machining (RUM) of ceramics is proposed and applied to predicting the MRR for the case of magnesia stabilized zirconia. The model, a first attempt at predicting the MRR in RUM, is based on the assumption that brittle fracture is the primary mechanism of material removal. To justify this assumption, a model parameter (which models the ratio of the fractured volume to the indented volume of a single diamond particle) is shown to be invariant for most machining conditions. The model is mechanistic in the sense that this parameter can be observed experimentally from a few experiments for a particular material and then used in prediction of MRR over a wide range of process parameters. This is demonstrated for magnesia stabilized zirconia, where very good predictions are obtained using an estimate of this single parameter. On the basis of this model, relations between the material removal rate and the controllable machining parameters are deduced. These relationships agree well with the trends observed by experimental observations made by other investigators.

Process Design and Optimization of end Milling Parameters of Al 7075 Metal Matrix Composite

2021 ◽  
pp. 929-937
Author(s):  
D. S. Sai Ravi Kiran ◽  
Alavilli Sai Apparao ◽  
Vempala GowriSankar ◽  
Shaik Faheem ◽  
Sheik Abdul Mateen ◽  
...  
Keyword(s):  
Tool Wear ◽  
Material Removal Rate ◽  
Metal Matrix ◽  
Material Removal ◽  
Hybrid Composites ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Weight Percentage

This paper investigates the machinability characteristics of end milling operation to yield minimum tool wear with the maximum material removal rate using RSM. Twenty-seven experimental runs based on Box-Behnken Design of Response Surface Methodology (RSM) were performed by varying the parameters of spindle speed, feed and depth of cut in different weight percentage of reinforcements such as Silicon Carbide (SiC-5%, 10%,15%) and Alumina (Al2O3-5%) in alluminium 7075 metal matrix. Grey relational analysis was used to solve the multi-response optimization problem by changing the weightages for different responses as per the process requirements of quality or productivity. Optimal parameter settings obtained were verified through confirmatory experiments. Analysis of variance was performed to obtain the contribution of each parameter on the machinability characteristics. The result shows that spindle speed and weight percentage of SiC are the most significant factors which affect the machinability characteristics of hybrid composites. An appropriate selection of the input parameters such as spindle speed of 1000 rpm, feed of 0.02 mm/rev, depth of cut of 1 mm and 5% of SiC produce best tool wear outcome and a spindle speed of 1838 rpm, feed of 0.04 mm/rev, depth of cut of 1.81 mm and 6.81 % of SiC for material removal rate.

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

Modeling of Material Removal Mechanism in Micro Electric Discharge Milling of Ti-6Al-4V

2014 ◽  
Vol 592-594 ◽  
pp. 516-520 ◽  
Author(s):  
Basil Kuriachen ◽  
Jose Mathew
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Milling Process ◽  
Work Piece ◽  
Machining Parameters ◽  
Removal Mechanism ◽  
Material Removal Mechanism ◽  
Electric Discharge Milling

Micro EDM milling process is accruing a lot of importance in micro fabrication of difficult to machine materials. Any complex shape can be generated with the help of the controlled cylindrical tool in the pre determined path. Due to the complex material removal mechanism on the tool and the work piece, a detailed parametric study is required. In this study, the influence of various process parameters on material removal mechanism is investigated. Experiments were planned as per Response Surface Methodology (RSM) – Box Behnken design and performed under different cutting conditions of gap voltage, capacitance, electrode rotation speed and feed rate. Analysis of variance (ANOVA) was employed to identify the level of importance of machining parameters on the material removal rate. Maximum material removal rate was obtained at Voltage (115V), Capacitance (0.4μF), Electrode rotational Speed (1000rpm), and Feed rate (18mm/min). In addition, a mathematical model is created to predict the material removal

Modelling and Optimization of End Milling Process Using TLBO and TOPSIS Algorithm

2017 ◽  
pp. 54-81 ◽  
Author(s):  
Atul Tiwari ◽  
Mohan Kumar Pradhan
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Metal Cutting ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Depth Of Cut ◽  
Multi Objective ◽  
Order Preference

To assure desire quality of machined products at minimum machining costs and maximum material removal rate, it is very important to select optimum parameters when metal cutting machine tool are used. Minimum Surface Roughness (Ra) is commonly desirable for the component; however Material Removal Rate (MRR) should be maximized. This chapter presents an approach for determination of the best cutting parameters precede to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology with Multi-Objective Technique for Order Preference by Similarity to Ideal Solution and Teaching and learning based optimization algorithm in face milling of Al-6061 alloy. 30 experiments have been conducted based on RSM with 4 parameters, namely Speed, Feed, Depth of Cut and Coolant Speed and three levels each. ANOVA is performed to find the most influential input parameters for both MRR and Ra. Later the multi-objective attribution selection method TOPSIS and multi objective optimization method TLBO is used to optimize the responses.

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