Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

2017 ◽  
Vol 232 (20) ◽  
pp. 3573-3594 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Genetic Algorithm ◽  
Empirical Mode Decomposition ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Noise Analysis ◽  
Cutting Parameters ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Tool Chatter

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.

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.

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.

Stable cutting zone prediction in computer numerical control turning based on empirical mode decomposition and artificial neural network approach

2018 ◽  
Vol 41 (1) ◽  
pp. 193-209 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Quality ◽  
Empirical Mode Decomposition ◽  
Cutting Parameters ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Tool Chatter

Stable cutting zone prediction is the key requirement for retaining high-productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for abrupt change in surface quality and productivity. In this research work, an optimum safe cutting zone has been predicted by analyzing the tool chatter so that higher productivity can be achieved. Initially, chatter signals have been recorded by performing experiments at different combinations of cutting parameters on computer numerical control trainer lathe. Further, these recorded signals have been preprocessed by empirical mode decomposition technique, followed by the selection of dominating intrinsic mode functions using Fourier transform. The preprocessed signals have been used to evaluate a new output parameter, that is, chatter index (CI). Artificial neural network (ANN) based on the feedforward backpropagation network has been proposed for predicting tool chatter in turning process. The input machining parameters considered are depth of cut, feed rate and cutting speed. It has also been deduced that from available different transfer functions, the Hyperbolic Tangent transfer function in ANN is best suitable to predict tool chatter severity in turning operation. Moreover, the safe cutting zone has been assessed by evaluating the dependency of CI on cutting parameters. Finally, more experiments have been conducted to validate the obtained cutting zone.

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.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

Interaction Between Wire and Ingot in Wiresaw Slicing

1999 ◽  
Author(s):  
Fuqian Yang ◽  
J. C. M. Li ◽  
Imin Kao
Keyword(s):  
Finite Element ◽  
Contact Zone ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Bending Stiffness ◽  
Mechanical Interaction ◽  
Wire Tension ◽  
Cutting Zone ◽  
The Wire

Abstract The deformation of the wire in the wiresaw slicing process was studied by considering directly the mechanical interaction between the wire and the ingot. The wire tension on the upstream is larger than on the downstream due to the friction force between the wire and the ingot. The tension difference across the cutting zone increases with friction and the span of the contact zone. The pressure in the contact zone increases from the entrance to the exit if the wire bending stiffness is ignored. The finite element results show that the wire bending stiffness plays an important role in the wire deformation. Higher wire bending stiffness (larger wire size) generates higher force acting onto the ingot for the same amount of wire deformation, which will leads to higher material removal rate and kerf loss. While larger wire span will reduce the force acting onto the ingot for a given ingot displacement in the direction perpendicular to the wire.

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

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.

Research on Microscopic Grain-Workpiece Interaction in Grinding through Micro-Cutting Simulation, Part 2: Factorial Study

2009 ◽  
Vol 76-78 ◽  
pp. 15-20 ◽  
Author(s):  
Lan Yan ◽  
Xue Kun Li ◽  
Feng Jiang ◽  
Zhi Xiong Zhou ◽  
Yi Ming Rong
Keyword(s):  
Cutting Force ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Shear Angle ◽  
Depth Of Cut ◽  
Specific Cutting Force ◽  
Single Grain

The grinding process can be considered as micro-cutting processes with irregular abrasive grains on the surface of grinding wheel. Single grain cutting simulation of AISI D2 steel with a wide range of cutting parameters is carried out with AdvantEdgeTM. The effect of cutting parameters on cutting force, chip formation, material removal rate, and derived parameters such as the specific cutting force, critical depth of cut and shear angle is analyzed. The formation of chip, side burr and side flow is observed in the cutting zone. Material removal rate increases with the increase of depth of cut and cutting speed. Specific cutting force decreases with the increase of depth of cut resulting in size effect. The shear angle increases as the depth of cut and cutting speed increase. This factorial analysis of single grain cutting is adopted to facilitate the calculation of force consumption for each single abrasive grain in the grinding zone.

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