scholarly journals A Cyber-Physical System for Surface Roughness Monitoring in End-Milling

2019 ◽  
Author(s):  
Uroš Župerl ◽  
Franci Čuš
Keyword(s):  
Surface Roughness ◽  
Process Control ◽  
Neural Control ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Inference System ◽  
Machining System ◽  
Chip Size ◽  
Roughness Control

A cyber-psychical machining system (CPMS) is developed to realize smart end-milling process monitoring. The CPMS provides a novel way for controlling the cutting chip size and monitoring the surface roughness in milling processes through Internet of Things (IoT) applications. The two level CPMS is realized by linking the IoT machining platform for process control to the machine tool with integrated visual system (VS). The VS is employed to acquire the signals of the cutting chip size during the machining of difficult to cut materials. The machining platform performs instant chip size and surface roughness control based on advanced signal processing, edge computing, modeling and cognitive corrective process control acting. A cognitive neural control system (CNCS) is employed to control the chip size by modifying the machining parameters and consequently maintaining surface roughness constant. An adaptive neural inference system (ANFIS) is applied to precisely model and in-process predict the surface roughness. Machining tests conducted using the proposed CPMS indicate that the cutting chip size and consequently the produced surface roughness are well maintained when the cutting-depth profile of a workpiece is varying step-wise or continuously.

scholarly journals Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Tools ◽  
Computing Methods ◽  
Milling Process ◽  
Dry Cutting ◽  
Inference System ◽  
Cutting Processes ◽  
Adaptive Neurofuzzy Inference System

Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end millingTi6Al4Valloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing ofANFISmodels, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability ofANFISin modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

End-Milling Machinability of Inconel 718

1996 ◽  
Vol 210 (1) ◽  
pp. 11-23 ◽  
Author(s):  
M Alauddin ◽  
M A El Baradie ◽  
M S J Hashmi
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Inconel 718 ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Life Tests ◽  
Life Values ◽  
Published Research ◽  
Carbide Inserts

Most published research works on machining Inconel 718 have been mainly concerned with turning, while the milling process has received little attention due to the complexity of the process. In this paper a series of end-milling experiments of Inconel 718 has been carried out in order to: (a) optimize cutting variables, (b) investigate tool life values and relationships and (c) investigate surface roughness. The machining parameters have been optimized by measuring cutting forces. Tool life tests have been carried out using carbide inserts and the surface roughness has been analysed.

scholarly journals Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Abdel Badie Sharkawy
Keyword(s):  
Surface Roughness ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Validation Data ◽  
Genetic Tuning ◽  
Inference Systems ◽  
End Milling Process

A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insureperfectoptimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.

Investigation on Effect of Machining Parameters of End Milling on Surface Finish and Temperature Using Taguchi Technique

2016 ◽  
Vol 16 (4) ◽  
pp. 255-261
Author(s):  
Tamiloli N ◽  
Venkatesan J
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
End Milling ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Parameters ◽  
Cutting Zone ◽  
Machining Optimization ◽  
Quality Surface

AbstractMachining of alloy materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the surface roughness and cutting temperatures becomes utmost important to avoid any damage to the quality surface. This paper presents the development of new models and optimizing these models of machining parameters to minimize the surface roughness and cutting temperature in end milling process by Taguchi method with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and cutting temperature. Due to the complexity of this machining optimization problem, a single objective Taguchi method has been applied to resolve the problem, and the results have been analyzed.

Experimental Study on Milling Hardened SKD Steel Using Micro CBN Ball-End Mills

2010 ◽  
Vol 126-128 ◽  
pp. 773-778
Author(s):  
Yung Tien Liu ◽  
Neng Hsin Chiu ◽  
Yen Chun Lin ◽  
Chih Liang Lai ◽  
Yu Fu Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Milling Process ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Machining Accuracy ◽  
Machining Parameters ◽  
Machined Surface ◽  
Performance Function ◽  
End Mills

Micro ball-end milling process features the ability of machining complex surfaces, precision machining accuracy, and excellent machined surface roughness. However, because the diameter of a micro milling tool is very small, a rapid progress of tool wear or even tool breakage usually happens when machining a high-strength hardened mold steel using improper machining parameters. As a result, the machining cost would rise due to the quality defect in machined workpiece. In this study, to investigate how the machining parameters affect the cutting behaviors, a series of experiments using micro CBN ball-end mills with a diameter of 0.5 mm were performed to cut the SKD11 mold steel with hardness of HRC 61. The machining parameters are selected as the feeding speed (f) being 840, 960 and 1,080 mm/min, depth of cut (ap) being 30, 45, 60 μm, and spindle speed (vs) being fixed as 30,000 rpm. According to the experimental results, the measured three-axis cutting forces, flank wears, and surface roughness of machined workpiece are highly related to the cutting length. It is expected that the measured results can be used to construct a performance function of a micro ball-end tool. With referring to the performance function, the tool life can be well expected, and thus a progress in machining efficiency without tool failure can be achieved.

scholarly journals Comparative study on Mamdani-Type Fuzzy Inference Systems and Regression model for end milling process using AA 6082T6

2021 ◽  
Vol 2070 (1) ◽  
pp. 012209
Author(s):  
Natarajan Tamiloli ◽  
Velpuri Venkat Raman
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Fuzzy Inference ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Product Profile ◽  
Inference Systems ◽  
Traditional Approaches ◽  
End Milling Process

Abstract The goal of this observes is to take a look at the effect of machining parameters on surface roughness in end milling. An incipient technique in modelling surface roughness that makes use of synthetic perspicacity implements is defined in this paper. This paper fixates on growing empirical models utilizing fuzzy logic and regression analysis. The values of surface roughness presaged with the aid of using those fashions are then in comparison. The effects confirmed that the proposed gadget can considerably boom the precision of the product profile whilst in comparison to the traditional approaches, like regression analysis. The effects designate that the regression modelling method may be effectively applied for the presage of surface roughness in dry machining.

Prediction of workpiece surface roughness using soft computing

2008 ◽  
Vol 222 (10) ◽  
pp. 1221-1232 ◽  
Author(s):  
B Samanta ◽  
W Erevelles ◽  
Y Omurtag
Keyword(s):  
Surface Roughness ◽  
Soft Computing ◽  
Prediction Models ◽  
End Milling ◽  
Depth Of Cut ◽  
Model Parameters ◽  
Machining Parameters ◽  
Validation Data ◽  
Inference System ◽  
Workpiece Surface

A study is presented to model surface roughness in end-milling using soft computing (SC) or computational intelligence (CI) techniques. The techniques include the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). ANFIS combines the learning capability of ANN and the effective handling of imprecise information in fuzzy logic. Prediction models based on multivariate regression analysis (MRA) are also presented for comparison. The machining parameters, namely, the spindle speed, feed rate, and depth of cut, were used as inputs to model the workpiece surface roughness. The model parameters were tuned using the training data maximizing the modelling accuracy. The trained models were tested using the set of validation data. The effects of different machining parameters, number, and type of model parameters on the prediction accuracy were studied. The procedure is illustrated using the experimental data of end-milling 6061 aluminium alloy. Although statistically all three models predicted roughness with satisfactory goodness of fit, the test performance of ANFIS was better than ANN and MRA. In comparison with MRA, the performance of ANN was better in training but similar in test. The results show the effectiveness of CI techniques in modelling surface roughness.

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.

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