Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

2020 ◽  
Vol 841 ◽  
pp. 363-368
Author(s):  
Zvikomborero Hweju ◽  
Khaled Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Surface Roughness Prediction ◽  
Peak Rate ◽  
Signal Parameters ◽  
Roughness Prediction ◽  
Ae Signal

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.

Development of Surface Roughness Prediction by Utilizing Dynamic Cutting Force Ratio

2014 ◽  
Vol 490-491 ◽  
pp. 207-212 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Kanyakarn Samanmit ◽  
Suthas Ratanakuakangwan
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Surface Roughness Prediction ◽  
Dynamic Cutting Force ◽  
Force Ratio ◽  
Roughness Prediction ◽  
Cnc Turning Process ◽  
Coated Carbide

This paper presents the development of the in-process surface roughness prediction in the CNC turning process of the plain carbon steel with the coated carbide tool by utilizing the dynamic cutting force ratio. The dynamic cutting forces are measured to analyze the relation between the surface roughness and the cutting conditions. The proposed surface roughness model is developed based on the experimentally obtained results by employing the exponential function with six factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the dynamic cutting force ratio. The dynamic cutting force ratio can be calculated and obtained by taking the ratio of the corresponding time records of the area of the dynamic feed force to that of the dynamic main force. The relation between the dynamic cutting force ratio and the surface roughness can be proved by the obtained frequency of them in frequency domain which are the same frequency. The proposed model has been proved by the new cutting tests with the high accuracy of 91.04% by utilizing the dynamic cutting force ratio.

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.

Surface Roughness Prediction Using Numerical Scheme and Feedback Control

2015 ◽  
Author(s):  
Michael K. O. Ayomoh ◽  
Khaled A. Abou-El-Hossein ◽  
Sameh F. M. Ghobashi
Keyword(s):  
Surface Roughness ◽  
Feedback Control ◽  
High Carbon Steel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Experimental Sample ◽  
Surface Roughness Prediction ◽  
Difference Analysis ◽  
Roughness Prediction

This paper proposes a numerical modelling scheme for surface roughness prediction. The approach is premised on the use of 3D difference analysis method enhanced with the use of feedback control loop where a set of adaptive weights are generated. The surface roughness values utilized in this paper were adapted from [1]. Their experiments were carried out using S55C high carbon steel. A comparison was further carried out between the proposed technique and those utilized in [1]. The experimental design has three cutting parameters namely: depth of cut, feed rate and cutting speed with twenty-seven experimental sample-space. The simulation trials conducted using Matlab software is of two sub-classes namely: prediction of the surface roughness readings for the non-boundary cutting combinations (NBCC) with the aid of the known surface roughness readings of the boundary cutting combinations (BCC). The following simulation involved the use of the predicted outputs from the NBCC to recover the surface roughness readings for the boundary cutting combinations (BCC). The simulation trial for the NBCC attained a state of total stability in the 7th iteration i.e. a point where the actual and desired roughness readings are equal such that error is minimized to zero by using a set of dynamic weights generated in every following simulation trial. A comparative study among the three methods showed that the proposed difference analysis technique with adaptive weight from feedback control produced a much accurate output as against the abductive and regression analysis techniques presented in [1].

Surface Roughness Prediction Model Based on AE in Grinding

2014 ◽  
Vol 701-702 ◽  
pp. 150-153
Author(s):  
Ning Ding ◽  
Wen Ze Yu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Model ◽  
Fuzzy Neural Network ◽  
Surface Roughness Prediction ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Roughness Prediction ◽  
Ae Signals

Based on the theory of roughness during grinding and the theory of fuzzy-neural network, a new intelligent prediction model is developed in this paper. The inputs for the model are the grinding parameters and the AE signals. Beijing Shenghua SAEU2S system was used to collect and analyze the signals of acoustic emission. The experiment was conducted, and the results verify the feasibility of the proposed model.

scholarly journals Surface Roughness Prediction Method of Titanium Alloy Milling based on CDH Platform

2021 ◽  
Author(s):  
Liu Xianli ◽  
Sun Yanming ◽  
Yue Caixu ◽  
Wei Xudong ◽  
Sun Qingzhen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Prediction Accuracy ◽  
Search Strategy ◽  
Prediction Method ◽  
Pattern Search ◽  
Apache Hadoop ◽  
Surface Roughness Prediction ◽  
Svm Algorithm ◽  
Roughness Prediction

Abstract Generally, off-line methods are used for surface roughness prediction of titanium alloy milling. However, studies show that these methods have poor prediction accuracy. In order to resolve this shortcoming, a prediction method based on Cloudera's Distribution Including Apache Hadoop (CDH) platform is proposed in the present study. In this regard, data analysis and process platform is designed based on the CDH, which can upload, calculate and store data in real-time. Then this platform is combined with the Harris hawk optimization (HHO) algorithm and pattern search strategy, and an improved hybrid optimization (IHHO) method is proposed accordingly. Then this method is applied to optimize the SVM algorithm and predict the surface roughness in the CDH platform. The obtained results show that the prediction accuracy of IHHO method reaches 95%, which is higher than the conventional methods of SVM, BAT-SVM, GWO-SVM and WOA-SVM.

scholarly journals Surface roughness prediction in milling based on tool displacements

2011 ◽  
Vol 13 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Jean Philippe Costes ◽  
Vincent Moreau
Keyword(s):  
Surface Roughness ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy

2021 ◽  
Author(s):  
XueTao Wei ◽  
caixue yue ◽  
DeSheng Hu ◽  
XianLi Liu ◽  
YunPeng Ding ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Finite Element ◽  
Prediction Model ◽  
Simulation Model ◽  
Finite Element Simulation ◽  
Simulation Software ◽  
Surface Roughness Prediction ◽  
Contour Shape ◽  
Element Simulation ◽  
Roughness Prediction

Abstract The processed surface contour shape is extracted with the finite element simulation software, and the difference value of contour shape change is used as the parameters of balancing surface roughness to construct the infinitesimal element cutting finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the central composite test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the finite element simulation model and surface roughness prediction model are verified and analyzed through experiment. The research results show that, compared with the experiment results, the maximum error of finite element simulation model and surface roughness prediction model is 30.9% and12.3%, respectively. So, the model in this paper is accurate and will provide the theoretical basis for optimization study of auxiliary milling process of supersonic vibration.

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