scholarly journals Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 4055
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Predictive Accuracy ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Support Vector ◽  
Process Data ◽  
Cutting Force Prediction ◽  
Artificial Neural Network Ann ◽  
Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

Analysis of Slope Stability Based on Gaussian Process Regression

2012 ◽  
Vol 170-173 ◽  
pp. 1330-1333 ◽  
Author(s):  
Yan Zhang ◽  
Guo Shao Su ◽  
Liu Bin Yan
Keyword(s):  
Slope Stability ◽  
Gaussian Process ◽  
Stability Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Support Vector ◽  
Test Study ◽  
Computation Process ◽  
Artificial Neural Network Ann ◽  
Better Than

Slope stability estimation is an engineering problem that involves several parameters. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used in slope stability problem. However, there are some open issues for above-mentioned methods, which are very hard to overcome. For this reason, Gaussian Process Regression (GPR) which has a theoretical framework for obtaining the optimum hyperparameters self-adaptively has been used in slope stability problem. Without complicated mechanics computation process, through learning the empirical knowledge coming from real engineering, the complicated nonlinear mapping relationship between slope stability and its influencing factors was established easily using GPR. The results of test study indicate that the method is feasible, effective and simple to implement for slope stability evaluation. The results are better than previously published paper of ANN and SVM.

An Improved and Low-dimensional Fingerprint-based Localization Method in Collocated Massive MIMO-OFDM Systems

2021 ◽  
Author(s):  
Seyedeh Samira Moosavi ◽  
Paul Fortier
Keyword(s):  
Computational Complexity ◽  
Gaussian Process ◽  
Massive Mimo ◽  
High Reliability ◽  
Gaussian Process Regression ◽  
Location Estimation ◽  
Ofdm Systems ◽  
Process Data ◽  
Localization Method ◽  
Low Dimensional

Abstract Localization has drawn significant attention in 5G due to the fast-growing demand for location-based service (LBS). Massive multiple-input multiple-output (M-MIMO) has been introduced in 5G as a powerful technology due to its evident potentials for communication performance enhancement and localization in complicated environments. Fingerprint-based (FP) localization are promising methods for rich scattering environments thanks to their high reliability and accuracy. The Gaussian process regression (GPR) method could be used as an FP-based localization method to facilitate localization and provide high accuracy. However, this method has high computational complexity, especially in large-scale environments. In this study, we propose an improved and low-dimensional FP-based localization method in collocated massive MIMO orthogonal frequency division multiplexing (OFDM) systems using principal component analysis (PCA), the affinity propagation clustering (APC) algorithm, and Gaussian process regression (GPR) to estimate the user's location. Fingerprints are first extracted based on instantaneous channel state information (CSI) by taking full advantage of the high-resolution angle and delay domains. First, PCA is used to pre-process data and reduce the feature dimension. Then, the training fingerprints are clustered using the APC algorithm to increase prediction accuracy and reduce computation complexity. Finally, each cluster's data distribution is accurately modelled using GPR to provide support for further localization. Simulation results reveal that the proposed method improves localization performance significantly by reducing the location estimation error. Additionally, it reduces the matching complexity and computational complexity.

scholarly journals Modified Mechanistic Model Based on Gaussian Process Adjusting Technique for Cutting Force Prediction in Micro-End Milling

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xiaoping Liao ◽  
Zhenkun Zhang ◽  
Kai Chen ◽  
Kang Li ◽  
Junyan Ma ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Mechanistic Model ◽  
Machining Process ◽  
Mechanistic Models ◽  
Machining Processes ◽  
Model Based ◽  
Micro End Milling

Micro-end milling is in common use of machining micro- and mesoscale products and is superior to other micro-machining processes in the manufacture of complex structures. Cutting force is the most direct factor reflecting the processing state, the change of which is related to the workpiece surface quality, tool wear and machine vibration, and so on, which indicates that it is important to analyze and predict cutting forces during machining process. In such problems, mechanistic models are frequently used for predicting machining forces and studying the effects of various process variables. However, these mechanistic models are derived based on various engineering assumptions and approximations (such as the slip-line field theory). As a result, the mechanistic models are generally less accurate. To accurately predict cutting forces, the paper proposes two modified mechanistic models, modified mechanistic models I and II. The modified mechanistic models are the integration of mathematical model based on Gaussian process (GP) adjustment model and mechanical model. Two different models have been validated on micro-end-milling experimental measurement. The mean absolute percentage errors of models I and II are 7.76% and 6.73%, respectively, while the original mechanistic model’s is 15.14%. It is obvious that the modified models are in better agreement with experiment. And model II performs better between the two modified mechanistic models.

scholarly journals Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Martin Bogdan ◽  
Dominik Brugger ◽  
Wolfgang Rosenstiel ◽  
Bernd Speiser
Keyword(s):  
Gaussian Process ◽  
Diffusion Coefficients ◽  
Gaussian Process Regression ◽  
Support Vector

scholarly journals Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework

2021 ◽  
Vol 10 (4) ◽  
pp. 218-227
Author(s):  
Jitendra Khatti ◽  
◽  
Dr. Kamaldeep Singh Grover ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Clay Content ◽  
Geotechnical Properties ◽  
Support Vector ◽  
Analysis Tool ◽  
Artificial Neural ◽  
Properties Of Soil

The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, sand, silt, and clay content. The correlation coefficient is calculated for pair of soil datasets. The correlation shows that sand, silt, and clay content play a vital role in predicting soil's liquid limit and plasticity index. The performance of constructed AI models is compared to determine the optimum performance models. The limited datasets of soil are used in this study. Therefore, artificial neural networks and relevance vector machines could not perform well. Based on the performance of AI models, the Gaussian process regression outperformed the RA, SVM, ANNs, and RVM AI technologies. Hence, the GPR AI approach can predict the geotechnical properties of soil by gravel, sand, silt, and clay content. The Monte-Carlo global sensitivity analysis is also performed, and it is observed that the prediction of geotechnical properties of soil is affected by sand and clay content

scholarly journals PENGARUH GAYA POTONG PEMBUBUTAN TERHADAP KEAUSAN PAHAT KARBIDA COATED PADA BENDA KERJA BAJA AISI 4340

POROS ◽  
2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Rosehan Rosehan Rosehan
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Alloy Steel ◽  
Tool Life ◽  
Cutting Tools ◽  
Machining Process ◽  
Maximum Value ◽  
Trial Analysis ◽  
Steel Aisi ◽  
Aisi 4340

Cutting force and tool life is the important data in planning a machining process. The research is in order to describe about the influence of the cutting force to the tool wear on carbide coated cutting tools used the turning process of an alloy steel of AISI 4340. The research was conducted by observing the growth of tool wear on minutes 4.5, 9, 13.5, 18, 22.5 with the maximum value VB 0.3 mm, at the same time, the condition of other cutting such as the motion while the cutting, the depth and speed of the cutting movement was constant. The purpose of this experiment is to examine scientifically the influence cutting force to the growth of tool wear on carbide coated while the cutting process of alloy steel AISI 4340. The graphical method was used for the trial analysis, to see the cutting force comparison with the decrease of tool life of the carbide coated, and the correlation of the cutting movement with the cutting force. The mechanism decrease showed the adhesion decrease.

scholarly journals Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods

2020 ◽  
Vol 48 ◽  
pp. 1000-1008
Author(s):  
Maryam Hashemitaheri ◽  
Sai Manish Reddy Mekarthy ◽  
Harish Cherukuri
Keyword(s):  
Gaussian Process ◽  
Cutting Forces ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Regression Methods ◽  
Orthogonal Machining
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