Tool wear parameters identification in precision milling using a hybrid model combining cutting forces analytical model and Gaussian process regression method*

2021 ◽  
Author(s):  
Shengqiang Zhao ◽  
Lin Zhou ◽  
Hao Sun ◽  
Fangyu Peng ◽  
Rong Yan ◽  
...  
Keyword(s):  
Tool Wear ◽  
Gaussian Process ◽  
Analytical Model ◽  
Hybrid Model ◽  
Cutting Forces ◽  
Gaussian Process Regression ◽  
Regression Method ◽  
Parameters Identification ◽  
Model Combining

TRAJECTORY PLANNING OF 6-DOF MANIPULATOR BASED ON GAUSSIAN PROCESS REGRESSION METHOD

2020 ◽  
Vol 35 (3) ◽  
Author(s):  
Jinzhi Zhao ◽  
Shizhao Wang ◽  
Aibing Jiang ◽  
Jin Xiao ◽  
Bin Wang
Keyword(s):  
Gaussian Process ◽  
Trajectory Planning ◽  
Gaussian Process Regression ◽  
Regression Method

scholarly journals Supply Chain Coordination under Trade Credit and Quantity Discount with Sales Effort Effects

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zhihong Wang ◽  
Shaofeng Liu
Keyword(s):  
Supply Chain ◽  
Analytical Model ◽  
Hybrid Model ◽  
Trade Credit ◽  
Supply Chain Coordination ◽  
Market Demand ◽  
Quantity Discount ◽  
Model Combining ◽  
Effort Effect

The purpose of this paper is to investigate the role of trade credit and quantity discount in supply chain coordination when the sales effort effect on market demand is considered. In this paper, we consider a two-echelon supply chain consisting of a single retailer ordering a single product from a single manufacturer. Market demand is stochastic and is influenced by retailer sales effort. We formulate an analytical model based on a single trade credit and find that the single trade credit cannot achieve the perfect coordination of the supply chain. Then, we develop a hybrid quantitative analytical model for supply chain coordination by coherently integrating incentives of trade credit and quantity discount with sales effort effects. The results demonstrate that, providing that the discount rate satisfies certain conditions, the proposed hybrid model combining trade credit and quantity discount will be able to effectively coordinate the supply chain by motivating retailers to exert their sales effort and increase product order quantity. Furthermore, the hybrid quantitative analytical model can provide great flexibility in coordinating the supply chain to achieve an optimal situation through the adjustment of relevant parameters to resolve conflict of interests from different supply chain members. Numerical examples are provided to demonstrate the effectiveness of the hybrid model.

scholarly journals Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2508 ◽  
Author(s):  
Guolong Zhang ◽  
Ping Wang ◽  
Haibing Chen ◽  
Lan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gaussian Process ◽  
Hybrid Model ◽  
Nearest Neighbor ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Training Dataset ◽  
K Nearest Neighbor ◽  
Positive Effects

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

Gaussian process regression for tool wear prediction

2018 ◽  
Vol 104 ◽  
pp. 556-574 ◽  
Author(s):  
Dongdong Kong ◽  
Yongjie Chen ◽  
Ning Li
Keyword(s):  
Tool Wear ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Wear Prediction ◽  
Tool Wear Prediction

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

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