Machining Efficiency Model Based on the Multiple Regression Theory in Turning Glass Ceramics

2012 ◽  
Vol 201-202 ◽  
pp. 1092-1095
Author(s):  
Lian Jie Ma ◽  
Ai Bing Yu ◽  
Ya Dong Gong
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Removal Rate ◽  
High Reliability ◽  
Cutting Speed ◽  
Glass Ceramics ◽  
Least Square Method ◽  
Least Square ◽  
Feed Speed ◽  
Tools Wear

The materials removal rate (V/VB) was selected to be objective function. It is comprehensive parameter about materials and tools wear. Through turning glass ceramics experimentation, the materials removal influence of cutting speed, cutting depth and feed speed were study. Based on least square method, the multiple regression prediction model of materials removal rate was built. And the model was tested. It was applied to predictive and control. The results indicated: this model was well to express materials removal law in turning glass ceramics. The multiple regression prediction model is high remarkable. The prediction value was coincident with measure value. This model is high reliability. So, expect materials removal rate can been obtained by this model, and choosing the technological parameter can been guided.

Cutting Force Empirical Model of High-Speed Precision Hard Cutting GCr15

2009 ◽  
Vol 69-70 ◽  
pp. 301-305
Author(s):  
Jing Shu Hu ◽  
Yuan Sheng Zhai ◽  
Fu Gang Yan ◽  
Yu Fu Li ◽  
Xian Li Liu
Keyword(s):  
Cutting Force ◽  
Empirical Model ◽  
High Speed ◽  
Orthogonal Design ◽  
Cutting Speed ◽  
Least Square Method ◽  
Cutting Parameters ◽  
Least Square ◽  
Physical Parameters ◽  
Hard Cutting

In the cutting process, cutting force is one of the important physical parameters, which affects the generation of cutting heat, tool life and surface precision of workpiece directly. In this paper an orthogonal design of experiment and subsequent data is analyzed using high speed finish hard cutting GCr15 whose hardness is 65HRC. Cutting speed is 200-400m/min, to study the influence of cutting parameters on cutting force, cutting force empirical model has obtained from least square method.

Research on the Concept of Controlled Speed Based on Cutting Band Saw

2013 ◽  
Vol 584 ◽  
pp. 45-49
Author(s):  
Ying Chun Xue
Keyword(s):  
Removal Rate ◽  
Cutting Speed ◽  
Machine Design ◽  
Feed Speed ◽  
Cutting Efficiency ◽  
The Past ◽  
Equation Solving ◽  
Cutting Machine ◽  
Band Saw ◽  
Core Problem

Speed control is the core problem of cutting machine design, the past has been the experience of adjusting the cutting speed, the lack of theoretical basis, randomness. In view of this, according to the basic requirements of cutting efficiency maximization, deduces the removal rate and feed speed, time equation, solving the cutting efficiency problem exists in the design of machine, and established when the removal rate is constant, relation curves of shear rate and time. Use of the method of cutting machine design, can improve the accuracy and scientific design, has higher application value.

FORMULATION OF MATHEMATICAL MODEL FOR MAINTENANCE COST OF A STONE CRUSHING PLANT BASED ON DIMENSIONAL ANALYSIS AND MULTIPLE REGRESSION

2013 ◽  
pp. 1-6
Author(s):  
PROF. ANJALI J. JOSHI ◽  
DR. JAYANT P. MODAK
Keyword(s):  
Mathematical Model ◽  
Multiple Regression ◽  
Dimensional Analysis ◽  
Least Square Method ◽  
Least Square ◽  
Maintenance Cost ◽  
Design Data ◽  
Static And Dynamic Analysis ◽  
Dimensionless Equation ◽  
Crushing Plant

This paper presents the approach for the mathematical modeling of maintenance cost for the set up of new Stone Crushing Plant based on the dimensional analysis and multiple regression. Presented maintenance cost mathematical model is derived based on the generated design data. Design data is generated from actual design of all stone crushing plants followed by static and dynamic analysis. Estimation of design data is carried out based on the assumed plant layout. Dimensional analysis is used to make the independent and dependent variables dimensionless and to get dimensionless equation. Later, multiple regression analysis is applied to this dimensionless equation to obtain the index values based on the least square method. The mathematical model of maintenance cost is formulated using these obtained index values. Finally, the formulated model is evaluated on the basis of correlation and root mean square error between the computed values by model and the estimated values.

Establishing Relationships Between Manufacturing Sustainability and Performance

2010 ◽  
Author(s):  
Matthew Johnson ◽  
Delcie Durham
Keyword(s):  
Life Cycle ◽  
Environmental Impact ◽  
Removal Rate ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Feed Speed ◽  
Productivity Cost ◽  
Full Life Cycle ◽  
Time Basis ◽  
And Performance

The current LCA methods assess a product’s sustainability over its full life cycle, cradle-to-grave. While the number(s) obtained detail the contributions a process makes to a product in terms of energy intensity or the generation of wastes, it is insufficient to optimize a process for both sustainability and performance objectives. The Economic Input/Output Life Cycle Analysis (EIO-LCA) was used to investigate whether metrics could be identified which address sustainability — performance issues in materials processing. This method lends itself to the assessment of processes on a unit time basis while allowing for calculation of resources used and byproducts expelled. Productivity of manufacturing processes is also based on time. For example, material removal rate is related to processing feed, speed, and the geometry and tolerances established during design. A scaled waterjet cutting process was tested to investigate the unit time relationships. The EIO-LCA was conducted and the subsequent environmental impact in the form of total energy consumed and equivalent CO2 expelled evaluated per unit time, establishing the relationship to cutting speed. Although this is a static LCA at set conditions, it suggests that relationships can be explored between the regulation of resources, productivity, cost and environmental impact by varying the processing parameters.

scholarly journals Accelerated Lifetime Data Analysis with a Nonconstant Shape Parameter

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Guodong Wang ◽  
Zhanwen Niu ◽  
Zhen He
Keyword(s):  
Shape Parameter ◽  
High Reliability ◽  
Least Square Method ◽  
Least Square ◽  
Accelerated Life Test ◽  
Stress Factors ◽  
Likelihood Method ◽  
Accelerated Life ◽  
Lifetime Data Analysis ◽  
Accelerated Lifetime

Accelerated life test is commonly used for the estimation of high-reliability product. In this paper, we present a simple and efficient approach to estimate the coefficients of acceleration models. Assuming that both scale and shape parameters of Weibull lifetime distribution vary with stress factors, we estimate the parameters of Weibull distribution using maximum likelihood method and reduce the bias of shape parameter estimator. Considering the heteroscedasticity, we compute the estimates of the coefficients of acceleration models through weighted least square method. Additionally, we obtain the confidence interval of low percentile via bootstrapping. We compare the proposed method with other methods using a real lifetime example. Finally, we study the performance of the proposed method by simulation. The simulation results show that our proposed method is effective.

scholarly journals Calculation and realization of new method grey residual error correction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254154
Author(s):  
Lifang Xiao ◽  
Xiangyang Chen ◽  
Hao Wang
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Residual Error ◽  
Least Square ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
True Value ◽  
Prediction Function ◽  
Fitting Method

Aiming at the problem of prediction accuracy of stochastic volatility series, this paper proposes a method to optimize the grey model(GM(1,1)) from the perspective of residual error. In this study, a new fitting method is firstly used, which combines the wavelet function basis and the least square method to fit the residual data of the true value and the predicted value of the grey model(GM(1,1)). The residual prediction function is constructed by using the fitting method. Then, the prediction function of the grey model(GM(1,1)) is modified by the residual prediction function. Finally, an example of the wavelet residual-corrected grey prediction model (WGM) is obtained. The test results show that the fitting accuracy of the wavelet residual-corrected grey prediction model has irreplaceable advantages.

Tool-Life Optimization in High-Speed Milling of Aeronautical Aluminum Alloy 7050-T7451

2009 ◽  
Vol 626-627 ◽  
pp. 117-122
Author(s):  
Y.Z. Pan ◽  
Xing Ai ◽  
Jun Zhao ◽  
X.L. Fu
Keyword(s):  
Aluminum Alloy ◽  
Tool Life ◽  
High Speed ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Least Square ◽  
High Speed Milling ◽  
Solid Carbide ◽  
Multi Linear Regression

A new approach is presented to optimize the tool life of solid carbide end mill in high-speed milling of 7050-T7451 aeronautical aluminum alloy. In view of this, the multi-linear regression model for tool life has been developed in terms of cutting speed and feed per tooth by means of central composite design of experiment and least-square techniques. Variance analyses were applied to check the adequacy of the predictive model and the significances of the independent parameters. Response contours of tool life and metal removal rates were generated by using response surface methodology (RSM). The analysis results show that it is possible to select an optimum combination of cutting speed and feed per tooth that improves metal removal rate without any sacrifice in tool life.

scholarly journals A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study

2021 ◽  
Vol 16 (4) ◽  
pp. 443-456
Author(s):  
D.D. Trung ◽  
H.X. Thinh
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Multi Criteria Decision Making ◽  
Turning Process ◽  
Decision Methods ◽  
Determination Methods

Multi-criteria decision-making is important and it affects the efficiency of a mechanical processing process as well as an operation in general. It is understood as determining the best alternative among many alternatives. In this study, the results of a multi-criteria decision-making study are presented. In which, sixteen experiments on turning process were carried out. The input parameters of the experiments are the cutting speed, the feed speed, and the depth of cut. After conducting the experiments, the surface roughness and the material removal rate (MRR) were determined. To determine which experiment guarantees the minimum surface roughness and maximum MRR simultaneously, four multi-criteria decision-making methods including the MAIRCA, the EAMR, the MARCOS, and the TOPSIS were used. Two methods the Entropy and the MEREC were used to determine the weights for the criteria. The combination of four multi-criteria making decision methods with two determination methods of the weights has created eight ranking solutions for the experiments, which is the novelty of this study. An amazing result was obtained that all eight solutions all determined the same best experiment. From the obtained results, a recommendation was proposed that the multi-criteria making decision methods and the weighting methods using in this study can also be used for multi-criteria making decision in other cases, other processes.

scholarly journals Determinant Analysis of Bilateral Trade Between Indonesia and Yemen During the Period 1990-2015: A Gravity Model Approach

2017 ◽  
Vol 1 (2) ◽  
pp. 37
Author(s):  
Sulthon Sjahril Sabaruddin
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Gravity Model ◽  
Bilateral Trade ◽  
Least Square Method ◽  
Least Square ◽  
Political Uncertainty ◽  
Independent Variables ◽  
Trade Model

This study examines the factors that influence the performance of bilateral trade between Indonesia and Yemen. In conducting the study, the authors use the conventional bilateral trade model is modified gravity model of international trade approach by using multiple regression analysis with Ordinary Least Square method. The analysis finds that the three independent variables that significantly affect the value of bilateral trade between Indonesia –Yemen is the GDP of Yemen, political uncertainty and security in Indonesia, as well as the presence of Indonesian Representative in the Republic of Yemen. While other independent variables namely Indonesia's GDP and political uncertainty and security in Yemen, based on the conclusions statistically, the two independent variables do not yet have enough evidence to say that the GDP of Indonesia and political uncertainty and security in Yemen significantly affect bilateral trade between Indonesia-Yemen. This trade gravity model previously passed testing assumptions multiple regression analysis with hypothesis testing and the accuracy of the model.

scholarly journals FORMULATION OF MATHEMATICAL MODEL FOR PRODUCTION TURNOVER OF BIODIESEL PLANT BASED ON DIMENSIONAL ANALYSIS AND MULTIPLE REGRESSION

2012 ◽  
pp. 268-276
Author(s):  
ASHWIN S. CHATPALLIWAR ◽  
DR. VISHWAS S. DESHPANDE ◽  
DR. JAYANT P. MODAK ◽  
DR. NILESHSINGH V. THAKUR
Keyword(s):  
Mathematical Model ◽  
Multiple Regression ◽  
Dimensional Analysis ◽  
Least Square Method ◽  
Least Square ◽  
Design Data ◽  
Dimensionless Equation ◽  
Set Up ◽  
The Mathematical Model ◽  
Data Design

This paper presents the approach for the mathematical modeling of production turnover for the set up of new Biodiesel plant based on the dimensional analysis and multiple regression. Presented production turnover mathematical model is derived based on the generated design data. Design data is generated from the estimated design data. Estimation of design data is carried out based on the assumed plant layouts of different capacities. Dimensional analysis is used to make the independent and dependent variables dimensionless and to get dimensionless equation. Later, multiple regression analysis is applied to this dimensionless equation to obtain the index values based on the least square method. The mathematical model of production turnover is formulated using these obtained index values. Finally, the formulated model is evaluated on the basis of correlation and root mean square error between the computed values by model and the estimated values.

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