Regression Model and Control in Experimental Mechanical Processes

Ângelo Márcio Oliveira Sant’Anna; Danilo Marcondes Filho

doi:10.4028/www.scientific.net/AMM.518.155

Paper Titles

Dynamic Experimental Study on Highly Weathered Granite
p.132

Research on Experimental Method for the Working Force between Packer Rubber and Casing Tube
p.138

Residual Stress Study of Autofrettaged Barrel under Impulsive Loading
p.144

Temperature Field and Thermal Stress Analysis of Large Caliber Gun Barrel
p.150

Regression Model and Control in Experimental Mechanical Processes
p.155

Relations of Lift and Drag Coefficients of Flow around Flat Plate
p.161

The Numerical Simulation of Composite Explosive Energy Coupling in Underwater Explosion
p.165

Experiment Study on Influence of Initial Stress in Concrete Filled Steel Tubular Latticed Columns under Axial Load
p.170

Performance of Earthquake Simulation Three Bi-Axial Shaking Tables
p.178

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 518Regression Model and Control in Experimental...

Regression Model and Control in Experimental Mechanical Processes

Abstract:

The use of the regression model is usually applied in experimental mechanics processes and allowing for modeling the relationship between one or more process variables. Besides, the regression models are used for monitoring of response variables as function of one or more process variables. The scheme is based on the residuals deviance from regression model for detecting any disturbance in the control variables. This paper presents the control charts from modeling of an experimental mechanic industrial processes that involve count variables. We illustrated the performance of scheme to case study based on real process.

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Periodical:

Applied Mechanics and Materials (Volume 518)

Pages:

155-160

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.518.155

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Online since:

February 2014

Authors:

Ângelo Márcio Oliveira Sant’Anna*, Danilo Marcondes Filho

Keywords:

Poisson Distribution, Regression Model, Statistical Quality Control

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