STATISTICAL DESIGN—Fractional Factorial Experiment Designs of the Mixed 2m3nSeries

1960 ◽  
Vol 52 (6) ◽  
pp. 93A-95A
Author(s):  
W. S. Connor
Keyword(s):  
Statistical Design ◽  
Fractional Factorial ◽  
Factorial Experiment

Effect of Heat Treatment Hardness of Al-Cu-Mg-Li-Zr, Al-Mg-Si and and Al-Mg-Zn Alloys-Experimental Correlation Using Factorial DOE and ANOVA Methods

2014 ◽  
Vol 881-883 ◽  
pp. 1317-1329 ◽  
Author(s):  
Mahmoud M. Tash ◽  
Saleh Alkahtani
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Cold Work ◽  
Solution Treatment ◽  
Statistical Design ◽  
Aging Treatment ◽  
Fractional Factorial ◽  
Statistical Design Of Experiments ◽  
The Impact ◽  
Zn Alloys

The present study was conducted to investigate the effect of heat treatment on the aging and mechanical behavior of Al-Cu-Mg-Li-Zr , Al-Mg-Si and and Al-Mg-Zn alloys (8090 , 6082 and 7075). The effect of cold work after solution treatment, aging parameters (time and temperature) on the microstructure and mechanical properties were studied. Attempts are made to determine the combined effect of cold work and aging treatment on the hardness, UTS and microstructure for these alloys. By study the impact of different heat treatments for Al-Mg-Si alloys (6082), Al-Cu-Mg-Li-Zr (8090) and Al-Mg-Zn (7075) aluminum alloys on the hardness and mechanical properties, it is possible to determine conditions necessary to achieve better mechanical properties and the maximum levels of hardness and values corresponding to those considered suitable for commercial applications of these alloys.Design of Experiment (DOE) method in Minitab is used to measure the impact of various factors and how they relate. Correlation between the hardness and different metallurgical factors for these alloys at both quantitative and qualitative are investigated and analysed. A statistical design of experiments (DOE) approach using fractional factorial design was applied to determine the influence of controlling variables of cold work and heat treatment parameters and any interactions between them on the hardness of the above alloys. A mathematical model is developed to relate the alloy hardness with the different metallurgical parameters to acquire an understanding of the effects of these variables and their interactions on the hardness of wrought Al-alloys. It is noticed that cold work, following solution treatment, accelerates the precipitation rate leading to a rise in strength

Fractional Factorial Experiment Designs for Factors at Two Levels.

Biometrika ◽  
1958 ◽  
Vol 45 (3/4) ◽  
pp. 598
Author(s):  
F. N. David ◽  
U.S. Dept. of Commerce
Keyword(s):  
Fractional Factorial ◽  
Factorial Experiment

Use of a Fractional Factorial Experiment to Assess the E-healthcare Application Design Needs of Persons With Dual Diagnosis

2012 ◽  
Vol 8 (4) ◽  
pp. 277-282 ◽  
Author(s):  
Armando J. Rotondi ◽  
Michael Spring ◽  
Jonathan Grady ◽  
Richard Simpson ◽  
James Luther ◽  
...  
Keyword(s):  
Dual Diagnosis ◽  
Fractional Factorial ◽  
Factorial Experiment ◽  
Healthcare Application ◽  
Application Design

scholarly journals Property Optimisation of EPDM Rubber Composites Using Mathematical and Statistical Strategies

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Sivaldo Leite Correia ◽  
Denilso Palaoro ◽  
Ana Maria Segadães
Keyword(s):  
Calcium Carbonate ◽  
Statistical Design ◽  
Minimum Cost ◽  
Fractional Factorial ◽  
Statistical Design Of Experiments ◽  
Rubber Composites ◽  
Elongation At Break ◽  
Predicted Values ◽  
Paraffinic Oil ◽  
Numerical Optimisation

This paper describes a study in which EPDM-based rubber composites were investigated aiming at developing formulations subjected to restrictions on cost and the properties of the material. The contents of components other than calcium carbonate, paraffinic oil, and CBS vulcanising accelerator, as well as additives and processing conditions, were kept constant. Fractional factorial design coupled with computational numerical optimisation was used to minimise the number of mixtures. The results demonstrate that statistical design of experiments and particle swarm optimisation (PSO) algorithms are promising methods to design composition variables. Mixture costs as low as 1.92 US$/kg can be achieved in compositions containing, for example, 107 phr of calcium carbonate, 95 phr of paraffinic oil, and 1.13 phr of CBS accelerator. The corresponding composite property-predicted values were 66.8 Shore A for hardness, tensile strength of 7.8 MPa, 570.8% elongation at break, and 23.0% rebound resilience. This demonstrates that, in this way, the desired product with specified characteristics can be comfortably manufactured at minimum cost.

Generation of Two-Level Cost Optimal Fractional Factorials

1978 ◽  
Vol 22 (1) ◽  
pp. 599-599
Author(s):  
Joseph J. Pignatiello
Keyword(s):  
Mathematical Programming ◽  
Programming Problem ◽  
Minimum Cost ◽  
Fractional Factorial ◽  
Factorial Experiment ◽  
Mathematical Programming Problem ◽  
Fractional Factorials ◽  
Number Of Factors ◽  
Main Effects ◽  
Full Factorial

It is assumed that, in a 2k factorial experiment, there are different costs per observation at each of the factor combinations. When the number of factors, k, increases, the total number of observations in the full factorial increases rapidly as does the expense of observing all observations in the full factorial. If the experimenter can assume certain classes of higher-order interactions are negligible, then advantage may be taken by observing measurements from an orthogonal fractional factorial. For any “1/2p” fraction of the full factorial, a 2k-p experiment, there are 2p feasible orthogonal fractions that could be selected at random. This paper develops an algorithm for generating the minimum cost such fraction in an efficient way. The problem is formulated as a mathematical programming problem subject to a resolution III constraint (main effects unconfounded). Computational experience is presented.

scholarly journals Effect of Different Design Parameters on the Cyclic Response of Reduced Beam Sections (RBS) Moment Connections

2021 ◽  
Author(s):  
Syed Asad Abbus Naqvi
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Design ◽  
Fractional Factorial ◽  
Design Parameters ◽  
Dissipated Energy ◽  
Northridge Earthquake ◽  
Initial Stiffness ◽  
Moment Connections ◽  
Cyclic Response ◽  
Rbs Connections

After the 1994 Northridge earthquake, research has been conducted to develop new types of beamcolumn moment connections, such as Reduced Beam Section (RBS) connections. This study performs a sensitivity analysis of the cyclic response of RBS connections using detailed finite element simulation. The significance of the effect of twenty-one factors is assessed using a statistical design of experiment method. The input factors are related to the material properties or the geometry of the beam-column connection. A two-level fractional factorial design is used to create factor combinations for the sensitivity analysis. The cyclic response of RBS connections is assessed in terms of five response variables, including: the total dissipated energy, initial stiffness, strength degradation rate, maximum moment capacity and rupture index at 7.5% storey drift. The sensitivity analysis results show that the beam depth has the greatest influence on the cyclic response of RBS connections.

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