DOE Overview - Revision history

Richard House: /* For Comparison: One Factor Designs */

2017-08-10T15:21:35Z

For Comparison: One Factor Designs

← Older revision		Revision as of 15:21, 10 August 2017
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	==DOE Types==		==DOE Types==
	===For Comparison: One Factor Designs===		===For Comparison: One Factor Designs===
	With these designs, only one factor is under investigation, and the objective is to determine whether the response is significantly different at different factor levels. The factor can be qualitative or quantitative. In the case of qualitative factors (e.g., different suppliers, different materials, etc.), no extrapolations (i.e., predictions) can be performed outside the tested levels, and only the effect of the factor on the response can be determined. On the other hand, data from tests where the factor is quantitative (such as temperature, voltage, load, etc.) can be used for both effect investigation and prediction, provided that sufficient data is available. (In ~~DOE~~++, predictions for one factor designs can be performed using the multiple linear regression folio or free form folio.)		With these designs, only one factor is under investigation, and the objective is to determine whether the response is significantly different at different factor levels. The factor can be qualitative or quantitative. In the case of qualitative factors (e.g., different suppliers, different materials, etc.), no extrapolations (i.e., predictions) can be performed outside the tested levels, and only the effect of the factor on the response can be determined. On the other hand, data from tests where the factor is quantitative (such as temperature, voltage, load, etc.) can be used for both effect investigation and prediction, provided that sufficient data is available. (In Weibull++ DOE folios, predictions for one factor designs can be performed using the multiple linear regression folio or free form folio.)

	===For Factor Screening: Factorial Designs===		===For Factor Screening: Factorial Designs===

Lisa Hacker: /* Verification */

2015-06-23T23:36:20Z

Verification

← Older revision		Revision as of 23:36, 23 June 2015
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	This final stage involves validation of the best settings of the factors by conducting a few follow-up experiment runs to confirm that the system functions as desired and all objectives are met.		This final stage involves validation of the best settings of the factors by conducting a few follow-up experiment runs to confirm that the system functions as desired and all objectives are met.

	~~[[Category: Completed Theoretical Review]]~~

Chris Kahn: /* For Factor Screening: Factorial Designs */

2013-10-18T17:45:51Z

For Factor Screening: Factorial Designs

← Older revision		Revision as of 17:45, 18 October 2013
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	* '''Plackett-Burman Design'''		* '''Plackett-Burman Design'''

	This is a special category of two level fractional factorial designs, proposed by ~~[[DOE References\|~~R. L. Plackett and J. P. Burman, 1946]], where only a few specifically chosen runs are performed to investigate just the main effects (i.e., no interactions).		This is a special category of two level fractional factorial designs, proposed by R. L. Plackett and J. P. Burman [[DOE References\|[1946]]], where only a few specifically chosen runs are performed to investigate just the main effects (i.e., no interactions).

	* '''Taguchi's Orthogonal Arrays'''		* '''Taguchi's Orthogonal Arrays'''

Chris Kahn: /* For Factor Screening: Factorial Designs */

2013-10-18T17:01:54Z

For Factor Screening: Factorial Designs

← Older revision		Revision as of 17:01, 18 October 2013
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	* '''Plackett-Burman Design'''		* '''Plackett-Burman Design'''

	This is a special category of two level fractional factorial designs, proposed by [[DOE References\|R. L. Plackett and J. P. Burman, 1946]], where only a few specifically chosen runs are performed to investigate just the main effects (i.e. no interactions).		This is a special category of two level fractional factorial designs, proposed by [[DOE References\|R. L. Plackett and J. P. Burman, 1946]], where only a few specifically chosen runs are performed to investigate just the main effects (i.e., no interactions).

	* '''Taguchi's Orthogonal Arrays'''		* '''Taguchi's Orthogonal Arrays'''

Chris Kahn: /* For Experiments with Constraints: Optimal Custom Design */

2013-10-18T16:57:44Z

For Experiments with Constraints: Optimal Custom Design

← Older revision		Revision as of 16:57, 18 October 2013
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	===For Experiments with Constraints: Optimal Custom Design===		===For Experiments with Constraints: Optimal Custom Design===
	The [~~http://www.reliawiki.org/index.php/~~Optimal_Custom_Designs optimal custom design tool] can be used to modify the above standard designs to plan an experiment that meets any or all of the following constraints: 1) limited availability of test samples, 2) factor level combinations that cannot be tested, 3) factor level combinations that must be tested or 4) specific factors effects that must be investigated.		The [[Optimal_Custom_Designs\|optimal custom design tool]] can be used to modify the above standard designs to plan an experiment that meets any or all of the following constraints: 1) limited availability of test samples, 2) factor level combinations that cannot be tested, 3) factor level combinations that must be tested or 4) specific factors effects that must be investigated.

	==Stages of DOE==		==Stages of DOE==

Chris Kahn: /* For Experiments with Constraints: Optimal Custom Design */

2013-10-18T16:56:50Z

For Experiments with Constraints: Optimal Custom Design

← Older revision		Revision as of 16:56, 18 October 2013
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	===For Experiments with Constraints: Optimal Custom Design===		===For Experiments with Constraints: Optimal Custom Design===
	The optimal custom design tool can be used to modify the above standard designs to plan an experiment that meets any or all of the following constraints: 1) limited availability of test samples, 2) factor level combinations that cannot be tested, 3) factor level combinations that must be tested or 4) specific factors effects that must be investigated.		The [http://www.reliawiki.org/index.php/Optimal_Custom_Designs optimal custom design tool] can be used to modify the above standard designs to plan an experiment that meets any or all of the following constraints: 1) limited availability of test samples, 2) factor level combinations that cannot be tested, 3) factor level combinations that must be tested or 4) specific factors effects that must be investigated.

	==Stages of DOE==		==Stages of DOE==

Chris Kahn: /* For Factor Screening: Factorial Designs */

2013-10-18T16:40:13Z

For Factor Screening: Factorial Designs

← Older revision		Revision as of 16:40, 18 October 2013
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	* '''Two Level Full Factorial Designs'''		* '''Two Level Full Factorial Designs'''

	~~These are factorial~~ designs ~~where the number of the levels for each of the~~ factors ~~is restricted to~~ two. Restricting the levels to two and running a full factorial experiment reduces the number of treatments (compared to a general full factorial experiment), and it allows for the investigation of all the factors and all their interactions. If all factors are quantitative, then the data from such experiments can be used for predictive purposes, provided a linear model is appropriate for modeling the response (since only two levels are used, curvature cannot be modeled).		With these designs, all factors must have only two levels. Restricting the levels to two and running a full factorial experiment reduces the number of treatments (compared to a general full factorial experiment), and it allows for the investigation of all the factors and all their interactions. If all factors are quantitative, then the data from such experiments can be used for predictive purposes, provided a linear model is appropriate for modeling the response (since only two levels are used, curvature cannot be modeled).

	* '''Two Level Fractional Factorial Design'''		* '''Two Level Fractional Factorial Design'''

	This is a special category of two level designs, where not all factor level combinations are considered, and the experimenter can choose which combinations are to be excluded. Based on the excluded combinations, certain interactions cannot be ~~determined~~.		This is a special category of two level designs, where not all factor level combinations are considered, and the experimenter can choose which combinations are to be excluded. Based on the excluded combinations, certain interactions cannot be investigated.

	* '''Plackett-Burman Design'''		* '''Plackett-Burman Design'''

Chris Kahn: /* For Comparison: One Factor Designs */

2013-10-18T16:35:51Z

For Comparison: One Factor Designs

← Older revision		Revision as of 16:35, 18 October 2013
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	==DOE Types==		==DOE Types==
	===For Comparison: One Factor Designs===		===For Comparison: One Factor Designs===
	With these designs, only one factor is under investigation, and the objective is to determine whether the response is significantly different at different factor levels. The factor can be qualitative or quantitative. In the case of qualitative factors (e.g., different suppliers, different materials, etc.), no extrapolations (i.e., predictions) can be performed outside the tested levels, and only the effect of the factor on the response can be determined. On the other hand, data from tests where the factor is quantitative (such as temperature, voltage, load, etc.) can be used for both effect investigation and prediction, provided that sufficient data is available. (In DOE++, predictions for one factor designs can be performed ~~by transferring the data to~~ the multiple linear regression folio or free form folio.)		With these designs, only one factor is under investigation, and the objective is to determine whether the response is significantly different at different factor levels. The factor can be qualitative or quantitative. In the case of qualitative factors (e.g., different suppliers, different materials, etc.), no extrapolations (i.e., predictions) can be performed outside the tested levels, and only the effect of the factor on the response can be determined. On the other hand, data from tests where the factor is quantitative (such as temperature, voltage, load, etc.) can be used for both effect investigation and prediction, provided that sufficient data is available. (In DOE++, predictions for one factor designs can be performed using the multiple linear regression folio or free form folio.)

	===For Factor Screening: Factorial Designs===		===For Factor Screening: Factorial Designs===

Chris Kahn: /* Introduction to DOE Principles */

2013-10-18T16:22:40Z

Introduction to DOE Principles

← Older revision		Revision as of 16:22, 18 October 2013
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	The number of treatments of an experiment is determined on the basis of the number of factor levels being investigated. For example, if an experiment involving two factors is to be performed, with the first factor having ''m'' levels and the second having ''n'' levels, then ''m'' x ''n'' treatment combinations can possibly be run, and the experiment is an ''m'' x ''n'' factorial design. If all ''m'' x ''n'' combinations are run, then the experiment is a ''full factorial''. If only some of the ''m'' x ''n'' treatment combinations are run, then the experiment is a ''fractional factorial''. In full factorial experiments, all the factors and their interactions can be investigated, whereas in fractional factorial experiments, at least some interactions are not considered because some treatments are not run.		The number of treatments of an experiment is determined on the basis of the number of factor levels being investigated. For example, if an experiment involving two factors is to be performed, with the first factor having ''m'' levels and the second having ''n'' levels, then ''m'' x ''n'' treatment combinations can possibly be run, and the experiment is an ''m'' x ''n'' factorial design. If all ''m'' x ''n'' combinations are run, then the experiment is a ''full factorial''. If only some of the ''m'' x ''n'' treatment combinations are run, then the experiment is a ''fractional factorial''. In full factorial experiments, all the factors and their interactions can be investigated, whereas in fractional factorial experiments, at least some interactions are not considered because some treatments are not run.

	It can be seen that the size of an experiment escalates rapidly as the number of factors (or the number of the levels of the factors) increases. For example, if 2 factors at 3 levels each are to be used, 9 (3x3=9) different treatments are required for a full factorial experiment. If a third factor with 3 levels is added, 27 (3x3x3=27) treatments are required, and 81 (3x3x3x3=81) treatments are required if a fourth factor with three levels is added. If only two levels are used for each factor, then in the four-factor case, 16 (2x2x2x2=16) treatments are required. For this reason, many experiments are restricted to two levels, and these designs are given a special treatment in this reference. Using a ~~two level~~ fractional ~~factorial experiment~~ further reduces the number of required treatments.		It can be seen that the size of an experiment escalates rapidly as the number of factors (or the number of the levels of the factors) increases. For example, if 2 factors at 3 levels each are to be used, 9 (3x3=9) different treatments are required for a full factorial experiment. If a third factor with 3 levels is added, 27 (3x3x3=27) treatments are required, and 81 (3x3x3x3=81) treatments are required if a fourth factor with three levels is added. If only two levels are used for each factor, then in the four-factor case, 16 (2x2x2x2=16) treatments are required. For this reason, many experiments are restricted to two levels, and these designs are given a special treatment in this reference. Using a fractional design further reduces the number of required treatments.

	==DOE Types==		==DOE Types==

Chris Kahn: /* For Factor Screening: Factorial Designs */

2013-10-17T23:54:29Z

For Factor Screening: Factorial Designs

← Older revision		Revision as of 23:54, 17 October 2013
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	* '''General Full Factorial Designs'''		* '''General Full Factorial Designs'''

	In general full factorial designs, ~~each factor~~ can have a different number of levels and can be quantitative or qualitative.		In general full factorial designs, the factors can have different number of levels, and they can be quantitative or qualitative.

	* '''Two Level Full Factorial Designs'''		* '''Two Level Full Factorial Designs'''