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#9
26th July 2010, 08:20 PM
 Miner Forum Moderator   Registration Date: Apr 2002 Location: Greater Milwaukee area, Wisconsin Posts: 3,124 Thanks Given to Others: 557 Thanked 1,738 Times in 1,113 Posts Blog Entries: 13 Karma Power: 391 Karma: 16738
Re: Taguchi's Analysis using Minitab 16

Quote:
 In Reply to Parent Post by Allattar Just wondering about things here. An L27 design for 4 factors and 3 levels on each factor. The standard design gives only 9 unique different combinations of those four factors. There are then 3 measurements at each of those 9, hence a 27 run design. So 8 degrees of freedom would come from 9 - 1. Which would indicate that the linear analysis here is only analysing the means of those 9 combinations. Which makes sense if we assume that the Standard deviation is not constant across the design, as we assume that the variance is constant across a GLM. Does that explain the missing df? as it isn't using 27 runs, but is just using means of the 9 different run combinations.
I reviewed Minitab's help documentation. I am not 100% positive, but I believe that Allattar is correct in his post. It would definitely explain the results. The only way to be certain, would be to manually calculate the ANOVA, or trick Minitab into it.

I was taught Taguchi methods by Shin Taguchi in the mid-80's. The use of the Orthogonal Array was taught separately from Robust Design, which analyzes means and S/N Ratio. This allowed you to think and decide how you would analyze the results of the DOE.

Minitab has highly automated the Taguchi analysis routine to the point that you cannot decide which analysis that you want to perform. It has made the decision for you and has not explained what it has done.

The only way to obtain the p-value is to pool the factor with the smallest MS, or perform a non-Taguchi analysis.
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"A fool can learn from his own experiences; the wise learn from the experience of others." - Democritus, 460-370 B.C.

#10
26th July 2010, 09:07 PM
 privilegeastros Getting Involved (6 to 9 Posts)   Registration Date: Jul 2010 Location: Greece Posts: 6 Thanks Given to Others: 0 Thanked 0 Times in 0 Posts Karma Power: 12 Karma: 10
Re: Taguchi's Analysis using Minitab 16

Allatar has a good point on that! However, when im doing the prediction, the mean result is negative, where for a "small is better" the result data has to be a non negative value as can be to zero.
#11
28th July 2010, 08:15 PM
 privilegeastros Getting Involved (6 to 9 Posts)   Registration Date: Jul 2010 Location: Greece Posts: 6 Thanks Given to Others: 0 Thanked 0 Times in 0 Posts Karma Power: 12 Karma: 10
Re: Taguchi's Analysis using Minitab 16

in another two similar predictions, the values come negative , one for S/N ratio and to the other the MEAN value. what does the negative sign means?
#12
30th July 2010, 10:05 PM
 wang1568 Involved in Discussions   Registration Date: Jul 2006 Location: Taiwan Posts: 10 Thanks Given to Others: 0 Thanked 2 Times in 2 Posts Karma Power: 31 Karma: 603
Re: Taguchi's Analysis using Minitab 16

Hello privilegeastros
I found the problem as below
1) When the minitab to create a 3-leval 4 Factor with default Taguchi design, the default designs simply takes the first 4 of columns of the orthogonal array.
2) Watch the design on orthogonalTaguchi.MTW or minitab output (with step 1), it seem to have 27 runs but just 9 different combination, so is a L9 * 3(replicate) design, and its the reason you loss df=19 (got df=9 not DF=27)
3) The main cause is 4 factor are confounded when 4 factor in column 1~4, so the design is failed
4) The correct design is using column 1 2 3 and 5, not column 4
#13
17th February 2013, 10:20 AM
 sethu89 Getting Involved (6 to 9 Posts)   Registration Date: Sep 2012 Posts: 6 Thanks Given to Others: 0 Thanked 0 Times in 0 Posts Karma Power: 3 Karma: 10
Re: Taguchi's Analysis using Minitab 16

Hello miner.
I am new to this concept. So help me with this design.
I need to choose a array from this.

3 factors : cutting speed, depth of cut, feed.

levels: cutting speed - 12,25.
depth of cut -10,20,30,40,50.
feed - 10,20,30,40,50.

what is the array to select?
and how to proceed with minitab??

#14
17th February 2013, 11:28 AM
 Miner Forum Moderator   Registration Date: Apr 2002 Location: Greater Milwaukee area, Wisconsin Posts: 3,124 Thanks Given to Others: 557 Thanked 1,738 Times in 1,113 Posts Blog Entries: 13 Karma Power: 391 Karma: 16738
Re: Taguchi's Analysis using Minitab 16

What is the objective of your experiment? Is it to determine which factors have a significant effect on the mean of the output, which factors have a significant effect on the variation of the output, or is it to determine the optimum levels at which to set the process/
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"A fool can learn from his own experiences; the wise learn from the experience of others." - Democritus, 460-370 B.C.
#15
17th February 2013, 12:22 PM
 sethu89 Getting Involved (6 to 9 Posts)   Registration Date: Sep 2012 Posts: 6 Thanks Given to Others: 0 Thanked 0 Times in 0 Posts Karma Power: 3 Karma: 10
Re: Taguchi's Analysis using Minitab 16

With the above said values i am conducting experiments. The machining force and temperature will be measured.

I have to get the influence of each factor for the force and temperature.
And come to a conclusion that which is the best set of combination to have a better force and reduced temperature.

Thanks again.
#16
17th February 2013, 09:04 PM
 Miner Forum Moderator   Registration Date: Apr 2002 Location: Greater Milwaukee area, Wisconsin Posts: 3,124 Thanks Given to Others: 557 Thanked 1,738 Times in 1,113 Posts Blog Entries: 13 Karma Power: 391 Karma: 16738
Re: Taguchi's Analysis using Minitab 16

I recommend that you take the following sequential experimentation approach:
1. Start with a 2-level full or half-fractional factorial design with center points.
levels: cutting speed - 12,25.
depth of cut -20,40.
feed - 20,40.
Center point = 18.5, 30, 30
2. If center points (curvature) are significant, augment the design by adding axial points to create a circumscribed Central Composite Design and additional center points to create a quadratic model.
3. If center points are not significant a linear model is sufficient to model.
4. Use Response Optimizer to determine the optimal settings for your two responses.
I recommend against using the Taguchi Orthogonal Arrays. Yes, they do work, and I personally have used them successfully, but have learned better approaches since then.
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"A fool can learn from his own experiences; the wise learn from the experience of others." - Democritus, 460-370 B.C.

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