Influence of cardboard variability on edge retention : 3Cr13, VG-10, k390, CPM M4


Introduction

Previous work looked at edge retention slicing cardboard with various steels at a specific apex angle and finish (15 dps/fine-DMT) 1. To minimize systematic bias, random sampling was performed on a large cardboard pile to ensure the cardboard influence was a random error only.

An interesting question considering such work is how large is this random error compared to the difference from one steel to another? If the influence of the cardboard is large enough it might not even be possible to see edge retention differences even in very different steels because all that would be seen is differences from one cardboard pile to another.

Method

As an experiment to have a look at that idea, the following knives were used to slice 1/8" ridged cardboard on a 2" draw with an approximate speed of 1 fps :

All knives had the edge bevels at 6-8 dps with a 15 dps micro-bevel set with an x-coarse DMT benchstone as shown on the image to the right.

Eight trials were completed with each blade, edge retention calculated as the TCE or total cutting efficiency. The stopping point was set to 1.5% of optimal. Median statistics were used to look at the combined results as well as a direct summation.

Results

A quick examination of the results which again showed the accumulation of eight runs to get an average :

In short, while carefully cutting a pile of cardboard and taking detailed measurements, it was even hard to see a difference between the typical lowest vs highest steels. This has some interesting implications for arguments on the importance of steel and general commentary and implications about steel influence. What does it say about just using knives for daily activities? In daily use the amount of variation is going to be more and so maybe steels don't matter as much as people seem to think?

Now why does this happen? Well during the cutting the performance isn't just due to the knife and the steel. It is also due to what is being cut. If what is being cut is changing at random, well the performance could scatter just due to that effect. Now you might think, well I will just use a very consistent material. This will help, but then think about all the other random factors that are coming into play (the speed, force, angles, etc. . )

Discussion

What can we learn from the amount of the scatter? Well we can determine how much variation there is in the cardboard. The plot on the right show the performance expected if cardboard could vary by a factor of 10:1.

(note here I am assuming an underlying performance ratio for the steels of 1:2:2.5:3 this is a basic wear based estimate)

In short, after ten runs with each steel that it isn't likely that the differences in steels would be seen as the variability due to the cardboard is actually larger. Given the actual data and general observations on how cardboard can vary significantly then it seems reasonable to conclude that the actual data from cutting cardboard represent a similar spread as the 10:1 estimate.

So it seems safe, at least as a coarse example, to say cardboard looks to produce changes in edge retention on a given knife by about 10:1.

An obvious question to ask then is how much cardboard would have to be cut in order to actually be able to tell the steels apart?

The image on the right shows a simulation of fifty runs with each knife, with the same kind of expected noise. It looks like this is how much it would take so that conclusions are statistically stable. What does that actually mean? Well it just means that the results would generally be accurate, which is to say if you were not to do that, you are going to frequenly just reporting random noise.

What does that mean in terms of general use? It means that if those knives were used in regular use then from one sharpening session to the next what would be seen is just a lot of random variation. However if the number of sharpening sessions was looked at for a month then it is likely a difference could be seen, if a yearly comparison was made it is even more likely that a difference could be seen.

If all of this is true then what about all the commentary on steels which describe how the edge retention varies and claims are made about comparisons done in a manner which based on the above should not be able to see such differences? The explanation :

Confirmation bias is the fuel for volumes of claims. A single example is the claim that emergency rooms are busier on nights with a full moon. Many hold this claim as common knowledge and swear to the truth of it from personal experience. However, this claim is not supported by evidence. In fact, evidence contradicts the claim. Similarly, there is no measurable increase in suicides during a full moon. So, the claim turns out to be a myth (let's call it the 'Full Moon Myth'). But why is it so prevalent in pop culture?

Once an idea such as the Full Moon Myth becomes known, many who work in emergency rooms will tend to take special notice of events occurring during nights with full moons. Emergency rooms are busy places in which "crazy" things happen all of the time. Proponents of the myth may go to work with the thought, "Oh no! There's a full moon tonight. Things are going to be crazy." Such a thought would prime the believer to remember the events occurring on the full moon nights, while forgetting the events on other 'common' nights.

In short, without proper experimental controls, the conclusions reached often are nothing more than the expected preconceived notions.

Recap

More extended commentary can be seen in the forum thread :

and the YT video.

References

1 : Edge retention : cardboard


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Written: 17/01/2015 Updated: Copyright (c) 2015 : Cliff Stamp