L6 Steel Kinetics

This steel is popular for use in knives and swords like the Samurai sword “Bainite Katana”.

Custom Howard Clark L6 Katana
The Bainite Katana is made of a special purpose low-alloy steel. It is very resistant to bending, to the point of near unbreakability. These blades can be made lighter and thinner and still remain stronger than conventional steel or 1086. The blades are also springy rather than soft, they will flex more than a normal blade, but the shape will not be altered. These blades are excellent for tameshigiri as well as general sword work.

Composition; I found the composition of L6 is something like: Fe-0.7C-0.5Si-1.75Ni-0.5Mo-0.25V-0.25Cu.

I calculated the time-temperature-transformation diagram, i’m not too sure about the accuracy bainite start temperature using this program, I would like to be able to find the experimental results for this alloy.

Depending on the bainite start temperature, the kinetics are fast enough to allow isothermal transformation to bainite on a reasonable timescale, at temperature of around 300 C which would give a high strength bainite. Howard Clarke sells the swords with as a martensite/bainite sword. If the bainite start temperature is higher it would be easy to get a mixed microstructure.

I should try to make a prediction of bainite start temperature – I made a neural network model for this.

AISI L6 TTT diagram calculated with MAP program MUCG83

Even without carbide precipitation transformation results in a large volume fraction of bainite.
AISI L6 Volume fraction calculated with MAP program MUCG83

Presumably if the bainite Katana is used in marteniste/bainite condition it is produced by continuous cooling or by quenching. Quenching would be good if it can be done in at a rate which gives martensite on the outside of the blade for sharpness and hardness, and bainite in the centre to give toughness. The other possibility is that the continuous cooling gives a mixture of bainite and martensite at every location. This can have a higher strength or hardness than martensite alone because austenite will get enriched in carbon as the bainite transforms, increasing it’s contribution to the strength.

I’d really like to have a look at the microstructure of these swords to see what structure the bainite has. Also it would be interesting to measure the mechanical properties of the alloy in the same condition – I need to look for some literature on L6 I guess. It looks like they should be strengthened by carbides and by copper precipitation.

Howard Clark who makes these swords has a webpage at mvforge.com.

–edit 6 October 2007–


Early stages of precipitation – Copper precipitation

Last year I attended a conference on Early stages of Precipitation, which was a paralled session organised by the Royal Microscopical Society (Not to be confused with this other RMS).

George Smith (etal) reported that in the early stages of copper precipitation, precipitation can be accelerated by additions of Nickel, despite the fact that we do not expect Nickel to segregate from thermodynamic calculations (based on continuum assumptions). Atom probe results show that Nickel segregates to the boundary between the two phases, this was explained by consideration of pair-wise interaction energies. Modelling was performed using a Monte-Carlo scheme which reproduced the observed precipitation sequences based on interaction energies.

Nickel is friendly to both Cu and Fe, while Cu-Fe don’t get on together so well.

From this type of explanation we should be able to make a prediction of which alloying elements can similarly accelerate or diminish the formation of clusters/ nuclei.

To do this I would like to calculate or find a table of interaction energies.

Magellan launch

The EPSRC National Service for Computational Chemistry Software (NSCCS) is pleased to announce the launch of its new machine, Magellan.

Magellan is a 224-core SGI Altix 4700, with 896GB of memory and 1TB of storage. You may not be aware that the service is also available for materials chemistry applications and supports the following codes;

DL_POLY 2.17 and 3.07
SIESTA 2.0.1
GULP 3.1
CPMD 3.11.1
Quantum-ESPRESSO 3.1.1
NWChem 4.7

The NSCCS is particularly keen to attract experimental groups in the solid state area who might be interested in running simulations to help them analyse their results. These groups will be involved in a three-way collaboration with software support and hardware provided by the NSCCS, and additional scientific support provided by the Computational Materials Science group at the Daresbury and Rutherford Laboratories.

Any interested parties should contact the Service Manager, Dr Sarah Wilsey, for more information.

Further information about the NSCCS can be found on the Service website at http://www.nsccs.ac.uk.

Link to national computer centre: NSCCS.ac.uk
magellan computer

According to the statistics above the machine is having almost as much storage space in RAM as it is in the disk storage.

Rolls-Royce and Birmingham University

Rolls-Royce has announcement the expansion of their research efforts at the University of Birmingham. The main remit is modelling of manufacturing processes.

Rolls Royce Press release:

Rolls-Royce and Birmingham University launch manufacturing technology partnerships
22 February 2007

Rolls-Royce, the world-leading provider of power systems and services, today launched two strategic partnerships with the University of Birmingham that expand the work scope of the materials technology centre established there in 1991.

The new partnerships, which will focus on casting technology and process modelling, build on the existing University Technology Centre (UTC) in Materials operated by Rolls-Royce and the university, and marks the establishment of new, leading edge, research facilities and the recruitment of key professorial staff to head the research teams.

Ric Parker, Rolls-Royce Director – Research and Technology, addressing guests at today’s partnerships launch in Birmingham, said: “Modern manufacturing methods are vital to Rolls-Royce in achieving maximum quality while minimising the quantity of often-precious and scarce raw materials. Casting is one of the oldest forms of metal forming, yet there are still exciting new technological developments. The combination of modern manufacturing techniques and computers harnessed to provide effective process modelling is unbeatable.”

Professor Paul Bowen, Director of the Birmingham Materials UTC, said: “The new partnerships will augment our position as a world-leading centre of excellence for the development of key structural materials. Each will also bring a new focus to the training of engineers, technologists and modellers in the science and technology of manufacturing, and will act as a further hub for collaboration with many other research providers. The University, and the School of Engineering, is pleased to underpin these initiatives.”

The casting partnership is established through an initiative jointly funded by Rolls-Royce and EPSRC (the Engineering and Physical Sciences Research Council). It has led to the appointment of Professor Nick Green to the Chair in Casting Technology, and will act as a key supplier of vital casting manufacturing technology to Rolls-Royce, which has five aerospace foundries on three continents.

Its remit is both to develop step-change technologies and to provide incremental process improvements to all aspects of mould making and casting. A highly instrumented production-scale furnace – capable of producing single crystal turbine blades for use in the hottest regions of jet engines – further enhances Birmingham’s world-class investment casting research laboratory.

The process modelling partnership will involve collaboration by the University of Birmingham, Rolls-Royce and the ESI-Group, together with hardware partners IBM, OCF and AMD. This group has agreed to build a technical consortium to perform research in the simulation of manufacturing processes, with emphasis on welding, casting, heat treatment and forming operations but potentially to include other manufacturing disciplines.

A new state-of-the-art computing and simulation laboratory has been set up in the Interdisciplinary Research Centre (IRC) for Net-Shape Manufacturing on the University’s campus. This activity, led by Professor Roger Reed, will focus on modelling the structural behaviour and properties of materials – both during processing and as finished components and assemblies.

Roger Reed

You can also find jobs advertised on jobs.ac.uk for phd projects are Birmingham in their department of Metallurgy and Materials working on the use of high temperature materials.

Roger Reed has recently produced a book on The Superalloys

Empirical Rant

In metallurgy we often term very simple models to be `empirical models’ in contrast to `physical models’. I really wish there was a better name for the `empirical models` – since physical models are more empirical, and `empirical models’ are actually less empirical. Use of such equations can be very useful because they do provide a summary of observations with-in some range of observed behaviour. Even when a physical model exists these simple models are often still preferred because of the ease with which they can be used.

The source of my confusion is the now contradictory uses of the word empirical…

Physical models incorporate more physical understanding, are based on a theoretical understanding. Any theory can only be based on, and validated against, observations. (Edit: i.e. empirical observations)

A better description for our `empirical models’ would be Ad-hoc, make-do, summary or arbitrary.

Comparison of empirical and physical models
This is best described by an example. The martensite start temperature (MS) is often described by an equation of the form; MS = A*XC + B*XMn + C*XCr…

MS(C) = 521 – 353.C – 225.Si – 24.3.Mn – 27.4.Ni 0 17.7.Cr – 25.8.Mo

Another example is the use of various ‘carbon equivilant’s.
Carbon Equivilant = CE = C + Mn/5 + Mo / 5 + Cr/10 + Ni/50

Thomas Sourmail and Carlos Garcia-Mateo have written a paper on prediciton of M_S by various methods,
(Critical assessment of models for predicting the Ms temperature of steels, T. Sourmail and C. Garcia-Mateo Comp. Mater. Sci., 2005:34, p323-334) it is available on Thomas’s webpage;Predicting the martensite start temperature (Ms) of steels.

Ms/ K, all compositions in wt%
[8] 772-316.7C-33.3Mn-11.1Si-27.8Cr-16.7Ni-11.1Mo-11.1W
[9] 811-361C-38.9Mn-38.9Cr-19.4Ni-27.8Mo
[10] 772-300C-33.3Mn-11.1Si-22.2Cr-16.7Ni-11.1Mo
[11] 834.2-473.9C-33Mn-16.7Cr-16.7Ni-21.2Mo
[12] 812-423C-30.4Mn-12.1Cr-17.7Ni-7.5Mo
[12] 785-453C-16.9Ni-15Cr-9.5Mo+217(C)2-71.5(C)(Mn)-67.6(C)(Cr)

Potency of Elements on MS temperature (Change per weight percent).

N C Ni Co Cu Mn W Si Mo Cr V Al
-450 -450 -20 +10 -35 -30 -36 -50 -45 -20 -46 -53 P-1976
  • P-1976 F.B. pickering, `Physical metallurgy of stainless steel developments’, Int. Met. Rev., 21, pp 227-268, 1976.
  • 8 P. Payson and C. H. Savage. Trans. ASM, 33:261-281, 1944.
  • 9 R. A. Grange and H. M. Stewart. Trans. AIME, 167:467-494, 1945.
  • 10 A. E. Nehrenberg. Trans. AIME, 167:494-501, 1945.
  • 11 W. Steven and A. G. Haynes. JISI, 183:349-359, 1956.
  • 12 K. W. Andrews. JISI, 203:721-727, 1965.
  • 13 C. Y. Kung and J. J. Rayment. Metall. Trans. A, 13:328-331, 1982.

Neural network models have been developed to predict both martensite start and bainite start temperatures. It is also possible to calculate these using ‘physically’ based models based on thermodynamics.

They’re all just maths! 🙂

Casino Royalties


The new bond movie, Casino Royale has been released for a few weeks now, and seems to be doing fairly well in the Box office.

Previously I had posted about my neural network model of James Bond Box office takings and predicted the movie should make between 350-500 million USD depending upon the amount of kiss kiss and bang bang in the film. The ‘average’ bond film would make 350 million and a high grossing film should make 350-500 million.


The daily show gave this simple explanation of modelling whilst discussing how weathermen meterologists predict long term hurricane patterns.


  1. Scientists
  2. Feed data
  3. into Computers
  4. and make Predictions.