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Computerised out-of-class exercises

Dan Phelan
Chisholm Institute of Technology

Students' access to, and knowledge of material databases can have an enhanced effect on the teaching of materials to engineering students. Most engineering subjects taught at tertiary level are usually mathematically based and students are normally familiarised with lecture material via mathematically based tutorial problem solving. Engineering Materials as taught at Chisholm is mainly non-mathematical and, as such, easy to set mathematically based tutorial problems are not generally applicable for reinforcing theory.

It is proposed that computerised materials databases can be used in a variety of ways to both reinforce lecture theory and acquaint students with the vast spectrum of material property data available. In fact, it can be argued that the teaching of engineering materials cannot be effectively performed without students having sufficient access to materials databases. To develop an appreciation of the applications of the theoretical concepts developed in Engineering Materials lectures, students need the feedback associated with materials databases.

Commercial materials databases

Engineering students at Chisholm use the materials database of the Australasian Institute of Metals and Materials (IMMAMAT) (Phelan, 1988), which has been developed by the author. This database contains over 500 generic alloys and over 500 internationally cross-referenced standard alloys. It occupies about 1.5 MB of disc space. The students are set exercises which require them to become familiar with the basic concepts of the material defining parameters used in the database.

Immamat student exercises

1. A simple exercise

Selection of a material to make an axle. The axle is to have a specified strength.
Even for this apparently simple exercise, a variety of tasks are required of the student. Some of these tasks are:
  1. Finding alloys in the data base which are described as being suitable for axles or shafts or spindles etc. That is, the student may need to think of other descriptors or components that could have similar functions.
The software used to construct the database contains a pattern-matching algorithm which allows for searching through the alloy text fields to obtain approximate matches to the search string. For instance, if "corrosion resistance" was being used as the search string, then an alloy described as "corrosion resistant" would be found by the search.
  1. Find alloys that have strengths equal to or greater than that required. The database allows for searching using minimum and maximum mechanical properties.

  2. Choose which type or types of alloys to search through; cast iron, heat treated steel, wrought aluminium etc.
Irrespective of which search path is chosen, several solutions will be presented and the students will be required to make a selection which will involve the use of theory gained from lectures. In the above example, steels having a variation in strength with section size will be presented to the student. The significance of this will need to be interpreted by the student. In fact, it is the author's experience that even though the theory associated with variation of strength with steel section size has been covered in lectures, most students will query it when the data is presented to them within the environment of what they perceive to be a large industrial metals database.

That is, they are impressed with the presentation, feel it is part of the real world as opposed to the unreal world of the academic classroom and subsequently take more notice of it. It may be, of course, that in being allowed to work at their own pace, as opposed to keeping up with the delivery of lecture material, they are in a much better position to assimilate and query the data being presented to them. Some students do remember or have studied their lecture notes and the presentation of the computerised data helps to reinforce their knowledge.

The above is a simple example of a material selection problem and the level of difficulty of the problems set may be varied to suit the standard expected of the student.

2. A more demanding exercise

A material is required for the manufacture of a light weight complicated section. The light weight section is to operate as a sliding mechanism at a variety of temperatures.
The student is required to translate the above functional requirements into the following material property requirements
  1. light weight - aluminium, magnesium or titanium alloy

  2. complicated section - casting alloy required

  3. sliding mechanism - wear resistance advantageous

  4. varying temperature - low thermal expansion to prevent jamming
Again, a variety of search techniques can be used by the student who would be expected to find aluminium casting alloys with relatively low thermal expansion and relatively high wear resistances. These alloys would be suitable for the manufacture of internal combustion engine pistons.

3. Cross referencing of specifications

A common industrial engineering materials problem occurs when specified standard materials, Australian or international, are not available locally. Commercial databases may supply other equivalent materials.

Students can be given Australian or International standards and be asked to find equivalent alloys.

Non commercial databases

Programmes have been developed that randomly simulate material selection problems requiring an input from the student. The programme uses a materials database which the student is expected to be vaguely conversant with. Properties are randomly generated and displayed. The student is then expected to input a material which will satisfy the displayed properties. Some materials are more acceptable than others and receive a higher score. The programme has a game format and subsequently presents a challenge to students to score as high as possible.

Students are given a broad outline in lectures of the material properties involved in the database and are then expected, through library research etc., to obtain the specific knowledge required to input suitable materials to the programme. For example, after a series of lectures on stainless steels, students would need to consider the chemical composition of the steels in the data base to rank them in order of corrosion resistance. Amongst other properties, the programme will ask for a steel having a certain minimum corrosion resistance. Students receive a score for their overall game performance and this score is recorded against their name on the floppy disc containing the programme.

At the end of the exercise the disc is returned to the office and the student's scores are recorded. Suitable encrypting of scores, etc, prevent any tampering with results. Students seem to go out of their way to complete the exercises and may be motivated by the need to see a score recorded against their name on the floppy disc which is to be returned to the office. A more detailed description of this and other similar programmes has been given elsewhere (Phelan, 1989).


Phelan, D. (1988). Institute of Materials and Materials Australasia Databank. Materials '88 Proceedings. London: The Institute of Metals.

Phelan, D. (1989). Using databanks in computer assisted learning. In J. Hones and M. Horsburgh (Eds.), Research and Development in Higher Education, Vol 8. Sydney: HERDSA.

Please cite as: Phelan, D. (1990). Computerised out-of-class exercises. In J. G. Hedberg, J. Steele and M. Mooney (Eds), Converging Technologies: Selected papers from EdTech'90, 27-29. Canberra: AJET Publications.

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