Modelling
Materials Processing:
An overview
Philip Sargent*, Hugh Shercliff* and Bob Wood#
*Cambridge University Engineering Dept.
Trumpington St., Cambridge, CB2 1PZ England
fax. +44 (1223) 332662
,
#Manufacturing Engineering Dept.
Loughborough University of Technology, LE11 3TU, England
fax. +44 (1509) 267725
Proceedings of International Conference on Computer-Assisted Materials Design and Process Simulation, COMMP'93, Tsukuba, Japan, Sept. 1993.Publ. Iron and Steel Institute of Japan, pages 474-479.
Synopsis: This paper presents an overview of a recent (1992-93) review of materials process modelling technologies performed for the British Science and Engineering Research Council. A more detailed report, including case studies, is available from the authors.
We survey types of models and modelling methods to produce classifications and case-studies in order to compare methods. A particular point of interest is the exploration of the trade-offs between shape-defining finite-element type technologies (finite difference, control volume etc.) and numerically solved analytical methods (e.g. lumped parameter, state variable methods).
We have made a thorough survey of current work and taking the viewpoint of the principles underlying materials properties enables us to produce rough estimates of manpower training requirements to tackle certain classes of problems; and estimates of the degree of likely generalization possible from classes of modelling techniques.
Keywords: process, modelling, finite element, finite difference, microstructure, casting, rolling, deformation, software, environment.
1 Introduction
Many manufacturing processes intrinsically involve the processing of materials, Understanding these processes at some specific level of detail is necessary if predictive models are to be used to optimize the process [Ashby, 1992]. However, while a substantial proportion of research funding is devoted to developing such models, too many projects are a black-hole of research effort which, while successful in their own narrow areas, never produce general, useful results. This study is based on the hypothesis that such non-generalizable projects should be, to a degree, identifiable in advance because of the nature of the materials information that they implicitly represent and manipulate [Sargent, Shercliff and Wood, 1993].
Inappropriate modelling techniques are too often used. Numerical methods are often used when really only qualitative results are required. Finite element modelling in particular is a notoriously all-absorbing activity. We have made case-studies which illustrate which types of process are most amenable to which types of modelling technique.
Conventional research projects in process modelling often intend to produce a general "methodology" for developing models of a particular type, but inevitably the development of a working, useful model for a particular process takes priority. The "methodology" is then abstracted from only a single example, and is asserted but not tested. We present a survey based on many different types of models.
The strength of this review is that it is focussed directly on studying the current state of the development of useful models and is not sidetracked by attempting to produce or to update any specific model of a particular process or class of processes.
If enough resources are devoted to model sufficiently specific materials over a narrow range of properties, then predictive models can always be created. This is not the point. What we would like to be able to do is to know which types of process model are easily generalizable to a wide range of materials, and which models can be applied to a range of "similar" properties.
This paper covers the problem under three headings: modelling, people and software.
2 Modelling: what is it for ?
This paper covers only the modelling of materials processing from the manufacturing engineer’s perspective [Charles, 1992; Cross, et al., 1992; Dean, et al., 1990; Ion, Shercliff and Ashby, 1992; Jain, 1992; Lalli, 1992; Overfelt, 1992; Sellars, 1990; Spilling, 1992; Towers, 1989; Wills and McCartney, 1992], and not the modelling of fundamental physics aimed at predicting materials properties from an atomistic or other materials science perspective [Bhadeshia, 1992; Campbell, 1991; Davis, et al., 1992; Funkenbusch, Lambropoulos and Lic, 1986; Humphreys, 1992; Hunt, 1991; Mehta, 1992; Pettifor, 1992; Saunders, 1992; Shercliff and Ashby, 1991]. We are not surveying models as used as a tool in the course of scientific research. Thus all processing models we consider fall into one of the following problem classes as classified by a commercial need:
Quite coarse models are appropriate for modelling entirely new processes: these can initially be simply based on equilibrium thermodynamics, supplemented later by gross kinetics. These simple models are also appropriate where an established process is to be scaled up. Simple models are needed for providing a first estimate of a trial (usually involving a new shape or geometry)which will then be tested empirically (e.g. new roll sections for shaped bar rolling, new multi-pass weld sequences for novel joint geometries, or moulds for new piston castings). If the shapes are simple then these models are often no more complex than those for new processes, but they are highly constrained and usually model only some small part of the entire process. Such simple models can produce significant savings from just getting an improved first "guess". This does depend on the type of process, many are not as radically affected by variants for different geometries as are welds and castings, and so can be optimized generally at an early stage. The models can then be "compiled down" into process diagrams such as forming limit diagrams [Chan, 1990] or laser surface treatment process plots [Ion, Shercliff and Ashby, 1992]. This requires that the process be understood, however, which may have required qualitative modelling.
Qualitatively understanding an existing process, as opposed to designing a new process or process variant, may require a more complex model if the issue that must be understood is the result of second order effects. Improving quality by reducing variability nearly always requires second order effects to be taken into consideration and the models are more complex, also requiring a greater range and detail of experimental data to provide model parameters.
2.1 Modelling organization
This business issue is also important:
The type of technology is important because "high" technology usually means that more resources are available to set up experiments to provide data for the models, that the materials are more expensive and as supplied have lower variability, and that more expensively trained and experienced modellers are available. Since greater benefits in terms of profits are likely, additional modelling costs can be justified. Deployment of the models either to production engineers or as part of packaged and simplified model-based control systems is not usually so much of a problem since both the process and the staff are already integrated with computer systems, money is potentially available and the procedures of software engineering and associated costs are more likely to be understood somewhere within the organization (though probably not in the processing division).
2.2 Types of prediction required
Some types of defect, such as the size of dirt particles, occur at the tail-ends of probability distributions and these are not individually predictable. However, predicting the occurrence or non-occurrence of a systemic type of defect is possible if there is a "criterion", a continuous variable which predicts defects if it is above some critical value. We can distinguish two types of systemic defect: those for which the modelled "field variables" (temperature, strain etc.) have an immediate, direct influence (they appear in the formula for the criterion) and those produced by the secondary effect of those fields on some internal microstructure (such as grain growth above a certain size). In reality there is a continuum between these extremes: simple criteria are easy to use but may not be accurate, complex criteria may be entire models in themselves and can require iterative coupling with the rest of the model.
Criteria do not have to have the same behavioural form as any "real" variable, they only have to be monotonic since the critical value for defect prediction can often be arbitrarily set or calibrated from experiment. Models for real variables which can (potentially) be measured, such as strain, crystal growth rate etc., have to evolve at least semi-quantitatively in the correct manner, a monotonic correlation is not good enough. Devising and calibrating such models is very difficult because microstructure tends to be affected by nearly every physical effect in the process. Thus, surprisingly, it is often much easier to predict the appearance of defects than to predict the numeric level of some microstructurally related feature such as grain size,or percentage recrystallization. Thus macroporosity in shaped castings is easily predicted for simple shapes by various "modulus" or "heat centre" ratios [Viswanathan, Sikka and Brody, 1992] and microporosity by the Niyama criterion [Overfelt, 1992].
Thus the industrial context and purpose of the process modelling task already strongly constrains the appropriate types of modelling methods. The type of process of course determines the modelling methods within those constraints.
3 Modelling: what is important ?
Given the constraints of the type of organization attempting the modelling and the types of results required, the major tasks are deciding what modelling methods are appropriate.
3.1 Analytic and mesh-structured models
One of the most important aspects is deciding when it is adequate to use analytic algebraic equations and when it is necessary to construct a discrete meshed model, e.g. for finite element or finite difference calculations. It is always necessary to do some non-meshed symbolic or analytical modelling in order to decide on appropriate boundary conditions for the meshed part of the problem.
There are only four reasons why a meshed method might be appropriate:
The first reason, complexity of shape, is very common in engineering application; the second is found more commonly in pure research; though it is used in some texture studies. Network models of grains, such as the Trondheim "Avrami machine" (while, strictly speaking, not a mesh), are invariably found at the "pure" end of research [Humphreys, 1992]. The third problem, discontinuities – such as a phase change in the material, can be handled easily if inelegantly with meshes using a volume-of-fluid (VOF) technique (e.g. [Wang, et al., 1993]).The fourth problem with boundary conditions often occurs in practice where, for example, the heat transfer coefficient is a non-linear function of temperature (often having a maximum at a particular temperature). This renders a closed-form analytical solution almost impossible – or requires many terms of a series expansion to be managed which approaches the complexity of a meshed formulation anyway – even for simple geometries.
Whether the shape is really complex enough to justify meshed models is a difficult decision because one does not usually know a priori whether some small geometrical feature is significant or not. In adition, the "shape" determining a materials processing operation is often not a physical shape at all, but the "shape" of a heat field. If a heat field can be treated as semi-infinite and highly symmetric (even if the physical material volume is not) then an analytic model can often be entirely adequate.
The "shape" can also depend on the material itself. When modelling heat affected zones from typical welding or laser surface treatments, the thermal conductivity of steel is sufficiently low that the heat source can be modelled as a line source and only a 2D section need be modelled as a semi-infinite plane, perpendicular to the plate and perpendicular to the direction in which the heat source travels [Ion, Shercliff and Ashby, 1992]. For aluminium alloys, for the same process, conduction is fast enough that metal ahead of the beam "sees it coming" and also conduction through the thickness of the plate means that top and bottom surfaces have almost the same temperature. In this case a different 2D approximation to the thermal field is valid: infinite in all directions, but in the plane of the plate. The same distinction between aluminium and steel is found in continuous casting: heat flow is radial (outwards, from a long molten pipe) for steel, but longitudinal (along the strand) for aluminium.
Analytic models are always useful to distinguish between mechanisms that have to be modelled as a coupled set and mechanisms that can be modelled separately; thus heat diffusion problems often have characteristic time constants which are orders of magnitude smaller than solid state atomic diffusion problems (e.g. for carburizing or nitriding treatments for steels). Careful attention to length scales is useful in determining when a mechanism can be thought of as operating in a continuum, and thus being susceptible to analytic modelling, and when spatial distribution must be modelled explicitly by a meshing method.
Analytic models used to require closed-form algebraic descriptions so that the parameters of interest could be solved for directly; but that is no longer the case. Simple computer programs can track evolving solutions and iterate to find solutions to implicit formulations on very modest personal computers indeed [Ashby, 1992; Fleck, Kuhn and McMeeking, 1992; Ion, Shercliff and Ashby, 1992; Shercliff and Ashby, 1991]. However, analytic models still require simple geometries and uniform boundary conditions.
3.2 Boundary conditions and multi-physics models
Determining the boundary conditions of a novel processing modelling problem is a major part of the task. Boundaries are not just geometric shapes, they are also statements of symmetry, continuity and constancy of temperature, heat flux, strain, stress or humidity fields with respect to time or any other field value (e.g. heat transfer coefficients are often a function of temperature).
Analytic models require the modeller to make a very early committment to specific boundary conditions because they control the entire form of the analytic description and do not appear in the model euations themselves. Meshed models, however, require explicit representation of boundary conditions separately from the model equations, and they are decoupled to some extent from the "model" itself.
Thus meshed models can change boundary conditions at a much later stage than analytic models, and nearly all of the model can be re-used with different conditions. In practice this is vital because in practice sensitivity analysis is required over different boundary conditions and not just over different parameter values; which for analytic models usually requires a complete new modelling effort for each trial.
Numerical meshed models are strongly affected by different boundary conditions but the effect takes place in the solving engine of the algorithms. This is also where different kinds of physics have their impact. Fundamentally, meshed models solve sets of partial differential equations (PDEs) and different types of physics: diffusion, flow, radiation etc., mean that different terms can be dropped from the PDEs. Thus, for example, the diffusion equation is a specialization of the lossy wave equation. Each type and sub-type of PDE formulation coupled with a particular type of boundary conditions and mesh-type can be "best" solved by a different algorithm; where "best" involves robustness, accuracy and speed.
Therefore intelligent use of numerical meshed models requires intelligent selection of software package (and solver algorithm), which is intellectually less difficult than constructing a solution from first principles. This has significant implications for education and training, and also for guiding development of software appropriate for such people.
3.3 Modelling accuracy
Practical manufacturing problems often occur at the tail-ends of statistical distributions, at the 100s of ppm level, from overlapping distinct processes (e.g. in solder reflow of surface-mount electronic devices on printed circuit boards where placement error and solder volume error combine). If there are 20 steps each with 96% success then overall productivity will only be 44% (0.9620=0.44) but a model to study such a problem will have to be much better than 96% accurate. Being at 96% of the target property might not denote failure however, the target behaviour might be robust to the ±5% level. This kind of problem is especially significant in processes which are basically repetitive, such as lithography or soldering for electronics, multi-pass metal rolling or hammer forging.
Materials processors often assume that theirs is the only process relevant and forget packaging, painting, cleaning etc. These can initiate or aggravate materials mechanisms which, on their own, would cause no problems in the process. This can sometimes be used to great advantage, as is the case with a recently developed aluminium alloy for automotive body panels which is soft during pressing (reducing spring back) and which develops full strength only on passing through the paint curing oven.
3.4 Materials parameters
Any model of a materials processing operation requires numeric parameters describing the relevant properties of the material being modelled. Acquiring these data is almost always expensive and difficult [Charles, 1992; Jain, 1991]; using the data appropriately is also not straightforward. Sensitivity analysis of any model, with respect to variation in the values of the materials parameters is always necessary because of the inevitable uncertainty in their values. In addition, because of the possible inaccuracy or slight inappropriateness of the model itself, the qualitative behaviour of the model with respect to parametric variation is often of much more use than the simple numeric "answer" produced by the model when "fed" the best estimates of the materials parameters.
There is only one exception to the requirement for sensitivity analysis and that is when separate models exist for both the upper and lower bounds of the materials behaviour [Cocks, 1993]. In this case, however, one must be careful to put the appropriate high or low estimates of the parameters to each bounding model. If there is only one parameter this is straightforward; but if there are many parameters, and the model is non-linear, it is often extremely difficult to predict whether increasing or decreasing the value of a particular parameter will have a positive or negative effect on the final modelled behaviour. In such cases, sensitivity analysis is again necessary.
In many cases, taking the complete set of upper and lower limiting estimates of a set of, say, 6 parameters, leads to such wide bounds on the modelled behaviour that the model appears to be of little use and there is a strong temptation to perform no sensitivity analysis at all but instead to merely use the "central" estimated parameter values. This is often due in reality to strong correlations between parameters, e.g. between the melting point and activation energy for diffusion, or between the Dorn "constant" and the power law index in the case of power-law creep. If these correlations are ignored when deciding on parameter variation for a sensitivity analysis, then the results will be unrealistically divergent.
Studying the sensitivity of a model to variations in one materials parameter at a time is a common method for developing understanding and insight into a processing operation. However it must be appreciated that this is a very tentative form of understanding, and modelling in an industrial context which leads only to this type of insight is rarely cost effective [Herbertson and Austin, 1993]. The reason it is such a poor form of understanding is that even the qualitative effects of a single parameter are only valid for entirely linear systems: an entire Taguchi-style matrix of sensitivity experiments is required for non-linear systems [Srinivasen and Chaudhary, 1990].
3.5 Generalizable models
It is the discrete differences in assumptions and types of approximations between different modelling problems that makes generalization difficult, not the differences in continuous parameter values. Thus the type of materials information used in a model only indirectly determines whether a model can be generalized. Thus a 2D approximate model of heat flow around a laser weld in a steel plate can be wrong for the same weld in an Aluminium plate because the assumption about the relative speeds of thermal diffusion and weld velocity becomes invalid. It is this assumption which depends on the thermal conductivity values.
Although it appears that we can draw no completely general conclusions about the effect of material information type on the breadth of a model’s applicability, nevertheless certain correlations do occur fairly frequently. Thus surface properties: wear rates, friction coefficients or corrosion rates, are highly variable for otherwise similar materials under similar conditions. Such high variability implies that the model is quite likely to be pushed into a regime where the original assumptions become invalid. Thus in rolling sheet it is the thickness of an oxide scale which determines whether the heat transfer coefficient between roll and sheet even enters the model at all. Fo thick oxide scales the heat transfer coefficient is irrelevant but for thin scales it must be included in the model.
4 People: education and training
In Britain, and also apparently in most of Western Europe and America, there is a shortage of modellers trained in both a breadth of materials science and metallurgy, and a depth of analytic and numerical analysis. There is apparently no shortage of expertise in narrow numerical and mathematical skills appropriate to devising new or improved formulations of the finite element method; but these are rarely coupled with adequate background in understanding the physical processes which actually cause the behaviour being modelled.
These problems are exacerbated by the strong tendency for undergraduate students on general engineering degree courses to choose materials engineering options if they are weak in numerical analytic ability. In addition, the numbers of bright school students choosing to study materials science/engineering at UK universities has been dropping over much of the past decade and shows no signs of recovering, though this is not the case in the USA. Students who choose these courses are also traditionally weak in mathematical ability although some materials departments are now producing more mathematically challenging courses. Even where numbers remain adequate this is sometimes at a cost of reduced entry standards. Government policies requiring universities to increase cost effectiveness can have unintended side-effects, especially on interdisciplinary subjects where a smooth combination of undergraduate teaching and postgraduate research is required. Lower, or non-traditional, qualifications on entry, however, do not necessarily imply less able or less appropriately trained graduates leaving the universities.
In America, the lower level of education (compared with Europe or Japan) in both basic science and mathematics on entry to undergraduate courses means that nearly all the training relevant for materials process modelling must take place at the post-graduate level. This is a strength because such courses can be more highly focussed, but also a weakness because the breadth of background is lost. In the UK, post graduate education in this area would take the form of distinct one-year MSc. courses, but there are very few specializing in materials process modelling and industry is not sufficiently well organized in modelling yet to provide a "customer pull" to the graduates of such courses. It is not even clear whether such courses should be based in materials engineering departments or, if they are, whether an intake of graduate from physics or chemical engineering would be more appropriate for industries’ needs than materials scientists.
The critical manpower shortage is for people able to critically evaluate a range of possible modelling options and pick the most appropriate for the problem in hand. This requires, as described above, the ability to perform intelligently the analytic modelling and to set up boundary conditions properly. Awareness of the industrial and business problem context is almost completely absent from process modelling education (where it exists) which concentrates on numerical methods.
The finite element approach and other meshing methods are usually the default techniques not because they are the best, but because the modeller is not capable of posing or solving the appropriate analytic model based on the materials science and physics of the problem. The software system leads the modeller through the modelling task by imposing a procedure (which may be inappropriate to the problem) which untrained modellers do not otherwise know how to begin. The essence of modelling skill is knowing how to manipulate assumptions about constitutive laws and boundary conditions appropriate to the physics of the situation.
Finite element models also have the great advantage of producing some kind of visual "result" within a defined period of time, whereas if a modeller attempts to derive an analytic model and cannot find a reasonable description, then there will be no result at all. The ease of producing complex coloured graphical results from a numeric simulation, compared with a handful of numbers from analytic equations, should not be underestimated as a determining factor. Modellers work in organizations, and reward for a job performed well is dependent on impression and communication as well as accuracy and validity [Handy, 1993].
4.1 Manpower costs
University research teams are not always appropriate as the primary point of contact for a commercial company wishing to model some practical processing problem. University researchers are in a situation which rewards specialization on a particular technique or class of techniques, which may be only partially appropriate for the company’s process. If the company has little modelling expertise (which is the case for the vast majority of companies), then this fact is not appreciated by any of the parties concerned. This is a direct result of the lack of broadly trained materials process modelling engineers and the small size of most academic research teams in this type of work. Larger departments expose academics to a wider range of modelling techniques, especially if they pool their programming teams.
The major fault is not with the research academics however: their task is and should be the development of new and improved specialized techniques, which is not aided by a broader appreciation of what is feasible with unrelated methods. The practical application of modelling technology would appear to be best done through shared industry research and development laboratories, trade association laboratories, large consultancy companies or an equivalent of the 60 or so German Fraunhofer Institutes or the 170 Japanese Kohsetsushi (public testing laboratories).
As far as the cost of modelling a processing operation is concerned, there seems wide agreement that it is dominated by manpower of analysis and programming (80%), excluding the cost of acquiring materials parameter values (which can be very high if measured from a fully functioning plant which would otherwise be producing product). Szekely estimated a total cost of $120 000 for a reasonably complex model taking one man-year to develop [Szekely, 1988]. The range was from $20-30 000 for "a simple exercise" to many millions for a "really complex model" (there are complementary costs in physical modelling, pilot plant work and plant-scale experimentation). Szekely’s estimate was made in 1987 but is reckoned to be much the same now:
Staff in the UK are paid less but computers are relatively more expensive compared with the USA. So an annual per-person cost in the UK of $40-50,000 (for a member of a company’s internal process modelling team) would be approximately equally split between salary, computer systems and general local support but not including any company-wide overheads. Costs would be approximately doubled for contract or consultant modellers (more than $500 per day). Hardware for process modelling is apparently not an issue. Costs are dominated by salaries, software and support.
5 Software, environments and programming
Models now are much more computationally intensive than was the case only a few years ago, so there is a real step-change in improving the representation of real processes in the models. (Being closer to reality, "multi-physics" modelling, needs more number crunching). Doing calculations on many different physical processes will also change the current balance between compuation spent on remeshing and computation spent on calculation: it will become cost effective to put more effort into changing the mesh size dynamically during the solution of the problem so as to minimize the number of physics calculations. (Remeshing multi-physics problems, where each type of physics has its own criterion for triggering remeshing, is currently a research issue.) This implies that the current situation where "solvers" and "meshers" are produced largely by different companies will have to change.
Most industrial modelling uses standard packages, many are customized by the suppliers to a specific need. Some projects require extra code writing in C (or fortran), sometimes because the data handling in the standard package is inappropriate. In the future, the ability of code to be run on parallel processors will be become critical. This will push programmers writing this extra code towards using languages such as High Performance Fortran which implicitly support parallelization without making the programming very much more complicated. A recent, critical facility is the ability to do real-time monitoring of a big simulation so that a modeller can abort it if something goes wrong. Once tuned, it is run overnight [Szekely, 1988].
Comprehensive finite element packages are perceived as being clumsy to use and there is a high initiation cost in training someone to use one at all. Specialist packages tuned to a particular process or solver are easy to get started (easy to get the input data in), but may well need modification to solve the correct problem. They usually just use one specialist type of solver, e.g. a radiative heat transfer solver, or a transmission-line matrix viscosity solver. A large company’s modelling team will typically use many of these, rather than try to tie all the solvers into one comprehensive environment comprising pre- and post-processors and data visualization tools - though that is the direction towards which large companies are attempting to move.
Packages tend to be licensed per "seat" or by processor. Systems to manage multiple use over a network are rudimentary but improving rapidly (more rapidly with personal computers than with workstations). However, multiple versions for different computers often require different releases of system software; which means that a modelling team’s systems are permanently out of step in any "open system" comprising computers (& system software especially) from different manufacturers. Integration and data management is thus a very real current problem and this is intimately connected with the difficulty of ensuring either that the solver appropriate for a modelling problem is available for the companies’ standard environment, or that data passed to several distinct packages is kept consistent.
Clearly the software situation is unsatisfactory. There are more important ramifications however: the need to become a specialist in one particular type of package or class of algorithms means that there are many fewer people with expertise in any particular range of areas. This means that collaboration and teamwork – which is always necessary in a modelling project – is done badly for two reasons: (a) because it is less likely that the appropriate expertise is in the team, and (b) the team members individually are less used to working outside their specialization. Thus attention to producing better, more modular, software environments [Wood, 1992] will have benefits in both manpower availability and interdisciplinary abilities.
5.1 Hardware entry costs
Although packaged software costs alone are much greater than hardware costs, and costs of software written in-house are greater still, the transition from personal computer (PC) hardware to workstation hardware is still perceived as a significant entry cost. This is despite the fact that a fully-configured PC with adequate disk-space, memory, screen and processing power is almost invariably more expensive than a workstation which is actually more powerful.
There are two reasons for this entry barrier: first, PC hardware can be bought incrementally over a period of months as required and can be justified on an item by item basis by modellers already producing useful results. Second, workstations use the Unix™ operation system which has very significant training costs for the system manager. The former is likely to persist but will be less significant as prices will be lower. The latter has already changed with the recent sale of Sun’s Solaris™ easy-management version of Unix and will change even more from 1993 as Windows NT becomes available on workstations as an alternative operating system.
6 Recommendations and conclusions
The most obvious conclusions from this study concern education and training. In Britain neither the undergraduate nor postgraduate courses appear to be producing the people able to use the new modelling tools produced over the past decade. Although our study is not yet complete, the same difficulty also appears to be relevant in North America. Coupled with the cheapness and ease of use of current finite element modelling packages (when specialized for a particular purpose) this is extremely worrying because it implies that a great deal of superficially convincing, but fundamentally flawed, materials process models will be produced during the next decade.
Modelling materials processing is seen as a strategic commercial issue in America, Europe and Japan; but most public money appears to be directed towards research and technology development demonstrator projects or towards developing more robust or efficient solver algorithms for existing or new processes. Our review has shown that for large companies in high technology industries these are indeed important for competitiveness (though perhaps only slightly more important than the data management and multiple software version problems).
However, for significant impact on manufacturing industry as a whole, several other issues are much more important: manpower training, well-integrated software environments and a great deal more help in setting up problems – perhaps on a much more specifically industry by industry or process by process basis than hitherto. (At a very fine level: certainly different software for pressure die casting compared with aluminium alloy sand casting, and probably different packages for aluminium as opposed to zinc die casting). The UK "CAST" initiative intended to introduce simple simulation models into the many small companies of the largely uncomputerized UK aluminium alloy shape casting industry is a significant step.
One idea that could help progress the fundamentals of process modelling skills would be the establishment of a UK centre which could form a focus for the interdisciplinary research specifically in modelling materials processing and be a reference point for a materials data repository (as has been suggested for the USA [Jain, 1991]). This centre could take a form along the lines of the Laboratoire de Méchanique et Technologie at the École Nationale Superior de Cachan (Paris 6) or the Centre for Process Simulation and Control at Imperial College (both of which could claim to be doing this research already) where there are semi-permanent research teams, or it could take the form of the Isaac Newton centre for mathematics at Cambridge where there is a very small permanent staff and research is carried out in 6-month projects by selected groups of visitors. This last option is particularly attractive since the permanent staff could offer expertise in a broad range of computational skills and the constant throughput of visitors would widely disseminate the expertise generated.
7 Acknowledgements
This review of modelling methods would never have been possible had not so many researchers and model developers given us so freely of their time and been so candid in their opinions. We are extremely grateful to all our colleagues and interviewees who are far too many to mention. We are particularly gratified that even in an area so close to industrial application as this is, our colleagues in industry have nevertheless felt able to be extremely supportive and informative. This work is supported by travel grant GR/H2412 from the Applications of Computers to Manufacturing Engineering (ACME) directorate of the British Science and Engineering Research Council (SERC).
last edited:
Sargent/Wood/Shercliff Friday, May 7, 1993
converted to HTML: Monday, June 23, 1997
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