The simulation of manufacturing processes and manufacturing systems using c
omputers has been carried out for several decades. The use of discrete even
t simulation models for production systems, material planning, machine grou
ping and various other key problems of resource allocation have been well r
esearched and a number of ready to use commercial products are available. I
n recent years, the use of artificial intelligence simulation and modelling
techniques for certain manufacturing processes have led to significant adv
antages over conventional techniques. However, the benefits of using AI tec
hniques have not been fully exploited in existing simulation systems. Owing
to the different way that some AI techniques process or represent simulati
on data compared to conventional mathematically based methods, it is not al
ways easy to include AI techniques in existing simulation systems with thei
r inherent structures for representing simulation data. in this paper, a co
ncept which allows the use of AI techniques such as artificial neural netwo
rks, genetic algorithms and fuzzy sets as well as traditional mathematical
techniques for the simulation and modelling of certain manufacturing proces
ses is presented and discussed. Methods for the exchange of simulation data
are demonstrated using the milling process as an example. Furthermore, the
notion of distributed simulation and the determination of essential proces
s parameters by remote access to common simulation databases using a client
/server model has been reviewed.