The operational mineral deposit reconnaissance tends to evaluate its parameters to conduct safe and profitable production. Particular deposit parameters, important from the point of mineral deposit management, are estimated on the basis of observations carried out by mining geological surveys. These observations usually involve sampling, drilling, laboratory analyses and others. The use of fuzzy description to assess the parameters of the mineral deposit was proposed in the paper. In the fuzzy characteristics, an imprecise descriptive description appeared in place of a particular numerical quantity. This approach was used to description of the ore deposit features (metal content, volume, and metal yield) by assigning them specific characteristic functions, whose distributions were based on basic statistical quantities. Characteristic functions can be used to prepare operational strategies for any configuration of required deposit parameters resulting from the production management needs. For this purpose, selected logical operators of fuzzy sets were used. In the next approach to fuzzy modeling, an opportunity to characterize the deposit in a subjective approach was indicated, where the assessment of the deposit parameters is based on rough, in some way, discretionary observation and evaluation. Such model construction enabled the overall assessment of the deposit from the point of view of any parameters. Through the implementation of appropriate inference rules, adequate fuzzy control planes were obtained, which may also be useful in the context of operational mine strategy planning.
The application of artificial intelligence (AI) in modeling of various machining processes has
been the topic of immense interest among the researchers since several years. In this direction,
the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized
to predict various performance measures (responses) and control the parametric settings of
those machining processes. This paper presents the application of fuzzy logic to model two
non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and
electrochemical machining (ECM) processes, while identifying the relationships present between
the process parameters and the measured responses. Moreover, the interaction plots
which are developed based on the past experimental observations depict the effects of changing
values of different process parameters on the measured responses. The predicted response
values derived from the developed models are observed to be in close agreement with those
as investigated during the past experimental runs. The interaction plots also play significant
roles in identifying the optimal parametric combinations so as to achieve the desired
responses for the considered NTM processes.