Cerchar abrasion index (CAI) is commonly used to represent rock abrasion for estimation of bit life and wear in various mining and tunneling applications. The test is simple and fast, but there have been some discrepancies in the test results which are related to the type of equipment, condition of the rock surface, operator skills, testing procedures, and measuring the wear flat. This paper focuses on the estimation of CAI and investigates the impact of various parameters on that. Results of a limited Cerchar tests on a set of rock samples from different laboratories are analyzed to correlate rock properties data to CIA value, which every value indicate an abrasiveness classification. As a result of a literature review, it is concluded that the abrasiveness of a rock sample based on the CAI value is strongly correlated with uniaxial compressive strength (UCS) and brittleness of rock samples. Rock brittleness is a function of UCS and Brazilian tensile strength (BTS). Thus, collected data of these parameters were hired to develop and train artificial neural networks (ANN) as an artificial intelligence (AI) method for estimation of drilling tool wear using data of rock strength and brittleness as inputs. It is pursued by the application of pattern recognition which is achieved by ANNs.

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