Dursun, Arif Emre2020-03-262020-03-262018Dursun A. E. (2018). Performance prediction of chain saw machines using schmidt hammer hardness. Scientific Mining Journal, 57(1), 25-33.2564-7024https://hdl.handle.net/20.500.12395/37227Schmidt hammer hardness (RL) provides a quick and inexpensive measure of surface hardness that is widely used for estimating the mechanical properties of rock material such as strength, sawability, cuttability and drillability. In this study, RL as predictors, which is thought to be a useful, simple and inexpensive test particularly for performance prediction of chain saw machine (CSM), is suggested. This study aims to estimate CSM performance from RL values of rocks. For this purpose, rock cutting and rock mechanics tests were performed on twenty four different natural stone samples having different strength values. In this study, Chain Saw Penetration Index (CSPI) has been predicted based on RL which is one of the two models previously used for performance prediction of CSMs. The RL values were correlated with UCS, CSPI and SE using simple regression analysis with SPSS 15.0. As a result of this evaluation, RL has a strong relation with UCS and SE. It is statistically proved that the model based on RL for predicting CSPI is valid and reliable for performance prediction of CSM. Results of this study indicated that the CSPI of CSMs could be reliably predicted by empirical model using RL © 2018 Union of Chambers of Engineers and Architects of Turkey. All Rights Reserved.eninfo:eu-repo/semantics/closedAccessChain saw machinesRock cutting testsSchmidt hammer hardnessSpecific energyArticle5712533Q4