Statistical Analysis of Rock Cutting Forces Acquired from Full-Scale Linear Rock Cutting Machine Tests

Understanding rock-cutting forces encountered in the field requires full-scale rock-cutting tests with original tools. The most reliable method that accounts for critical aspects of rock-cutting technologies is a full-scale linear cutting machine (LCM) test. The cutting force data acquired through this test sheds light on how the rock and cutter interact, as well as how different cutting geometries and tool variations perform in different geologic formations. Peaks in recorded forces during the full-scale cutting represent the points of a major failure of rock leading to chip formation. This study utilizes a statistical approach to conduct a thorough analysis of cutting force data collected during full-scale LCM testing. As an alternative to the calculation of average or peak cutting force values, the emphasis is on establishing a statistically derived range of forces to assess the cutting force data for performance prediction. The proposed methods and existing methods along with literature were used to model selected machines and compared with field data for validation. 1. INTRODUCTION A full-scale linear cutting machine (LCM) test is the most reliable method for the evaluation of rock cutting parameters, as it uses large-scale rock samples that closely represent the rock mass character and force requirements in the field (Cho et al., 2013; Dogruoz et al., 2016; Rostami, 1997; Thyagarajan, 2018; Yasar & Yilmaz, 2018). While full-scale LCM tests provide the most realistic rock cutting data, real-world simulation techniques that cannot be combined with other testing methodologies, such as advanced numerical modeling, can be examined (Jeong et al., 2020). The emphasis on full-scale testing arises from the fact that rock is a heterogeneous media with micro/macrostructures, and its behavior is greatly affected by scale (Balci & Bilgin, 2007; Bilgin et al., 2006; Thuro & Plinninger, 2001). Thus, the full-scale LCM test remains the preferred methodology for understanding real-world rock cutting efficiency despite formidable logistical challenges (Balci & Bilgin, 2007; Bilgin et al., 2006; Pan et al., 2018; Rostami, 1997).