[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121748-en":3,"doc-seo-121748-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121748,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Cutting Force Component-Based Rock Differentiation - Utilising Machine Learning - Accepted Dissertation","This dissertation evaluates the possibilities and limitations of rock type identification in rock cutting with conical picks. Machine learning is combined with features derived from high frequency cutting force measurements to characterize cutting responses. Using linear cutting experiments, boundary layers can be identified with a precision of less than 3.7 cm by the developed program routine. The study also demonstrates reliable identification of rocks weakened by cracks and highlights the relevance of anisotropic rock behavior.","Cutting Force Component-Based Rock Differentiation  \nUtilising Machine Learning  \nFaculty of Geosciences, Geotechnics and Mining Institute of Mining and Special Civil Engineering Chair of Surface Mining  \nTechnical University Bergakademie Freiberg  \nAccepted Dissertation  \nTo Obtain the Academic Degree of Doctor of Engineering  \nDr.-Ing.  \nSubmitted by: Bruno Grafe  \nBorn 18.04.1987 in Dresden Freiberg, 08.06.2022  \nReviewer:  \nProf. Carsten Drebenstedt Prof. Nikolaus A. Sifferlinger Prof. Dragan Ignjatović  \nAwarded on: 09.12.2022  \nI hereby declare that I completed this work on my own without receiving any improper help from a third party and without using any aid other than those cited. All ideas derived directly or indirectly from other sources are identified as such.  \nI also confirm that I did not seek the help of a professional doctorate consultant and no persons received payment from me for any work done for me. Neither this thesis nor any parts thereof have been previously submitted for formal assessment in the same or a similar form at this university, nor anyother.  \nBruno Grafe  \nFreiberg, 08.06.2022  \nFirst and foremost, I would like to thank Prof. Carsten Drebenstedt for providing financing, guidance in project acquisition, and his broad experience—as well as providing the research infrastructure which makes rock cutting research at Technical University Bergakademie Freiberg (TU BAF) possible in the first place. I also thank him for his input and opinions that improved the accessibility of this work. Also, I am grateful for providing me with a very diverse work environment that gave me important life experiences.  \nMy secondary supervisor, Prof. Nikolaus A. Sifferlinger from University of Leoben (MU Leoben), provided his expert input in the field of rock cutting research and shared his experiences in the rock excavation industry. By that, he could point out possible problems which allowed tackling them early on, for which I am very grateful.  \nI further thank Dr. Taras Shepel from TU BAF for being a very professional companion over the years, for his valuable input in discussions as well as his opinion on my work—but also for being a moral support along the way.  \nDr. Phillipp Hartlieb indirectly incited the idea on this work during our joint experiments on granite treated with high-power microwave radiation. I am also very grateful for the direct professional input during these joint experiments between MU Leoben and TU BAF that resulted in three publications and form a part of the database used for this work.  \nI thank Max Bögl Schotterwerk & Natursteinhandel Dörfel who made it possible to take samples in their operating quarry and supported us to extract and transport ca. 1.5 t of sample material.  \nAdditionally, the team from the young research group InnoCrush must be mentioned. In this group around the Professors Drebenstedt, Heide, Lieberwirth, Konietzky, Rehkopf, and Bongaerts, highly selective process chains in mining were investigated. Parts of this study were investigated within the Framework of the InnoCrush project. Furthermore, I thank Prof. Mischo from TU BAF and Ralph Schlüter (Heitkamp construction Swiss), who donated lumps of sphalerite-galena ore that was used for the case study in this work. The ore was a by-product of the BHMZ project “Design, implementation and operation of an underground in-situ bioleaching research and testing unit at the “Reiche Zeche”research and educational mine”.  \nWolfgang Gaßner and Tilo Tobies from TU BAF supported me with their practical experience at the cutting test rig. Without that support, the actual experiments would not have been possible. Wolfgang Gaßner also supported me with his knowledge in project financing, for which I am very grateful.  \nIn addition, I thank Dr. Andreas Lemm and the late Hans Leppkes from Caterpillar Lünen, who provided valuable insights based on a lifetime of work in R&D in the mining equipment manufacturing industry.  \nI thank Dr. St","cbCaipDaKzzo6ChP","https://ap.wps.com/l/cbCaipDaKzzo6ChP","pdf",18226827,1,226,"English","en",105,"# Accepted Dissertation\n## Declaration and Personal Acknowledgements\n## Research Evaluation of Rock Type Identification","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It evaluates how well rock types can be identified during rock cutting with conical picks, including both possibilities and limitations.\"},{\"question\":\"How is machine learning used in the study?\",\"answer\":\"Machine learning is applied using features derived from high frequency cutting force measurements.\"},{\"question\":\"What accuracy is reported for boundary layer identification?\",\"answer\":\"Boundary layers are identified with a precision of less than 3.7 cm using the developed program routine.\"}]","Cutting Force Component-Based Rock Differentiation - 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