No description available.

No description available.

No description available.

The aim of this thesis work was to design and evaluate a wear test method for cemented carbides inserts used in rotary drilling. An appropriate in-house wear test method would provide a better understanding of the wear mechanisms limiting tool life in real drilling. The test method should be easy to use and be able to distinguish between wear of insert materials with different microstructure and properties. The literature study showed few published articles about wear tests and mechanisms concerning rotary drill bit inserts. These methods included two standard wear tests; ASTM G65 and ASTM B611. Furthermore, a modified ASTM G65 test was found as well as an impact-abrasion test. In this work the modified ASTM G65 test, using a rock counter surface, was evaluated in order to understand if the method would mimic the wear of cemented carbides used in rotary drilling. The test method was further developed and showed high repeatability. Measured weight losses showed that the test could distinguish between two common rotary grade materials with a small difference in hardness but with different microstructures. The wear of the tested materials was analyzed with scanning electron microscopy and compared with rotary drill bit inserts collected from the field. The modified test method proved able to produce wear by mechanisms very similar to those found on field worn inserts. Identified wear mechanisms included cracking, fragmentation and spalling of WC grains as well as embedded fragments of WC grains on the surface. In addition, the binder phase was removed and adhered material from the counter surface was detected.

This thesis work sought to find a tribological testing method suitable for cementedcarbide drill bit inserts used when drilling rock. A review of the literature publishedon the matter showed that there are quite a few test methods developed for wearstudies with cemented carbides, but most of them were not designed for the rockdrilling industry. Published studies performed with the found methods and articleswith analyzed field tests have been studied. It is generally agreed upon that the stepsof wear is that the binder disappears first, followed by removal of carbide grains. Themechanisms of binder phase and carbide grain removal is somewhat debated, butalmost every study observes fracture of the carbide grains. The wear test created inthis thesis was shown to give wear linear with time, but not with load. The newmethod was shown to be capable of distinguishing between different cementedcarbides worn in three body abrasion against different rocks. Analysis of the wornsamples shows that there are similarities with bit inserts worn in field testing. Many ofthe observations made during the analysis are also similar to observations inliterature.

No description available.

No description available.

Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. Accurate failure detection helps mining operations avoid financial losses by preventing unplanned breakdowns, costly repairs, and extended downtime. Moreover, it optimizes operational efficiency by enabling timely maintenance interventions, extending the lifespan of drill bits, and minimizing disruptions. Failure detection also plays a critical role in ensuring the safety of personnel and equipment involved in drilling operations. Traditionally, failure detection in tricone drill bits relies on manual inspection, which can be time-consuming and labor-intensive. Incorporating artificial intelligence-based approaches can significantly enhance efficiency and accuracy. This thesis uses machine learning methods for failure inference in tricone drill bits. A classic Convolutional Neural Network (CNN) classification method was initially explored, but its performance was insufficient due to the small dataset size and imbalanced data. The problem was reformulated as an object detection task to overcome these limitations, and a post-processing operation was incorporated. Data augmentation techniques enhanced the training and evaluation datasets, improving failure detection accuracy. Experimental results highlighted the need for revising the initial CNN classification method, given the limitations of the small and imbalanced dataset. However, You Only Look Once (YOLO) algorithms such as YOLOv5 and YOLOv8 models exhibited improved performance. The post-processing operation further refined the results obtained from the YOLO algorithm, specifically YOLOv5 and YOLOv8 models. While YOLO provides bounding box coordinates and class labels, the post-processing step enhanced drill bit failure detection through various techniques such as confidence thresholding, etc. By effectively leveraging the YOLO-based models and incorporating post-processing, this research advances failure detection in tricone drill bits. These intelligent methods enable more precise and efficient detection, preventing operational losses and optimizing maintenance processes. The findings underscore the potential of machine learning techniques in the mining industry, particularly in mechanical drilling, driving progress and enhancing overall operational efficiency

In this thesis, drill bit buttons with cobalt, nickel and iron binders in different compositions have been tested against granite and sandstone and the wear and friction have been measured. Furthermore, the wear and degradation of the buttons have been categorized. Buttons with cobalt binder were tested against granite and sandstone and buttons with alternative binders (Ni, Fe, Co) were tested against granite. Cobalt buttons were used as a reference and the wear and friction of the alternative binders was compared to the reference. The amount of worn rock was also measured. Furthermore, post treated drill bit buttons with a composition of Fe-Ni-Co were compared to buttons with the same composition that had not been post treated The results show that buttons with an alternative composition of Fe-Co-Ni and Fe- Ni wears less than the cobalt reference. The post treatment process does not decrease the wear of the drill bit but lowers the deviation from the mean wear. The amount of worn rock does not differ between the samples except for between the post treated and not post treated buttons with a composition of Fe-Ni-Co. The post treated buttons produces more rock debris than the not post treated. No apparent difference could be seen on the surface of the tested buttons after the test. However, composition specific cracks could be found underneath the surface of the samples. EDS-analysis showed signals of oxygen inside of all of the investigated cracks. For some compositions at depths of 20 micrometers. The curves of friction shows similar appearance but the values of the coefficient of friction differs. No apparent correlation was found between the wear and friction of the samples. Furthermore, no apparent correlation was found between the hardness and the wear of the buttons.