In today’s industry, the competitiveness between companies is continuously increasing and thus it is important to continue utilizing new technologies to make the operation more efficient and it is vital to strive towards continuous improvement. At this moment it is getting more and more vital to utilize the 4th industrial revolution in the production industry but to utilize this new wave of technology it is crucial to understand what in-efficiencies exist in a production plant and how to work with Lean production as well as implementing the new technologies such as Artificial intelligence and machine learning. These technologies are relatively new to the industrial industry and naturally, with new technologies, there are challenges, benefits but also risks involving new technology implementation. This must be done for companies to stay competitive within the sector even though it is not easy to implement, hence this thesis focuses on realizing these challenges and risks as well as understanding the benefits that can be gained when implementing artificial intelligence in production. When working with this kind of implementation it is important to consider all aspects that are affected by the change, this includes humans as well as the production itself in addition to that also the environment. It is important to understand that when working with this type of implementation it is important to realise what the lean wastes are and by understanding this it will be easier to know what AI can do to minimize or eliminate these wastes and thus making the operation more efficient. This study focuses on the challenges that this type of implementation might have as well as what benefits and risks that AI aided scheduling will have when its implemented. In this study, the findings are connected to a case study made at a focal company in the wood industry as well as an extensive literature study within the field. This thesis provided information in the analysis chapter about how these subjects are linked to the industry and how it's linked to the four main fields of this study. By combining the literature search with the findings from the case study at the focal company, vital information could be gathered and analysed. The areas of this study that was analysed and later discussed when answering the three research questions. The result of this thesis was later used as a base for suggestions for possible future implementations within the field. In addition to that, this study also acts as a framework for how AI implementations can benefit a company’s operation within the industrial sector

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