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- Power-Agnostic Approach for Surface Drill Rigs(2026) Islam, Maliha; Jayath Koppisetti, SatyaThe mining industry’s transition towards low-carbon operations requires electrification strategies that remain reliable under weak grids, variable renewable output, and highly dynamic drilling loads. This thesis proposes and evaluates a power-agnostic electrical architecture for surface drill rigs that enables operation across AC grids, DC microgrids, and hybrid renewable-storage systems without major system redesign. The work develops a conceptual multi-source architecture, assesses technology maturity through TRL mapping, and outlines a road-map for the evolution of converters, batteries, and control systems that can be substituted or upgraded without redesigning the system between the grid interface and the motor. The study proceeds in two parts. The first decomposes the rig power supply into its functional subsystems and assesses each against technology readiness and S-curve maturity, producing evolution matrices for cables, transformers, filters, rectifiers, grid-forming converters, DC-DC converters, batteries and inverters. A cross-impact analysis then examines how those subsystems constrain one another, and three candidate architectures are derived from the matrices. The second part evaluates the selected architecture, a grid-forming converter with a common DC bus and DClink battery storage, in time-domain simulation over four supply scenarios spanning short-circuit ratios from 20 to 2 and four disturbance families. The technology assessment finds the constituent hardware mature and the integration immature: active front ends and medium-voltage converters sit at readiness levels 8 to 9, while the supervisory and analytics layers that would make an architecture genuinely source-agnostic remain at 5 to 7. The simulations show that DC-link storage decouples the rig from the supply almost completely. A load variation of 290.5 kW across the drilling cycle reaches the point of common coupling as 0.13 kW on the stiffest connection, and the machine side registers no measurable response to any grid disturbance applied. As the connection weakens, more of the load variation reaches the grid, rising to 2.2 % of it on the weakest supply. The converter also meets its active-power droop once the frequency has settled, although the power swings well beyond the droop value while the frequency is still changing. The main limitation lies in the converter current. Three of the sixteen disturbance cases took it above the 1.50 pu rating. In the 60◦ phase jumps the current rose faster than the virtual-impedance current limit could act, reaching 1.97 pu on the stiffest supply, so a higher converter rating or a different current-limiting method is needed. In the zero-voltage sag on the weakest supply, the converter took 2.2 s to regain synchronism, because the angle correction applied after fault ride-through has no output limit. Overall, the architecture can keep a drill rig running across a wide range of grid strengths, provided the grid-side converter is rated and controlled for the disturbances. Based on these results, the thesis recommends limiting the output of the angle correction and selecting the converter rating or current-limiting method with phase steps in mind. It further proposes simulating a full working shift to size the storage by energy, using a switching converter model to assess harmonics and semiconductor losses, quantifying total cost of ownership and CO2 emissions, and checking the technology assessment for design evolution and implementation strategies to support OEMs in advancing sustainable, future-ready drilling systems.
- Use of AI techniques to extract data from CAD drawings: To create a COW (Crude Oil Washing) manual(2026) Arvidsson, RasmusCrude oil washing (COW) manuals require vessel-specific technical information from drawings, questionnaires and system documentation. Much of this information is available in documents provided by the shipyard, but it is distributed across drawing pages, tables, labels and diagram regions, making manual extraction time-consuming and sensitive to missing or inconsistent source material. This thesis asks to what extent AI-based techniques can support the production of COW manuals from CAD derived drawings and related technical documents with varying formats, layouts and information content. Confidential source documents, complete page images, full OCR output and system-connectivity information were processed locally. Only selected metadata and bounded excerpts assessed as insufficient to reconstruct a drawing or its connection logic were made available to a cloud-based large language model (LLM). The work implemented a multi-stage pipeline for page categorization, text extrac tion, token-level semantic labelling, structural page analysis, target-region detection, retrieval and LLM-assisted draft generation. The source material described in the dataset appendix contained 32 project entries from anonymized Chinese and Ko rean shipyards. The pipeline combined direct PDF text extraction, OCR, TF-IDF page-category scoring, rules, dictionary matching, LayoutLMv3 token classification, YOLO-based page-content and target-region detection, YAML-guided search and an evidence-controlled LLM workflow with local preprocessing. The final page-category model classified 64 of 67 validation pages correctly, corre sponding to 95.5 % accuracy. The final directed-search retrieval reached 96.5 % validation box recall, 94.4 % page recall and 66.5 % candidate precision. In a one project proof-of-concept case study, the workflow generated a draft of the cargo oil system section and an LLM-generated questionnaire. Of the 24 questionnaire fields, 11 were supported by the available evidence and 13 remained unresolved. The draft included the system family, tank arrangement, main cargo oil pump data, major pipe dimensions and stripping-system information, while control, monitoring, heating and several operational fields were omitted or marked for review. The case study demonstrates workflow feasibility but does not estimate generalization to new projects. The results show that AI methods can support parts of COW manual production, particularly evidence location, structuring and preparation of a traceable first draft on the evaluated material. The system is not suitable as an autonomous manual generation workflow, and no conclusion can be drawn about performance on new projects from the available LLM case study. Human engineering review and addi tional structured source material remain necessary for completeness, approval qual ity and operational safety.
- Molecular modelling for investigation of material properties of graphene nanoplatelet composites(2026) Petersson, MarcusThis master’s thesis investigates the molecular modelling of material properties of graphene nanoplatelet (GNP)/epoxy composites using molecular dynamics (MD) simulations. A cross-linked epoxy matrix (DGEBF/DETDA) with embedded GNPs was constructed in BIOVIA Materials Studio using the COMPASS force field. Elastic constants were calculated via the strain fluctuation method, yielding full compliance tensors and derived Young’s moduli. Results from production runs lasting 50 ps and 100 ps were compared, showing that simulation length substantially affects predicted values, with relative errors in compliance components sometimes exceeding the mean values. While Young’s moduli (approximately 1.6–2.6 GPa) were of the same order of magnitude as experimental references (≈3 GPa at 3–4 wt% GNP), the high statistical errors highlight the need for convergence studies. Additional contributions include the development of a workflow for molecular modelling with MD and the finding that MD simulations of insufficient duration limit the accuracy of the modelling. The thesis concludes with recommendations for longer simulations and the use of high-performance computing platforms (e.g. LAMMPS or GROMACS), as well as future work on neural network surrogate models for investigating plastic material behaviour.
- Conceptual Design of a Next-Generation Wire Harness Assembly Workstation for Industry 5.0(2026) Carlsson, Johan; Sekar Vasanthi, Arvind NavinThis thesis presents a conceptual design for the next generation of wire harness assembly workstations at Volvo Cars. With the manufacturing industry shifting toward Industry 5.0, the primary objective of this research was to develop a realistic, efficient, and flexible assembly solution capable of being implemented within a 5 to 10-year timeframe. Through a comprehensive literature review, interviews with industry stakeholders, and a detailed requirement analysis, this study evaluates the integration of Automated Guided Vehicles (AGVs) and collaborative robots (cobots) as a viable strategy to enhance production performance. The proposed solution emphasizes a gradual transition toward increased automation, where cobots and AGVs are integrated directly into the wire harness workstation to support operators, improve ergonomics, and stabilize material flow. The simulation assessment was used to evaluate the proposed workstation layout, movement sequences, approximate cobot reach, and compatibility with the existing line speed. In addition, the concept includes an AI-based automated quality control system integrated into the wire-harness workstation. The system is intended to support inspection of routing, component presence and variant correctness immediately after assembly tasks are completed. The findings suggest that the role of the human operator will evolve from performing repetitive manual labor to assuming more high-level responsibilities, such as system management, operational oversight and complex problem-solving. By retaining human expertise for scenarios requiring specialized judgment, the proposed design achieves a balanced synergy between advanced automation and human-centric manufacturing. This research provides a structured roadmap for Volvo Cars to modernize its production facilities, ensuring long-term competitiveness and operational efficiency