To achieve success in today's competitive environment, companies are realising the importance of design collaboration during new product development. The aim of this research was to develop a collaborative design tool for use by industrial designers and engineering designers. To achieve this, a literature review was undertaken to understand the working relationship among the two disciplines during new product development. Following this, empirical research through interviews and observations outlined three problem areas: conflicts in values and principles; differences in education; and differences in representational tools and methods. The latter was chosen because the problem area of design representations was found to be highly significant. In looking at bridging differences in design representations, a taxonomy comprising 35 forms of sketches, drawings, models and prototypes was generated. A second stage of empirical research was conducted to establish the popularity of each representation and the type of design / technical information that industrial designers and engineering designers communicated with. The information was indexed into CoLab cards that would enable the two disciplines to gain joint understanding and create shared knowledge when using visual design representations. Following a pilot evaluation and minor modifications, student and practitioner interviews with a case study were employed to assess the significance of CoLab. The findings revealed that 82% of the interviewees felt CoLab to have built a common ground through the use of visual design representations. 75% gave a positive rating when asked if the system would enhance collaboration and 91% gave the physical cards a positive response as it provided instant access to information and allowed easy sharing. This thesis is a step towards a greater understanding of collaboration between industrial designers and engineering designers. The use of the CoLab system provides the prospect of achieving a common ground between the two disciplines.

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The loss of senior expertise in power line design poses a critical challenge to the sustainable energy transition. Current methods of knowledge transfer fail to prevent the loss of invaluable knowledge necessary for future junior power line designers. Additionally, the rise of informal deployment of generative language models may also threaten to bury hand-written knowledge documents before this knowledge can be extracted, structured, and preserved for future guidance. This thesis proposes a framework where large language models are integrated into knowledge transfer and decision-making guidance for an engineering enterprise. Using this framework, this thesis further explores how data-driven knowledge tools can assist junior design engineers by supporting information retrieval and directing to knowledge sources. The ability of a large language model to retrieve relevant knowledge from an engineering design document was validated by comparing the process of human designers manually completing a similar task. In this evaluation involving six participants and the large language model, responses to questions on mechanical dimensioning of stays for utility poles were ranked by experts. The results showed that the large language model responses were ranked similarly to the junior designers on average. Additionally, a small-scale demonstrative knowledge tool, insights from interviews, literature studies as well as the results from the validation study lead to the conclusion that large language models can assist power line designers via a knowledge tool. Beyond power line design, this thesis contributes to the understanding of how data-driven language models can assist knowledge retrieval and decision-making across other engineering design domains. This work utilizes a professional education document on the mechanical dimensioning of wooden power line poles including an analysis on the wind and weight span’s affect on the dimension of the pole, developed parallel to this work. The original design data from the document supported the tests conducted in this thesis. The professional education document on the mechanical dimensioning of wooden power line poles was developed in parallel to this thesis as a case study supporting the tests with original design data on power line design knowledge. The work also discusses risks and ethical aspects when implementing such a knowledge tool. Risks such as leakage of classified information are emphasized and need comprehensive systems and methods to be avoided. It is therefore highlighted how important it is to carry out the project with care and expertise to avoid damage to companies and society. Local language models or highly trusted AI system providers are recommended to ensure that no sensitive information is leaked to an unwanted third-party. With a high degree of caution and consideration of risks, an effective knowledge tool can contribute to increased efficiency, faster and more sustainable development of power line infrastructure, and thus an faster energy transition. / Förlusten av senior expertis inom kraftledningskonstruktion utgör en kritisk utmaning för den hållbara energiomställningen. Nuvarande metoder för kunskapsöverföring är otillräcklig för att förhindra förlusten av ovärderlig kunskap som är nödvändig för framtida juniora kraftledningsprojektörer. Dessutom kan den ökade informella användingen av generativa språkmodeller hota att begrava mänskligt skrivna kunskapsdokument. Detta arbete presenterar ett ramverk d¨ar storskaliga språkmodeller används för att underlätta kunskapsöverföring och tillhandahålla vägledning vid beslutsfattande inom ingenjörsföretag. Med hjälp av detta ramverk utforskar arbetet ytterligare hur datadrivna kunskapsverktyg kan hjälpa juniora kraftledningskonstrukt¨orer genom att stödja informationsinhämtning med hänvisning till kunskapskällorna. En storskalig språkmodells förmåga att hämta relevant kunskap från ett tekniskt designdokument validerades genom att jämföra processen för mänskliga designers som manuellt slutförde en liknande uppgift. I denna utv¨ardering, som involverade sex deltagare och den storskaliga spr˚akmodellen, rankades svaren på frågor om mekanisk dimensionering av stag för kraftledningsstolpar av experter. Resultaten visade att den storskaliga språkmodellens svar i genomsnitt rankades på liknade nivå som de juniora ingenjörerna. Tillsammans med  ett småskaligt demonstrativt kunskapsverktyg, insikter från intervjuer med kraftledningskonstruktörer, litteraturstudier samt resultat från valideringsstudien dras slutsatsen att storskaliga språkmodeller kan stödja kraftledningskonstruktörer via ett kunskapsverktyg. Utöver kraftledningskonstruktion bidrar detta arbete till förståelsen av hur datadrivna språkmodeller kan hjälpa till med kunskapsinhämtning och beslutsfattande  inom andra tekniska designområden. Arbetet använder ett professionellt utbildningsunderlag om mekanisk dimensionering av kraftledningsstolpar i träkonstruktion, inklusive en analys av vertikala- och horistontella linspannets påverkan på stolpens dimension, utvecklat parallellt med detta arbete. Orginaldesigndata från underlaget stödde de tester som genomfördes. Arbetet belyser även risker och etiska aspekter vid implementering av ett sådant kunskapsverktyg. Risker som läckage av sekretessbelagd information betonas, och omfattande system och metoder behövs för att undvika dem. Därför understryks hur viktigt det är att genomföra liknande projekt med noggrannhet, försiktighet och expertis för att undvika skador på företag och samhälle. Lokala språkmodeller eller API-leverantörer med högt förtroende rekommenderas för att minimera risken att känslig information läcker ut till en oönskad tredje part. Med stor försiktighet och hänsyn till riskerna kan ett effektivt kunskapsverktyg bidra till ökad effektivitet, snabbare och mer hållbar utveckling av kraftledningsinfrastruktur, och därmed en snabbare energiomställning.