Robust concept development utilising artificial intelligence and machine learning
| dc.contributor.author | Karlsson, Kevin | |
| dc.contributor.author | Alfgården, Hugo | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Industrial and Materials Science | en |
| dc.contributor.examiner | Isaksson, Ola | |
| dc.contributor.supervisor | Knuts, Sören | |
| dc.contributor.supervisor | Pradas Gomez, Alejandro | |
| dc.date.accessioned | 2024-09-16T11:46:10Z | |
| dc.date.available | 2024-09-16T11:46:10Z | |
| dc.date.issued | ||
| dc.date.submitted | ||
| dc.description.abstract | The 80-20 rule suggests that design decisions significantly impact downstream effects, such as product cost, with many of these decisions made during concept generation. This early commitment limits the ability to make changes later in development. Early-stage design requires a variety and quantity of concepts, but designers often fixate on existing designs, limiting innovation. In the aerospace industry, the complexity of concept development and evaluation is particularly challenging. Therefore, this study seeks to explore how AI/ML methods can aid designers in the concept development process. This thesis was initiated as a results of a 2023 internship at GKN Aerospace, which involved generating a concept for the sectioning and manufacturing of an existing part. Recognizing the intricacies of these phases, the authors explored the potential of AI/ML methods to enhance robustness in concept generation and evaluation. The aim is to evaluate how GKN Aerospace can effectively integrate AI and ML into their product development workflows. This involves understanding current methodologies and identifying gaps to address before implementation. The focus is on leveraging AI and ML to streamline complex decision-making processes, ultimately providing actionable insights for robust, efficient concept design aligned with the Zero Defect paradigm in aerospace. Additionally, the thesis identifies gaps in the organization that needs to be address before a possible integration, such as data quality and data secrecy. The result, building on extensive interview studies and literature studies is that there is potential in incorporating AI/ML in concept development processes. Although, AI methods such as LLMs, still have limitations, including confidently producing incorrect results, a phenomenon known as hallucinations. The conclusion is that Generative AI, design tools with integrated AI/ML methods, and LLMs still offers opportunities to simplify concept generation. LLMs can assist with ideation, creative reasoning, and cognitive task offloading. Fine-tuned LLMs, trained on internal documentation, provide instant feedback on less complex tasks, helping designers explore a broader design space, mitigate bias, and enhance knowledge, facilitating the development of robust design solutions | |
| dc.identifier.coursecode | IMSX20 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/308633 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Product Development | |
| dc.subject | Concept Generation | |
| dc.subject | Concept Evaluation | |
| dc.subject | Set- Based Design | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Machine Learning | |
| dc.title | Robust concept development utilising artificial intelligence and machine learning | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Product development (MPPDE), MSc |