This thesis focuses on the integration of artificial intelligence (AI) into IPS IMMA, a software used in digital human modelling (DHM), as a way to enhance efficiency and usability in product and production design. The project investigates real-world issues faced by users in ergonomics simulation and operation planning, exploring the potential for AI-enhanced support.
A user-focused methodology was followed. Semi-structured interviews with design, engineering and ergonomics specialists were conducted. The qualitative data gathered were thematically analysed to understand recurring issues, unmet requirements and user expectations regarding AI support. Personas were established to represent different categories of users and facilitate ideation. The Design Thinking methodology supported a systematic understanding of user needs, identification of key issues and generation of AIenabled support concepts.
Several usability issues were identified, including unwanted manual steps, lack of intuitive workflow support and limited automation. Participants recommended AI-calculated grip points, workflow-guidance assistants, learning-based personalisation and automated ergonomic testing. These suggestions were compiled into a ranked list of system requirements.
To explore the feasibility of these ideas, a basic proof of concept was developed in the form of a modular AI assistant. The assistant integrates natural language interaction, context-specific support and direct manipulation of IPS IMMA elements to illustrate how AI could enhance key parts of the workflow.
Findings indicate that integrating AI into IPS IMMA could significantly enhance usability by delivering a more efficient, effective and satisfactory experience, particularly for novice or occasional users. By virtue of its user-centred approach and structured innovation methodology, the study outlines a pathway towards more accessible and adaptive AIsupported DHM tools.
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