Dyson School of Design Engineering
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1 Dyson School of Design Engineering Author/Date Prof Saeema Ahmed-Kristensen Deputy Head of School
2 Dyson School of Design Engineering The newly-established Dyson School of Design Engineering offers: 1. 4-year MEng in Design Engineering with 6-month industrial placement 2. MA/MSc in Global Innovation Design run jointly with the Royal College of Art 3. MA/MSc in Innovation Design Engineering run jointly with the Royal College of Art 4. Research PhDs and research and development projects in design engineering
3 Dyson School of Design Design projects Research: Engineering Product Development Industrial Design and Human factors Additive Manufacturing Improving design process Improving products Improving people Improving user experience Sounds, aesthetic, interaction
4 Research characteristics Saeema Ahmed-Kristensen Creativity & Ideation User experience Interaction & Emotional design Service Engineering Knowledge management Multi-disciplinary approachdesign, computational, psychology Design theory Foundations of design tools/methods Engineering Change Design synthesis Design expertise Decision-making Interaction & Emotional design
5 Industrial collaboration Saeema Ahmed-Kristensen Research: Complex & Variant Design: Aerospace: Roll-Royce, BAE SYSTEM Medical devices: Ambu. NovoNordisk, Radiometer Phillips Medical One-off (platform approaches): Aker Solutions: Drilling equipment (oil rigs) Maersk drilling: Oil Rigs owners Consumer Stelton
6 Engineering Product Development: Engineering Knowledge management Transfer of knowledge from: Designers, engineers, service other parts of value chain to improve product process, people, Products Users to improve user experience, products, processes Emotional design: assess design, and generate Interaction design: user studies, comfort assessement Key output is organisation of knowledge/data, modelling of complex products, Process Distributed manufacturing and design Global product development Support innovation Flexibility: Agile Processes Smart design for values
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9 Deeper case study: Indexing knowledge Problem: 60% EDIT 56% How to access of information 50% 40% Approach: Empirical interviews (BAE SYSTEM, Rolls-Royce) Modelling complexity-ontology Visualisation of relationships - Build up expertise - Assist search query 30% 20% 10% 0% 19% 17% 7% Products Issues Functions Design Process Outcomes: Ontology (user conceptual models) visible browsing structure Methodology for ontology building Patent auto-generation of relationships visualisation data
10 Engineering Knowledge Management Reduce failure CONCEPT SELECTION Reasons to stop concepts First reason to stop Second reason to stop Third reason to stop Link between reasons Objective not satisfied by designs Reduce product development times Slip & run off Stroke out Run free Jerky / jammed Risk, reliabiity & safety Construction Failure modes Integration Production Dependability Readiness Safety Parts break Lack of friction Non-proven Weak clicks Weak push Spring effect Bend/deform Too much friction Creep Wear Backlash Misalignment Dim variation Lack of bearing Scrapping risk Bad order Assy axes Complex process Complex shape Many parts Feedback error Sync delay System complexity Power budget Interface complexity Room to hardware Order by last discarded Crownwheel Spindle Gearwheel NextNP4 NextNP6 AutoPen 4 AutoPen 6 Caulking gun Cupwheel Piezo AutoPen 7 AutoPen 8 AutoPen 10 AutoPen 12 AutoPen 9 AutoPen 11 AutoPen 14 AutoPen 5 AutoPen 16 Vinicius Marini Saeema Ahmed-Kristensen
11 Distributed design and manufacture 6MDKK Industriens Fond Joint Innovation models Measuring impact of GPD Decision making tools Large network of Companies Related EC-HVEN 2MDKK UK:Prof. Mike Gregory, University of Cambridge; Dr Yufeng Zhang, Birmingham University. China: Harbin Institute of Technology; Tsinghua University; Shanghai Jiao Tong; Zhejiang University.
12 Determining models of wellbeing in office environments through the use of sensor-integrated products Objectives A. To determine the influencing factors of well B. B. To compare sensors data to qualitative data of office employees and assess the effect of each influencing factor of wellbeing. To develop test and validate a mathematical model to express wellbeing as a function of the influential factors identified and assessed in A and B.. To improve wellbeing conditions in the office environment through a series of combined interventions embedded in sensor integrated office related products.
13 Prof Saeema Ahmed-Kristensen Deputy Head of School
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