A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations
Human-centred design of collaborative human–robot (HRC) workspaces is central to Industry 5.0. While proximity with collaborative robots offers productivity and flexibility gains, it also raises concerns for both physical safety and cognitive ergonomics. Although physical safety is well addressed, f...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2025.2508451 |
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| author | A. Bonello C.A Brown E. Francalanza M.V. Gauci P. Refalo |
| author_facet | A. Bonello C.A Brown E. Francalanza M.V. Gauci P. Refalo |
| author_sort | A. Bonello |
| collection | DOAJ |
| description | Human-centred design of collaborative human–robot (HRC) workspaces is central to Industry 5.0. While proximity with collaborative robots offers productivity and flexibility gains, it also raises concerns for both physical safety and cognitive ergonomics. Although physical safety is well addressed, few studies integrate cognitive and physical well-being into workstation design. This research presents a novel approach that combines Kansei Engineering (KE) with Suh’s Axiomatic Design (AD) to support physically and cognitively safe HRC workstations. Unlike existing studies that rely solely on Suh’s Axiom 1 (maintain independence), this work also takes into account Axiom 2 (minimise information) to select between equally independent physical and cognitive design parameters. The approach is demonstrated through a case-study workstation, visually illustrating the relationship between functional and physical metrics. This study advances the field by providing a novel replicable, human-centred approach that unites cognitive and physical ergonomics,bridging theory and practical application for both academic and industrial contexts. |
| format | Article |
| id | doaj-art-ced5e9d9412b4b8a86f1253918f2133d |
| institution | Kabale University |
| issn | 2169-3277 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-ced5e9d9412b4b8a86f1253918f2133d2025-08-20T03:47:12ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772025-12-0113110.1080/21693277.2025.2508451A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstationsA. Bonello0C.A Brown1E. Francalanza2M.V. Gauci3P. Refalo4Department of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, MaltaDepartment of Mechanical & Materials Engineering, Worcester Polytechnic Institute, Worcester, MA, USADepartment of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, MaltaDepartment of Disability Studies, Faculty for Social Wellbeing, University of Malta, Msida, MaltaDepartment of Industrial and Manufacturing Engineering, Faculty of Engineering, University of Malta, Msida, MaltaHuman-centred design of collaborative human–robot (HRC) workspaces is central to Industry 5.0. While proximity with collaborative robots offers productivity and flexibility gains, it also raises concerns for both physical safety and cognitive ergonomics. Although physical safety is well addressed, few studies integrate cognitive and physical well-being into workstation design. This research presents a novel approach that combines Kansei Engineering (KE) with Suh’s Axiomatic Design (AD) to support physically and cognitively safe HRC workstations. Unlike existing studies that rely solely on Suh’s Axiom 1 (maintain independence), this work also takes into account Axiom 2 (minimise information) to select between equally independent physical and cognitive design parameters. The approach is demonstrated through a case-study workstation, visually illustrating the relationship between functional and physical metrics. This study advances the field by providing a novel replicable, human-centred approach that unites cognitive and physical ergonomics,bridging theory and practical application for both academic and industrial contexts.https://www.tandfonline.com/doi/10.1080/21693277.2025.2508451Axiomatic designcollaborative workstationcognitivequantificationKansei Engineering |
| spellingShingle | A. Bonello C.A Brown E. Francalanza M.V. Gauci P. Refalo A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations Production and Manufacturing Research: An Open Access Journal Axiomatic design collaborative workstation cognitive quantification Kansei Engineering |
| title | A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations |
| title_full | A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations |
| title_fullStr | A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations |
| title_full_unstemmed | A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations |
| title_short | A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations |
| title_sort | novel human centred approach using axiomatic design and kansei engineering for designing physically and cognitively safe human robot collaborative workstations |
| topic | Axiomatic design collaborative workstation cognitive quantification Kansei Engineering |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2025.2508451 |
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