Nowadays the Smart Factories operating within the Industry 4.0 revolution, require more and more reliable, fast and automatic tools for production analysis and improvement. Manufacturing companies, in which the human labour has a crucial role, need instruments able to manage complex production systems in terms of resource utilization, product mix, component allocation and material handling optimization. In this context, this work presents an original hardware/software architecture, Motion Analysis System (MAS), aimed at the human body digitalization and analysis during the execution of manufacturing/assembly tasks within the common industrial workstation. MAS is based on the integration of the Motion Capture (MOCAP) technology with an ad hoc software developed for productive and ergonomic analysis of the operator during his work. MAS hardware integrates a network of depth cameras initially developed for gaming (Microsoft Kinect v2™, conceived for markerless MOCAP) and now used for industrial analysis, while an original software infrastructure is programmed to automatically and quantitatively provide productive information (human task analysis in terms of time execution and used space within the workplace, movements of hands and locations visited by the operator) and ergonomic information (full body analysis implementing all the internationally adopted indexes OWAS, REBA, NIOSH and EAWS). This double perspective makes MAS a unique and valuable tool for industrial managers oriented to the workplace analysis and design (in terms of productivity) without neglecting the operator health. This proposed contribution ends with a real industrial application analysing a water pump assembly station: the system setup is discussed and the key results obtained adopting MAS are presented and analysed.
Bortolini, M., Faccio, M., Gamberi, M., Pilati, F. (2020). Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes. COMPUTERS & INDUSTRIAL ENGINEERING, 139, 1-13 [10.1016/j.cie.2018.10.046].
Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes
Bortolini, M.;Gamberi, M.
;Pilati, F.
2020
Abstract
Nowadays the Smart Factories operating within the Industry 4.0 revolution, require more and more reliable, fast and automatic tools for production analysis and improvement. Manufacturing companies, in which the human labour has a crucial role, need instruments able to manage complex production systems in terms of resource utilization, product mix, component allocation and material handling optimization. In this context, this work presents an original hardware/software architecture, Motion Analysis System (MAS), aimed at the human body digitalization and analysis during the execution of manufacturing/assembly tasks within the common industrial workstation. MAS is based on the integration of the Motion Capture (MOCAP) technology with an ad hoc software developed for productive and ergonomic analysis of the operator during his work. MAS hardware integrates a network of depth cameras initially developed for gaming (Microsoft Kinect v2™, conceived for markerless MOCAP) and now used for industrial analysis, while an original software infrastructure is programmed to automatically and quantitatively provide productive information (human task analysis in terms of time execution and used space within the workplace, movements of hands and locations visited by the operator) and ergonomic information (full body analysis implementing all the internationally adopted indexes OWAS, REBA, NIOSH and EAWS). This double perspective makes MAS a unique and valuable tool for industrial managers oriented to the workplace analysis and design (in terms of productivity) without neglecting the operator health. This proposed contribution ends with a real industrial application analysing a water pump assembly station: the system setup is discussed and the key results obtained adopting MAS are presented and analysed.File | Dimensione | Formato | |
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CAIE Mas.pdf
Open Access dal 25/10/2020
Descrizione: accepted manuscript
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Postprint
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Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
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1.15 MB
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Adobe PDF
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