Manual work is a cornerstone of manufacturing, also for factories of Industry 4.0 era. Use cases of manual work regard the production of single item, customized assemblies, small batches. Several injuries can be caused or aggravated by manual handling activities at work. Moreover, the efficiency of the whole process can benefit from correct body posture, parts' visibility and accessibility. Finally, manual work is strongly human-centered and its performance is affected by the expertise, the level of knowledge, attitudes and belief of workers. In this complex context where multiple factors such as Efficiency, Work Performance, Ergonomics and Safety relate each other to achieve a satisfactory smart industry, the paper proposes an innovative Tangible Augmented Reality platform to train and assist workers during the manual handling and assembly tasks necessary to produce consumer goods with high aesthetic qualities. The proposed platform is the result of the application of a multipath methodology to link health and safety elements, typologies of injuries, ergonomics factors and relative qualitative and quantitative assessment methods and ergonomics analysis tools. The TAR platform allows the worker to consult the assembly instructions in a simple and user friendly way and to be informed by potential risk of injuries by a real-time alert. Based on video mapping techniques, the TAR system superimposes the necessary digital contents on the physical model of the product while the operator is building it.

Tangible augmented reality model to support manual assembly / Matteucci, Marco; Raponi, Damiano; Mengoni, Maura; Peruzzini, Margherita. - 9:(2017), p. V009T07A038. (Intervento presentato al convegno ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 tenutosi a USA nel 2017) [10.1115/DETC2017-67742].

Tangible augmented reality model to support manual assembly

Peruzzini, Margherita
2017

Abstract

Manual work is a cornerstone of manufacturing, also for factories of Industry 4.0 era. Use cases of manual work regard the production of single item, customized assemblies, small batches. Several injuries can be caused or aggravated by manual handling activities at work. Moreover, the efficiency of the whole process can benefit from correct body posture, parts' visibility and accessibility. Finally, manual work is strongly human-centered and its performance is affected by the expertise, the level of knowledge, attitudes and belief of workers. In this complex context where multiple factors such as Efficiency, Work Performance, Ergonomics and Safety relate each other to achieve a satisfactory smart industry, the paper proposes an innovative Tangible Augmented Reality platform to train and assist workers during the manual handling and assembly tasks necessary to produce consumer goods with high aesthetic qualities. The proposed platform is the result of the application of a multipath methodology to link health and safety elements, typologies of injuries, ergonomics factors and relative qualitative and quantitative assessment methods and ergonomics analysis tools. The TAR platform allows the worker to consult the assembly instructions in a simple and user friendly way and to be informed by potential risk of injuries by a real-time alert. Based on video mapping techniques, the TAR system superimposes the necessary digital contents on the physical model of the product while the operator is building it.
2017
ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
USA
2017
9
V009T07A038
Matteucci, Marco; Raponi, Damiano; Mengoni, Maura; Peruzzini, Margherita
Tangible augmented reality model to support manual assembly / Matteucci, Marco; Raponi, Damiano; Mengoni, Maura; Peruzzini, Margherita. - 9:(2017), p. V009T07A038. (Intervento presentato al convegno ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 tenutosi a USA nel 2017) [10.1115/DETC2017-67742].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1151358
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