Today manufacturing enterprises aim not only to deliver high-value, cost-effectively products in a sustainable way, but also to consider the quality of the working environments. The analysis of human factors, which strongly affect time and quality of manufacturing processes, are crucial for satisfying people involved in the manufacturing process and making them safe, preventing diseases, errors and excessive workload. The paper presents a structured procedure to automatically extract data from virtual analysis made by digital manufacturing tools and measure a set of indicators to validly assess manufacturing ergonomics. The research considers the state of the art in manufacturing ergonomics and defines a set of indicators suitable for manufacturing manual operations, focusing on assembly tasks. Furthermore, it defines a methodology to automatically extract data valorising the selected indicators and an application, based on Visual Basic, to generate the specific task list and related assessment. The result is a rapid and objective assessment, independent from the experience of the user, which can be executed during process design. The procedure has been applied to an industrial case study, where the manual assembly of cabin supports on the tractor chassis has been analysed in order to correct the most uncomfortable steps and obtain a more ergonomic process. A decrease of the EAWS score, calculated with the proposed method, allowed to validate the proposed solution, suggesting a redesign of the assembly cycle to improve the working conditions. Such a procedure anticipates the analysis of the workers’ wellbeing during the design stage to support the definition of human-centric manufacturing processes, simplifying and accelerating the assessment activities.

Fabio, G., Margherita, P., Luca, Z., Marcello, P. (2019). An automatic procedure based on virtual ergonomic analysis to promote human-centric manufacturing. PROCEDIA MANUFACTURING, 38, 488-496 [10.1016/j.promfg.2020.01.062].

An automatic procedure based on virtual ergonomic analysis to promote human-centric manufacturing

Margherita, Peruzzini;
2019

Abstract

Today manufacturing enterprises aim not only to deliver high-value, cost-effectively products in a sustainable way, but also to consider the quality of the working environments. The analysis of human factors, which strongly affect time and quality of manufacturing processes, are crucial for satisfying people involved in the manufacturing process and making them safe, preventing diseases, errors and excessive workload. The paper presents a structured procedure to automatically extract data from virtual analysis made by digital manufacturing tools and measure a set of indicators to validly assess manufacturing ergonomics. The research considers the state of the art in manufacturing ergonomics and defines a set of indicators suitable for manufacturing manual operations, focusing on assembly tasks. Furthermore, it defines a methodology to automatically extract data valorising the selected indicators and an application, based on Visual Basic, to generate the specific task list and related assessment. The result is a rapid and objective assessment, independent from the experience of the user, which can be executed during process design. The procedure has been applied to an industrial case study, where the manual assembly of cabin supports on the tractor chassis has been analysed in order to correct the most uncomfortable steps and obtain a more ergonomic process. A decrease of the EAWS score, calculated with the proposed method, allowed to validate the proposed solution, suggesting a redesign of the assembly cycle to improve the working conditions. Such a procedure anticipates the analysis of the workers’ wellbeing during the design stage to support the definition of human-centric manufacturing processes, simplifying and accelerating the assessment activities.
2019
Fabio, G., Margherita, P., Luca, Z., Marcello, P. (2019). An automatic procedure based on virtual ergonomic analysis to promote human-centric manufacturing. PROCEDIA MANUFACTURING, 38, 488-496 [10.1016/j.promfg.2020.01.062].
Fabio, Grandi; Margherita, Peruzzini; Luca, Zanni; Marcello, Pellicciari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/952202
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