Ergonomics study on an assembly line used in the automotive industry

Abstract

Today, for the enterprises, the competition is more and more intense and more competitor try to satisfy their customers. In the field of automotive industry, the demands for the product increase and the company need to satisfy the demand. As the demands increases, the company should produce more product than usual. In the same time the comfort and health of the workers should be consider. Some factors such as workstation design should take into consideration in order to increase the productivity and at the same time protect workers from accidents and health problem. Therefore, the workstations need to be redesign by applying the ergonomics principles. This paper presents the combined application of Artificial Neural Networks and the Rapid Upper Limb Assessment (RULA) Analysis in the process of redesign ergonomic workstations. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to ranking a workstation. The neural network is simulated with MATLAB. The experiment presented in this paper was realized at University of Piteşti, Faculty of Mechanics and Technology, Department of Manufacturing and Industrial Management, using CATIA V5 software.

Automation of unstructured production environment by applying reinforcement learning

“…ML approaches can be employed for process planning and system design in addition to traditional product design and job assignment. Anghel et al (2019) offers experimental research on the ergonomic workstation redesign process for automobile assembly lines by merging neural networks with rapid upper limb assessment (RULA) analysis. Apart from the conventional approach of digital simulation, camera sensors and recorded videos are used to automatically compute RULA scores based on computer vision and ML (MassirisFernández et al, 2020).…”

A comparative neural networks and neuro-fuzzy based REBA methodology in ergonomic risk assessment: An application for service workers

“…Even if most applications focus on the risk level definition of tasks using REBA or similar assessment methods like RULA, few studies consider neural network comparisons and fuzzy logic in alignment with this research. For example, Anghel et al (2019) studied the RULA method by integrating neural networks and applied the proposed method to automotive workers. They structured an experiment in MATLAB in order to rank workstations according to their ergonomic risk levels.…”

A New Model for Determining Factors Affecting Human Errors in Manual Assembly Processes Using Fuzzy Delphi and DEMATEL Methods

“…Noise [49,50], inappropriate lighting [49,50], improper temperature and humidity [49,50], poor indoor air quality [25], poor ergonomics design of the workplace [28], accessibility problems [25], and poor workplace layout [25].…”

“…Human errors have been studied extensively, with many previous studies concentrating on organizational factors such as training [25], a lack of supervision, and avoiding the root cause of the error [39]. Appropriate lighting and noiseing in environmental work improves awareness and cognitive performance [49,50]. The poor ergonomic design of the workplace [28] and poor workplace layout [25] led to increased human errors.…”