2022

Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzanie

Rocznik: 2022 | Numer: no. 164

artykuł naukowy

angielski

EN Purpose: The cognitive goal of the article is to quantify various states of variables influencing the worker's burden in the assembly process. On the other hand, the utilitarian goal is to assess the significance of variables for the application of artificial neural networks methods in supporting IE management. Design/methodology/approach: The article deals with the management of ergonomic interventions in industry 4.0. The main tasks during the assembly process were defined on the example of the window production analysis. The application of the method of registering human load indicators to manage the states of variables in the chain of operation of the assembly process was justified. The study analyzed 16 states of variables such as noise, work pace, forced body position, movement, and the location of information and control elements of the IT system. During the bench tests, postural load, heart rate and NASA-TLX assessment were performed. In the preliminary and final studies, metric data was collected, cognitivemotor skills and work fatigue were assessed. The obtained results were quantified using a quantitative comparative method. Findings: The article verifies the approach of measuring the individual workload of an employee for shaping working conditions in the context of assembly works. For the examined example, the weights of the system variables for the inference of artificial intelligence were determined in detail. Research limitations/implications: The main limitation of the study is the research sample. Although the concept departs from statistical research, from the point of view of science, it is reasonable to look for the correlation of the burden on individual user groups, e.g. the elderly, people with disabilities. It is also important to further measure the synergy of individual variables. Originality/value: The novelty of the article is the idea of EI management in the aspect of industry 4.0 through operational shaping and tactical state variables affecting the individual workload of an employee with the use of methods of artificial neural networks. For this purpose, a conceptual method of determining the workload of an employee was presented. The work is addressed to theorists and practitioners responsible for designing and organizing working conditions.

527 - 541

10.29119/1641-3466.2022.164.40

CC BY (uznanie autorstwa)

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