ORCID

Abstract

Recent advances in AI, above all machine and deep learning, have brought about unprecedented possibilities in automation, prediction and problem solving with impact on operators and their way of working and interacting with automation on the shop floor. While the expected effects are focusing on increasing the efficiency, flexibility, and productivity of operations in the industrial and service sector, there is justified scepticism towards its implementation due also to the challenge of integrating AI into operator’s current way of working and practices in a way that actually supports also the human in the loop. Therefore, it is now time to consider the user’s side from an employees’ point of view in order to foster AI in a human-technology relationship. The present paper is exploring the preliminary steps taken in this direction while trying to identify a problem definition and its suitable solutions for, firstly, improving the human automation interaction and, secondly, reduce the time variability and improve efficiency in a milling process for large metal metal components of a wind turbine at a manufacturing facility. To complement this description, a data analysis of the manufacturing process status is provided. The analysed data sets contain general information of relevant parameters of the manufacturing system as well as the required inputs from the operators. The purpose of this report is to establish the basis on which a thorough operational description of the overall man-automation process is defined and the usefulness of including a better integration for the manual tasks in it. The operational description of the tasks is a key ingredient to achieve better requirements specifications and how we can enhance the human performance of the operators by increasing their situational awareness on the shop floor. Moreover this task mapping can account of a lot of missing information regarding variability of execution time in the process and to support scheduling of manual activities for the operator to perform while the automated task may not need direct supervision.

Keywords

Collaborative Intelligence, Data analysis, Mutual performance monitoring, Requirement specification, Task analysis, Teamwork

Publication Date

2022-01-01

Event

32nd European Safety and Reliability Conference, ESREL 2022

Publication Title

Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future

Publisher

Research Publishing Services

ISBN

9789811851834

First Page

3267

Last Page

3276

Deposit Date

2026-07-29

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