Affective Manufacturing System

Affective Manufacturing System

This project demonstrates a smart robotics automation for optimised production based on a modular machine platform that monitors and controls quality functionalities in line with human factors.

The solution

The affective manufacturing system has an integrated robotics system for manufacturing of complex photonic systems and contains a sensor system for in-line process monitoring. The platform integrates a novel operator stress detection module, based on measured physiological features of the operator, and artificial intelligence algorithm for stress detection. Production performance and related operator stress is displayed on an interactive dashboard, used for both stress management and process control.
The measured stress levels are used in a smart decision algorithm that takes into account all relevant decision factors, such as the stress indications, information about circumstances of the employee, information about the production process, and production instructions.


The digitisation of the industry and the increasing need for customized production offers great opportunities for low-volume, high complexity manufacturers of smart factories. Smart factories combine a high degree of flexibility and are data-driven, integrate IoT technology and smart sensor systems for in-line quality and process control, and combine hybrid technologies for customised production. Despite the high degree of automation in smart factories and cyber-security for agile production, human factors still negatively impact safety, yield, and product quality. In some industries, human errors might account for 80% of the total number of incidents. For manufacturing companies, it’s important to take measures to minimise risks related to human behaviour.


Affective manufacturing leads to improved efficiency and productivity, by incorporating human stress factors in the optimisation of the production processes. For simple tasks, low arousal (inattention of the operator) leads to poor performance, high arousal leads to high performance. Difficult tasks require moderate arousal for optimum performance. A too low arousal (drowsiness) as well as a too high arousal (stress) lead to poor performance and should be avoided.
Affective manufacturing leads to improved product quality. One of the requests is error-free production through a self-learning production method in which the system responds autonomously to any errors.
Affective manufacturing leads to improved safety, by preventing under-alertness or drowsiness.
Affective manufacturing leads to improved self-control and operator satisfaction/happiness.

Facts and figures

  • Technology Area:

    Human-Robot Collaboration

  • End User:

    Tooling industry Transportation industry Processing industry Labour-intensive industries

  • Start Date - End Date:

    01/07/2020 - 30/06/2021

  • Duration:

    13 months

  • FSTP Funding:

    297 500,00 €

  • TRL Level at Start:


  • TRL Level at End:


  • Number of early adopters raised:


  • Project results
    The addition of affective strategies will further strengthen self-learning and will cause a paradigm shift in smart manufacturing. It will create higher standards of production, and more operator happiness.

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