Operator capability monitoring and management by capability-dependent work instructions

Deadline

ICON open call - Operator capability monitoring and management by capability-dependent work instructions

Challenge

High-Variety, Low-Volume (HVLV) assembly requires operators to acquire (and maintain) a broad set of skills and knowledge. They have to cope with large quantities of potentially complex product customizations. As such, there is a HVLV-specific need for continuous operator (re-)training:

  • to refresh assembly procedures of rare product variants,
  • when new product variants are introduced,
  • when new operators join the workforce,
  • etc.

Next to this, HVLV operators require adequate support while performing their assembly operations, most notably in the form of digital work instructions (DWIs). To maximize operator acceptance as well as performance, both the training and the DWI content should be personalized based on the capability level of the involved operator. An important role in this personalization is played by the type of media that is included in the training and DWI content (e.g., text-only, supporting images, (interactive) videos, (interactive) CAD models).

As a basic example, a novice operator will typically benefit heavily from a detailed video, whereas that same video might be considered overkill for an experienced operator and could even have an adverse effect as it might disrupt his/her flow. Finally, as is the case with mass production, HVLV assembly strives for quality maximization and fault minimization; well-trained and DWI-supported operators contribute towards achieving this goal.

In this project we will research an IoT-powered, service-oriented system architecture that holistically encompasses the following technological features, all applied at the level of individual operators:

  1. Fusion of heterogeneous data sources for real-time capability tracking;
  2. managing capability evolution by deploying targeted on-the-job training strategies, and
  3. capability-dependent personalization of media-rich training and DWIs.

The project will be validated via two SBO and three industrial pilots and the impact of the capability-dependent adaptation of media-rich training and DWIs on the acceptance, performance and learning curve of operators will be evaluated.

Project goals

In this project we will research and realize a networked system architecture which includes services for:

  • Operator capability monitoring based on data streams measured using low-cost, non-intrusive sensing solutions

  • Operator capability dashboarding and management through micro-leanings and on-the-job training

  • Personalization engine for DWIs and training based on operator capability, using rich interactive video formats

  • Easy authoring of personalized DWI’s and micro-learnings

Validate to TRL5 level through the realization of instances of the system architecture in:

  • Two SBO cases: the assembly of three complex mechatronic products at the Flanders Make flexible assembly co-creation infrastructure (Infraflex lab and Smart & Agile Assembly lab)

  • Three industrial pilot use cases

The results should facilitate:

  • Training time to reach a required competence level with 50%

  • Reduce the number of human errors with 50%

  • Increase the versatility of operators with at least 30%

 

Interested?

CAPABLE_ICON is an Industrial Research ICON project. We are looking for companies to join the User Group and work with us on the valorisation of the project.

Interested? Complete the form below and we will contact you as soon as possible.

Other ICON open calls

Deadline

Industrial Research (ICON)