KI4Tools

Optimization of hand-carried tools through Machine Learning to support manual work

KI4Tools – Artificial Intelligence for Hand Tools

At a glance

© Fraunhofer IIS
Screwdriver module in the application

Even highly automated production involves numerous manual working processes. Technicians need to be vigilant at all times if these processes are to run smoothly, while information on the work to be done can change regularly when dealing with individualized products. When that happens, the correct use of tools is essential in order to ensure not only quality but also the safety of technicians. With this in mind, Fraunhofer IIS has developed a retrofittable and intelligent sensor module for hand tools that measures various parameters during work processes and thereby delivers process optimization, transparency, and quality assurance. Multiple sensors detect not only actions (e.g., the tightening or loosening of screws) but also the location and sequence of operations.

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Benefits of our solution

  • Retrofittable to existing tools
  • Recording of manual work processes
  • Quality assurance and improvement
  • Optimization of work processes
  • Improvement of process transparency
  • Overview of job status

Applications

  • Maintenance of machinery
  • Furniture construction
  • Manual production steps
  • Monitoring and quality assurance

Technical description

The intelligent tools are underpinned by core technology consisting of a specially designed module with sensors to measure acceleration, rotation rate, and magnetic fields as well as a microphone, whose use is optional. This retrofittable solution is both compact and economical. It can be attached easily to a wide range of hand-operated tools, thereby paving the way for flexible working and universal applications. The module uses Wi-Fi or Bluetooth to transmit a steady stream of data, which form the basis for an AI pipeline that detects and identifies all operations.

© Fraunhofer IIS
Sensor module with camera

Here, an initial binary classifier works as an action detector and starts by filtering out all time intervals that contain actions relating to the corresponding tool (such as the tightening or loosening of a screw using the electric screwdriver). The output provided to the user then consists of “action windows,” which contain the sensor data for the identified time intervals. A second, AI-based classifier subsequently receives these action windows and identifies the specific action to which the filtered data segment pertains. The position of the tool in the room at the time when the action takes place is also pinpointed.

An additional positioning system (QR code tracking), which is independent of the sensor module, also ensures that operations are carried out at the correct location and/or in the right order. An app provides the operator with real-time feedback on the progress of their work and informs them immediately if they deviate from the stipulated process. Another noteworthy feature is that these processing chains are trained using automatic machine learning, so that the entire process of training and evaluating new models is fully automated and requires no expert knowledge. In the future, the aim is for processes to be optimized (by continual learning) at the touch of a button via the existing app and for measurement data to be annotated semi-automatically. This is with a view to making the applied models more robust and more powerful on an ongoing basis.

© Fraunhofer IIS
Application gives feedback in real-time

Machine Learning

 

Thanks to long-standing experience in artificial intelligence and machine learning, we offer you extensive expertise in the implementation of intelligent technologies within your specific use case.

Positioning

 

Drawing on extensive expertise, Fraunhofer IIS can assist you with the introduction of positioning technologies. We have access to cutting-edge measurement techniques as well as a complete test center for the simulation of real usage scenarios.

Further Information

Tool tracking

Errors can be avoided by monitoring tool user activity and by giving straightforward instructions.

Cognitive Handtools

Cognitive sensor technologies allow sensor data from the tools to be recorded and then analyzed and processed directly for the relevant application.