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Small lot sizes and production quantities

FRAUNHOFER — European research initiative SMErobotics is developing new modular and interactive operating concepts and control systems for the efficient use of robots in a variety of applications.

With this in mind, Fraunhofer IPA is designing and developing a cognitive and collaborative welding robot assistant known as CoWeldRob. The goal is to significantly reduce the programming effort for automated production in small and medium-sized welding businesses.

“CoWeldRob is designed to make the automation of welding operations profitable also in the case of small lot sizes and production quantities. It does this by being easy and intuitive to program by the welder and by continuously learning from him,” explains Thomas Dietz, project manager and group leader in the Robot and Assistance Systems department.

The welding robot assistant can automatically transfer programs to similar components without the need for major new programming effort.

“This allows above all small and medium-sized enterprises to respond more flexibly to changes in customer orders,” says Dietz.

Components

Robot programs are automatically generated on the basis of different models of the components, welding process and robot system. Intuitive operation, e.g. using a touchscreen, pointing or showing, allows changes made by the welder to be quickly incorporated and implemented. Such user inputs as well as sensor-detected data are brought into a logical relationship.

This information can then be reused for various downstream processes, such as grinding or quality control. This makes it possible for programming and set-up times to be significantly reduced.

Component localization: By comparing CAD and sensor data, the welding robot assistant is able to automatically determine the precise position of the component and therefore of the welding paths. This makes it possible to adapt the robot path and to dispense with rigid fixtures for exact positioning of the components.

Robust handling of uncertainties: The developed approaches are designed to cope with and suitably react to tolerances both of the component, such as air gap and weld preparation, and of the process, such as a permitted torch orientation error.

This makes CoWeldRob more robust than a conventional automated welding system.

Learning robot: A welder knows from many years of experience which settings are required to produce a high-quality component. The process expert can transfer this experience to CoWeldRob by, for example, evaluating a suggested weld seam sequence and by thus instructing the robot system with regard to the desired mode of behavior.

Consequently, using methods from cognitive research, the robot can learn from the welder’s process know-how and continuously improve its performance over time.