Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries

Abstract

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force-torque (FT) sensor. The proposed work is demonstrated on a 500 mm² near-net-shaped Wire-Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production.

Keywords: WAAM; collaborative robotics; kinesthetic; non-destructive evaluation; robot programming.

Figure 1

Comparison of collaborative robotic arms and industrial manipulators in terms of reachability and load capacity [14,15,16]. These industrial arms can support the kinesthetics concept for path planning, transforming these robots into collaborative entities.

Figure 2

Communication interface for kinesthetic guidance and real-time kinematics generation based on the RSI protocol between external PC, KRC, and FT sensor. The external target updates, cyclically, the control process algorithm in the KRC4 controller.

Figure 3

Real-time control process algorithm for kinesthetic guidance path planning describing the cyclical flow of process information between FT sensor current measurements, setpoint forces and torques, LabVIEW external control program, and generated robot positional corrections.

Figure 4

Expansion of the control process algorithm of kinesthetic path planning (Figure 3) to support the direct feedback of the FT corrections to the LabVIEW environment for adaptive motion control.

Figure 5

LabVIEW External Real-Time Control GUI, which handles the kinesthetic teaching and the generation of kinematics for the taught path.

Figure 6

Experimental setup: (a) robotic setup with a 6 DoF FT sensor and a WAAM roller probe for NDE inspection mounted as an end effector; (b) WAAM component consisting of three sections with three embedded defects.

Figure 7

Kinesthetic teaching: (a) Four points recorded 100 mm above the workpiece, which are the starting, between, and endpoints during the three areas of inspection. Thirteen points were recorded by manipulation of the end effector 5 mm above the specimen, and at these points, the adaptive FT control was enabled to perform the UT inspection for defects; (b) top view of the complex-shaped WAAM component showcasing the taught positions generated from the kinesthetic path planning.

Figure 8

UT NDE Inspection took place following the kinesthetic guidance. The kinematics generation based on the feedback of the FT PI controller to the endtarget position adapted the motion to the overbuild features of the WAAM component.