Force controlled robotic sanding of free-form composite panels
Bibliographic record
Abstract
In recent decades, the adoption of industrial robots in various manufacturing finishing processes such as sanding, deburring, and polishing has been on the rise. Robotic finishing processes require very close interaction with the workpiece, and forces are exerted by the robot’s end-effector on the workpiece during this interaction. The contact forces play a major role in the surface quality of the finished product and therefore need to be regulated to achieve the desired finish. However, when dealing with complex parts with free-form surfaces, such as the composite panel used in this thesis, controlling the contact forces would require more advanced force control methods. The aim of this research is to develop a hybrid force/position control methodology to enhance the quality and efficiency of the sanding process and control the forces applied on the workpiece. The first aim of this thesis is to develop a simulation model for the KUKA KR6 R700 used in this study. The model is developed in MATLAB Simulink using the forward and inverse kinematic model of the robot. The sanding motion is simulated with a visual representation, and the response and efficiency of the designed hybrid controller are tested. The forces and torques at the end-effector during the sanding motion are estimated. The estimated forces/torques are validated and used as feedback to the force control loop. The designed model can successfully estimate the forces at the end-effector using the Jacobian, and also control the contact forces and torques applied on the workpiece based on a defined setpoint. In the second part of this thesis, the designed hybrid force/position controller is experimentally implemented on a 6-axis robot. A 6-axis force/torque sensor is mounted at the end effector to measure the forces and torques applied on the workpiece when the robot performs the sanding motion. A PI controller is used for the force control loop, and a PD controller is used for closing the position loop. The sanding process is also performed using the KUKA ForceTorqueControl software package, and the results and limitations are discussed. It is shown that the designed controller effectively maintains a constant contact pressure between the sanding tool and workpiece, which is crucial for achieving a uniform surface finish.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".