Balancing intended collaboration and safety requirements during the design and implementation of an industrial cobot application: A case study
Bibliographic record
Abstract
The aim of this article is to illustrate, through a real-life case study, the importance of balancing the intended human-robot interactions and occupational health and safety (OHS) requirements when designing and implementing a collaborative application involving industrial robots (in the sense of ISO 10218).This article discusses the obstacles and efforts involved in achieving greater but safe collaboration by chronologically detailing the risk reduction approach.A consortium of manufacturers commissioned the National Research Council Canada (NRC) to implement a TRL-5 cyberphysical finishing cobotic platform for companies of the metal industry.By finishing, we mean polishing, grinding and deburring tasks on metal parts.While the cobot performs the finishing tasks, the human operator supervises the process, oversees the quality control of the parts and indicates which production lot should start.The main reasons behind the consortium's request were: 1) the need to alleviate the musculoskeletal disorders among the finishing operators, 2) the labor shortage and 3) a drive to innovation.The robotic platform consists mainly of a support table with a dust extraction system, a 6-axis cobot arm attached to a rail to add a 7th axis, accessories and tools for part finishing (e.g.compliance head, abrasive discs).The industrial robot used was a UR10 cobot.One of the initial objectives was for the finishing platform to be collaborative to ensure flexibility and fluidity of operations.Typically, a needs analysis and a risk assessment help determine the feasibility and the usefulness of a collaborative application.However, in the case of the NRC's laboratory application, a different approach was taken.The pursuit of innovative solutions to increase flexibility in finishing activities required to push the limits of technology and to explore what is achievable and what is not.In fact, such an approach is frequently employed in the industrial context.Thus, an iterative risk assessment was carried out once the platform structure has been assembled.First, fifty-five risks have been identified, including mechanical risks coming from the cobot arm movements and the disc rotation.Regarding those mechanical risks, the protective measures the OHS team suggested allowing the operator to be close to the functioning cobot were 1) a reduced speed of the moving cobot arm to 16 mm/s (PL = d, category = 3), 2) a protective stop, of the moving arm, triggered by a PLe, cat. 3 Airskin "sensitive skin" in the event of contact, 3) PLe, cat. 4 emergency stop buttons and 4) a protective stop, of the rotating disk, triggered by two PLd, cat. 3 laser scanners in the event of intrusion into the safeguarded space.Personal protective equipment (PPE) has also been suggested.The residual risk was then considered acceptable only if the disc was at a standstill.However, the protective measures concerning the rotation of the disc conflicted with the collaborative application intended by the consortium and the expressed need to approach the rotating disc during finishing to perform a visual quality control of the part (operator positioned approximately 200 mm from the tool).As a result, the solution proposed by the OHS team at this new stage was to allow access to the zone using a dead man's switch (DMS) to bypass the scanners' protective stop, combined with wearing close-fitting clothing, tying back hair, and wearing PPE to protect against the risk of projections and gloves to protect against the risk of cutting with the disc.Wearing gloves was intended to reduce residual risks since contact with the disc remains possible (e.g.free hand, inertia of the disc to stop).To verify the actual hand protection provided by gloves, five models offering high mechanical resistance were tested with two abrasive discs in different configurations.The tests consisted of rotating the disc at 9,000 rpm (operating speed), bringing it close to the glove at a speed of 16 mm/s with the cobot arm, then touching the glove for a fraction of a second to simulate unexpected contact (withdrawal of the arm when the force sensor measures 70 N in contact with the support inserted into the glove).The results were unequivocal: all gloves were cut or torn.Thus, wearing gloves will not protect the operator in the event of contact with the rotating disc on the finishing platform.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".