Modeling Human Expertise in a Sanding Task

Abstract

Objective

Sanding strategies were identified that correlated with better performance and less physical stress.

Background

Sanding requires manual skill because of the dexterity and perceptual needs to perform the job well, and it involves exposure to physical stresses.

Methods

Force and tool kinematics were measured using an instrumented platform and motion capture for 20 participants while performing a timed paint removal sanding task in the laboratory. Performance was based on the quantity and uniformity of the paint removed and measured using image processing. The ACGIH Threshold Limit Value (TLV) for Hand Activity Level Peak Force Index was used to evaluate physical stress.

Results

Linear regression and machine learning algorithms revealed that high performers (above the 75th percentile) moved the sander more, while they varied but applied greater force compared to the low performers (below the 25th percentile). Some high performers could maintain a TLV below the acceptable limit by pausing more and applying less force but performing more frequent exertions.

Conclusions

More force, greater variations of force, and speed were related to better sanding performance, while less force and more frequent exertions and pauses helped reduce physical stress.

Applications

Strategies identified in this study may be useful for training operators in sanding tasks for performance and safety.

Keywords

surface finishing regression modeling feature extraction discomfort manufacturing

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