Determinants explaining the variability of hand-transmitted vibration emissions from two different work tasks: grinding and cutting using angle grinders.

BACKGROUND There are numerous factors including physical, biomechanical, and individual that influence exposure to hand-transmitted vibration (HTV) and cause variability in the exposure measurements. Knowledge of exposure variability and determinants of exposure could be used to improve working conditions. We performed a quasi-experimental study, where operators performed routine work tasks in order to obtain estimates of the variance components and to evaluate the effect of determinants, such as machine-wheel combinations and individual operator characteristics. METHODS Two pre-defined simulated work tasks were performed by 11 operators: removal of a weld puddle of mild steel and cutting of a square steel pipe. In both tasks, four angle grinders were used, two running on compressed air and two electrically driven. Two brands of both grinding and cutting wheels were used. Each operator performed both tasks twice in a random order with each grinder and wheel and the time to complete each task was recorded. Vibration emission values were collected and the wheel wear was measured as loss of weight. Operators' characteristics collected were as follows: age, body height and weight, length and volume of their hands, maximum hand grip force, and length of work experience with grinding machines (years). The tasks were also performed by one operator who used four machines of the same brand. Mixed and random effects models were used in the statistical evaluation. RESULTS The statistical evaluation was performed for grinding and cutting separately and we used a measure referring to the sum of the 1-s r.m.s. average frequency-weighted acceleration over time for completing the work task (a(sa)). Within each work task, there was a significant effect as a result of the determinants 'the machine used', 'wheel wear', and 'time taken to complete the task'. For cutting, 'the brand of wheel' used also had a significant effect. More than 90% of the inherent variability in the data was explained by the determinants. The two electrically powered machines had a mean a(sa) that was 2.6 times higher than the two air-driven machines. For cutting, the effect of the brand of wheel on a(sa) was ~0.1 times. The a(sa) increased both with increasing wheel wear and with time taken to complete the work task. However, there were also a number of interaction effects which, to a minor extent, modified the a(sa). Only a minor part (1%) of the total variability was attributed to the operator: for cutting, the volume of the hands, maximum grip force, and body weight were significant, while for grinding, it was the maximum grip force. There was no clear difference in a(sa) between the four copies of the same brand of each machine. CONCLUSIONS By including determinants that were attributed to the brand of both machine and wheel used as well as the time taken to complete the work task, we were able to explain >90% of the variability. The dominating determinant was the brand of the machine. Little variability was found between operators, indicating that the overall effect as due to the operator was small.