Abstract

Chainsaw kickback is a serious safety concern for both experienced and novice operators. A key to developing improved kickback control systems is a better understanding of saw motion during kickback and the development of improved methods for distinguishing kickback from normal saw operation. In this study, accelerometers and gyroscopes were mounted to a battery-powered electric chainsaw and to a midsize, gasoline-powered chainsaw, and data were collected during normal cutting and kickbacks. These sensors measured accelerations along the guide bar and perpendicular to the bar as well as rotational velocities toward the operator's torso. Results from the battery-powered saw showed that accelerations during normal cutting and kickbacks had peak magnitudes of from ~2 to ~6 g and from ~6 to ~8 g, respectively, and that rotational velocities typically reached over 600°/s during a kickback. Analysis of these results showed that the gyroscope alone, using a threshold value of 300°/s, was effective in distinguishing normal cutting from kickback. Results from the gasoline-powered saw showed the same general trends as those with the battery-powered saw; however, the rotational velocities during a kickback were greater, typically exceeding 1,000°/s. Through the use of machine learning techniques, a more effective method than a simple threshold for distinguishing kickback from normal saw operation was developed. Using this method, kickback was determined very reliably and often when the deviations from the rotational velocities corresponding to normal cutting were small. Implementation of these findings could lead to improved kickback control systems on chainsaws.

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