Predicting the performance characteristics of assembled rollover protective structure designs for tractors.

Tractor overturn fatalities are the single leading cause of agricultural fatalities in the United States. Approximately 100 people die each year as the result of a tractor overturn. Effective engineering controls to mitigate injury and prevent death from tractor overturns are available in the form of rollover protective structures (ROPS) and seatbelts. However, approximately 50% of all tractors in the United States are without ROPS. Cost of ROPS is frequently cited as a reason why tractor owners do not have ROPS installed. Cost-effective ROPS (CROPS) have been developed with the intent to lower the cost of retro-fitting a tractor with ROPS. Cost reductions were achieved by using common structural components (e.g. fasteners, tubing, plate) and eliminating welding from the CROPS assembly process. A CROPS design was developed and fabricated for a Ford-3000 tractor. Experimental data demonstrates that the Ford-3000 CROPS prototype successfully completed static testing according to consensus standard SAE J2194. A finite element analysis (FEA) model was developed for the Ford-3000 CROPS prototype based upon SAE J2194 experimental data. The model predicted longitudinal load at energy criterion within 10% and transverse load at energy criterion within 5%. At the longitudinal loading simulation end point, energy absorbed in the simulation differed from experimental energy absorbed by 2%. For transverse loading this value was 9%. Probabilistic design techniques were utilized with the model to evaluate the effect of expected variation in Ford-3000 CROPS geometric and material properties on standard testing performance. Simulations were conducted for both SAE J2194 and OSHA 1928.52 ROPS test requirements. FEA screening tests were performed to identify statistically significant input variables. A central composite design (CCD) of experiments was used to build response surfaces for output variables of interest. Ten-thousand Monte Carlo simulations were performed using the