Structural design of an agricultural backhoe using TA, FEA, RSM and ANN

Abstract

An agricultural backhoe is an important machine designed for multiple assignments in agriculture and livestock. Due to severe working conditions, agricultural backhoe elements are subjected to high loads. Therefore, a structural design must provide a safe machine under all loading conditions at minimum weight and cost. In this work, a 3D model of an agricultural backhoe was proposed, to be used in tractors category II according to the classification of the ASABE S217 standard. In the structural design of the agricultural backhoe Theoretical Analysis (TA), Finite Element Analysis (FEA), Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were used. A finite element model of the agricultural backhoe in the critical position was developed, considering a maximum breakout force according to SAE J1179 standard. The finite element model was theoretically validated through a comparison between numerical and theoretical normal stresses at twelve strategic points of the agricultural backhoe components, finding a maximum absolute difference of 7.0 %. Also, a mass reduction of the principal backhoe components (bucket, arm, boom and links) was done, using Central Composite Design (CCD) under RSM in a commercial FE software and ANN technique with Neural Lab software. A mass reduction of the initial agricultural backhoe model of 24.8% (from 446.3 kg to 335.4 kg) was achieved using the RSM technique, with an increment on the maximum von Mises stress of 6.8% (from 117.4 MPa to 125.4 MPa), as well as a reduction of the minimum safety factor of 4.8% (from 2.94 to 2.80). ANN allowed predicting the results obtained by RSM to reduce the boom mass with a correlation coefficient of 0.96, using 80.0% of data and around 13.0% less time. This study showed that a combination of RSM and ANN techniques with TA and FEA provides useful results to reduce the structural mass of agricultural equipments, thus it is recommended to decrease the number of numerical case studies and the solution time with satisfactory results.