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Identification of the forces on a bulldozer ripper with a neural networksmethodology

MIGUEL CURINHA SAMARRA and LUIS MANUEL ROSEIRO

Department of Mechanical EngineeringCoimbra Institute of Engineering Polytechnic Institute of CoimbraRua Pedro Nunes Quinta da Nora 3030 199 Coimbra - Portugal

[email protected]

MARIA AUGUSTA NETODepartment of Mechanical Engineering

Faculty of Science and Technology - University of CoimbraRua Luis Reis Santos, Pinhal de Marrocos, 3030-788 Coimbra - Portugal

TONY DA SILVA BOTELHO

Supmca/LISMMASchool of Mechanical and Manufacturing Engineering3, Rue Fernand Hainaut - 93407 Saint-Ouen - FRANCE

Abstract: - This paper presents a methodology for monitoring forces acting on a bulldozer ripper to avoidoverloading and fracture in service conditions. The methodologywasbuilding and tested using experimentaldatacollected in a laboratory conditions and numerical data from a developed finite element model. Using thesedata and the neural network technique it was possible to create an identification tool for the bulldozer ripper.The promising results obtained are presented and discussed.

Key-Words: - Finite Elements; Neural Networks; Structural Identification

1 IntroductionThe ripper is a mechanical component attached tothe rear of a trawl machine or bulldozer, and it mainfunction is to penetrate and tear soil, particularlywhere it is not possible to use small size machines.It is usually used for doing works of high stress andfast jobs, such as in quarries for stone revolver, inmines for ground penetration or in works oflevelling and uniformity of land. Normally, theripper is placed in the rear of the machine, operating

as a complement to the main unit (paddle), which ishydraulically driven. When ripper fracture occurs,given the size and costs associated with the ripperreplacement, companies choose to weld thefractured parts instead of the purchase of a newripper component. However, even under this

procedure, the costs are high. In fact, the only possible way to avoid undesirable downtime of the bulldozer is to have an available ripper forreplacement. When the welding process becomesunfeasible, another option is to produce a newripper. Nevertheless, the ripper fracture is a situation

that occurs frequently, and we strongly believe thatthe monitoring of forces during service conditions

could allow the machine operator to decide for thestop of the ripper work, instead of allowing thatripper force reaches threshold values for thematerial, and this would be a decisive way to reducecosts associated with their use. However, given therandomness and variety of soils that is possible toidentify, this is not a linear process and is not easyto implement. In fact, when the ripper is inserted inthe soil, it can encounter obstacles (usually stones)with varying dimensions that could be attacked bythe ripper in different positions.This paper presentsa procedurebased on the neural network techniqueto develop a methodology that should be able toidentifythe forces acting on the ripper underworking conditions. A scaled experimental setupwere developed and created to test the idea. Theneural networks were trained with the experimentaldata collected from electrical resistance strain gagesand tested from numerical data. The results are

presented and discussed.

2 Experimental SetupIn order to test the methodology idea, a scaled setripper - support structurewas developed and built.

Recent Advances in Systems Science and Mathematical Modelling

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The main idea was to develop the seconnection as similar as possibleconditions. Figure 1 showsa bulldoconditions. Moreover, this bulldozeas a base for the work presented.

Figure 1: Based Bulldozer wo

The 3D model of the set was crSolidworks program. The real geripper was accounted for and,in orthe machining process, the suppor simplified. In figure 2 is possiblegeometric models of the real andmodel of the ripper-support structure1:10 from the real one.

Figure 2: 3D Models of the set ripp

Using the information from

modelswas possible to create the ripstructures by means of a CNC macwas produced in 7022 aluminum allthe support of the machine in AISIThe monitoring of the ripper was asgage measurements. The ripper wawith 3 unidirectional strain gages,(Micro Measurements Group) ref250BG-120, grids made with constaauto-compensation on temperatureand a resistance of 120 ohms. Thfactor of calibration certificate is 2selection of gages locations in the rion the information collected fr

ripper-supporto the workinger in workingis considered

rking

eated with theometry of theer to facilitatestructure was

to see the 3Dthe simplifiedwith a scale of

er-support

he geometric

er and supportine: the rippery material and340 (figure 3).sured by strains instrumented, from Vishayrence EA-13-

tan alloy, withfor aluminium

nominal gain.1 0.5%.The per was basedm the ripper

numerical model.The numwas made with SolidWor

presented in section 3.

Figure 3: Model of ripper an

by the CN

Figure 4 shows the instlocation of the three unidir

Figure 4: Support and

The load applied to the rcompression load cell with

5 kN. The load cell is coTSTM 5kN AEP Transduccontrolled by electric-pnwith the monitored load cof a force from 10 N tosystem developed for tallows the rotation of thshown in figure 5.The exdistinct positions for the lto simulate a rock collision

Figure 5: Expe

rical model of the rippers simulation and will be

support structure produced

machine

umented ripper and thectional strain gages.

ripper instrumented

pper was monitoredby aa maximum rated load of

mercially designated asers. A pneumatic actuatorumatic valves, together

ell allows the application500 N. The mechanicale application of loads, pneumatic cylinder, as

erimental setup providesad application, intendingto the ripper.

imental Setup


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The angles at which the force can befrom 0 to 90 using eight discrete10, 20, 30, 45, 60, 70, 90]. The datausing a National Instruments acqreference NI USB-9162, with

programming (figure 6). Noticehorizontal force was applied to the rivary from 0 (geometric plane of th(perpendicular plane of the ripper).connected to the board in aconfiguration and the load cell is co

bridge. According to the applied forvariation of the deformation is betwe252,00 .

Figure 6: LabView program used in thSetup

3 Numerical ModelThe numerical study of the ripperthe software Solidworks Simulation.model of the ripper - support structu

by using parabolic tetrahedral f(SOLID element inthe software librand three degrees of freedomtranslations in the three orthogonalholes that provide the external cosupport were used to provide

boundary conditions. In fact, allfreedom of the nodes located in thewere fixed(all degrees of freedom r connection between the ripper anstructure is modeled by a pin with hicontacts between the surfaces of thesupport structure were modeled

penetration." The load application tothe experimental test conditionsload. The finite element model ifigure 7.Figure 8 shows the vadeformation on the Ripper forthsituation. The numerical results arethose acquired experimentally in figshow a good correlation, with erro7%.

applied rangedossibilities [0,were acquired

uisition board,a LabViewthat only a

per, which canripper) to 90

he gages werequarter-bridgenected in full-

e, the range ofen4,23 and

e experimental

as done usingThe numericale was obtainedinite elementsry - ten nodeser node, theirections). Thenection of thethe numericalhe degrees ofholes surfaces

estrained). Thed the supporth rigidity. Theripper and thethrough "no

ok into accountith controlledillustrated in

iation of thefrontal load

compared withre 8, and they

rs from 1% to

Figure 7: Numerical model

Figure 8: Distribution of the

Figure 9: Comparisonbetnumerica

4 Identification Me 4.1 Selection of NeuralSince early,has beennetworks offer a numberapplication in the field ofidentification problems. Sneural networks is the abexamples, they do notknowledge and can appronon-linear continuous fuseveral architectures usedtype neural networks, sch10, have been consididentification problem of t

Figure 10: Feedfor

0100200300400500600700

100 200 30

D e

f o r m a

t i o n

[ ]

Force [N

of the experimental Setup

deformation on the ripper

ween Experimental andl results

hodology

Networksecognized that neuralof potential benefits forngineering, especially forme appealing feature oflity for learning through

require any a prioriimate, arbitrary well, anynction [1]. Among thein practice, feedforward

ematically shown in Fig.ered suitable for thee signature analysis.

ardneural network

0 400 ]

A - Experimental

A - Numeric

B - Experimental

B - Numeric

C - Experimental

C - Numerical


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A feedforward neural network consists of severallayers; each one with some processing elementscalled neurons, linked to each other by weights. Theweights determine the nature and the strength of theconnection between the neurons. The number ofnodes considered in the input and output layersdepend on the specifications of the problem. Thenumber of hidden layers, the number of neurons ineach hidden layer, as well as the activation functiontype for each neuron is selected according to theexperience and some convergence criterions.Theapplication of artificial neural network consists oftwo stages, namely training and testing. During thetraining stage an input-to-output mapping, using theavailable sample data, is presented to the network.The network evaluates its own output based on the

presented input and compares this value with the

target (presented) output. The actual output error(the sum of squares error function in this study) isused to adjust the node weights so that the error can

be reduced. The learning stage stops once a crossvalidation pre-set error threshold is reached and thenode weights are frozen at this point. During thetesting stage, data that have not been presented tothe network in the learning stage are provided asinput and the corresponding output is calculatedusing the fixed node weights.

4.2 Neural Networks Considered

Normally, in neural network structural identification problems,the authors use numerical data to train thenetwork and experimental or numerical data to testit. In this work the training and cross validation

process are executed with the collected experimentaldata (force obtained from the load cell and thecorresponding deformation collected from the straingages). After this validation phase, the mainobjective is to estimate the horizontal force F and

position relatively to the ripper as shown in figure11.

Figure 11: Definition of the neural network output

variables

It is not possible to say exactly how many layers andneurons in inner layers are suitable for properaccuracy, but the data training should berepresentative of a broad class of possible input-output pairs. Thus, it is important that the networksuccessfully generalize from the entire populationthat was used to learn. For the ability of successfullygeneralization of a network it is important thatintern parameters of the net (weights and bias) must

be less than training patterns [2]. In this study, fourlayers are considered. The schematic neural networkis present in figure 12.

Figure 12: Configuration of the neural network used.

A total of 4000pattern data sets were acquired. Theneural network was programmed (definition andtraining) in the Matlab program. The number ofneurons in the input and output layers have a directdependence of theinput and output data type desired.In the inner layers, the choice of the number ofneurons is based on experimentation with aminimum number that allows achieve the desiredlearning. The training of the neural network have

been performed with a second order type algorithm,the Levenberg Marquardt [3] andseveralcombinations of number of neurons in the innerlayers were tested.

4.3 Obtained Results75% of the acquired data were used to train thenetworks and 25% for the cross validation process.For each network tested,the selection of the data totraining, validation and test were random.Afterward, the network was tested with100 retainedexperimental and numerical data.Table 1 shows themean relative error of the identification processgiven by the neural network, errors showed to beless than 9%.

Table 1: Identification Results

Testing Data

IdentificationMean Relative Error

[|Net-Real|/Real x 100]

Force Position

Numerical 7,34 8,05

Experimental 1,66 6,34


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4 ConclusionIn this study, a ripper-support assembly was

produced in a CNC machine andintended torepresent in a laboratory environment the behaviourof a bulldozer ripper. Experimental and numerical

study of the ripper support has been carried out,with a good correlation among results. Following,anartificial neural network was developed and trained.The neural network development was based onsome experimental data collected from strain gagesand a load cell. Moreover, some of the experimentaland numerical datawere used to test theneuralnetwork. The neural network identificationerrorsthat were verified represent a good promisethat it will be possible to develop a methodology

based on neural networks for real-time monitoringof the forces acting on the ripper. Furthermore, these

data will also be extremely useful in the optimizingof the ripper geometry.

References:[1] K. Hornik, H. Stinchcombe and H. White,

Multilayer Feedforward Networks areUniversal Approximators, Neural Networks,Vol. 2, 183-192, 1989.

[2] M.T. Hagan, H.B. Demuth e M. Beale, Neural Network Design, PWS Publishing Company,USA, 1996.

[3] M. Hagan and B. Menhaj, TrainingFeedforward Networks with the MarquardtAlgorithm, IEEE Trans. on Neural Networks,5, 6, 989-993, 1994.


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