Intelligent Mapping of Stresses in a Hydraulic Crane

Intelligent Mapping of Stresses in a Hydraulic Crane Hamid Roozbahani1, Appiah-Osei Agyemang 2, Farid Alijani 3, Takao Nishiumi4, Heikki Handroos 5 1

P.O.Box 20, Lappeenranta, FI-53850, Finland; [email protected] P.O.Box 20, Lappeenranta, FI-53850, Finland; [email protected] 3 P.O.Box 20, Lappeenranta, FI-53850, Finland; [email protected] 4 6-16-3 Noukendai, Kanazawa-ku, Yokohama, Kanagawa, 236-0057, Japan; [email protected] 5 P.O.Box 20, Lappeenranta, FI-53850, Finland; [email protected] 2

ABSTRACT In this paper, the application of intelligent control to determine stresses in a hydraulic crane is analyzed. The paper also focuses on the use of neural networks as possibilities of developing algorithm to map stresses on a hydraulic crane. The intelligent algorithm was designed to be a part of the system of a crane, the design process started with solid works, ANSYS and co-simulation using MSc Adams software which was incorporated in MATLAB/Simulink. Finally, MATLAB Neural network (NN) was used forstress optimization process. The flexibility of the boom accounted for the accuracy of the maximum stress results in the ADAMS model. The flexibility created in ANSYS modelproduced more accurate results compared to the flexibility model in ADAMS/View using discrete link due to high accuracy of the meshing in the ANSYS model. The compatibility between ADAMS and ANSYSsoftwares was paramount in the efficiency and the accuracy of the results. Von Mises stress analysis is more suitable due ductility of the steel. The quality of the Von Mises analysis is attributed to the repeated tensile and shear loading. In this paper, we further compare the neural network predictions for the maximum stressestoADAMS-MATLAB co-simulation results. According to results obtained fromthese studies, it can be concluded that results obtained from neural network model is promising and therefore neural network model can be utilized in the mapping of stresses in a hydraulic crane instead of ADAMS MATLAB co-simulation method which is time consuming, tedious and expensive. Keywords: Hydraulics, Crane, Stresses, co-simulation, neural network. 1. INTRODUCTION Hydraulic systems consists of different components which are used to control the position or speed of resisting loads and capable of transmitting power by using pressurized liquid to transfer energy into motion.[1]. Hydraulic boom has a wide range of applications including mining industries, construction, agriculture (mainly forestry) and many more due to its ability to resists high loads with low inertia, minimize vibration and shocks.[2]. A lot of researchers have investigated the fatigue stress and fatigue life of cranes. Mikkola et al. investigated the use of MSc. ADAMS software to analyzed dynamic system simulation to obtain stress history in ANSYS finite element software. The estimation of the fatigue life was based on rain flow analysis and fussy logics which was used to improve the fatigue life after comparing the results with the strain gauge measurements on the real boom. The researcher concluded that the co-simulation of ANSYS and ADAMS programs were efficient and accounted for accurate results due to stress analysis [3].Similarly, SasuMakinen carried out a research on improving fatigue life of log crane using Fuzzy Logic to conduct more intensive tests on a prototype and the results were compared to the design model [4]. In spite of numerous papers written on the stress analysis on hydraulic crane using intelligent control, the research on method adopted in this paper is limited. This paper discusses co-simulation between ADAMS and Matlab/Simulink. The hydraulic systems have been designed in ADAMS due to mathematical Model of the system. The crane has been modeled in MSc. ADAMS and the parameters are transferred to MATLAB to perform co-simulation which plays a vital in controlling the dynamics of the system. Shabana et al. have investigated about different methods of finding the flexibility of a crane and these methods presented are used purposely for non-linear equations for precise description and it difficult during the controlling of the boom [5]. The hydraulic crane investigated consists of steel welded components put together. Fluctuations occur during loading which require fatigue assessment [6]. The paper employs hot spot stress method which is considered as efficient and straightforward structural stress distribution[7].In multibody simulations, the results obtained are just rough estimate of the stress distribution even though the method is efficient and computationally effective unless special techniques. Maximum principal hotspot and Von Mises hotspot stresses analysis method have been adapted to find the highest in the simulation process. In this paper, we attempts to develop method of mapping stresses on a hydraulic crane by applying neural networks. The results are then compared to the Adams MATLAB co-simulation. The neural network was useful in the estimation of the stresses using Hot spot approximation method. The best performance values were obtained based on factors such as the data for testing, training and validation in the neural network. 2. Hydraulic Crane under Investigation The hydraulic crane under investigation is shown in Fig.1. The crane is constructed steel FE-510 of steel grade S355 at the Laboratory of Intelligent Machine, Lappeenranta University of Technology for experimental purposes.

Fig.1 the Hydraulic Crane under Investigation The cylinder is double acting with diameter of 100mm and piston diameter of 15mm and the hydraulic boom has dimensions of 4125-100-150mm.

Solid work

Adams Model Neural Network

Ansys

Matlab

Fig.2 Sequential order of the method used The crane has been modeled in Solid works and transferred to ADAMS to add various joints to the crane and also hydraulic components have been taken into accounts considering ADAMS model. Two methods have been applied to deal with the flexibility of the boom, first the solid work was transferred to ANSYS software to calculate the flexibility of boom before ADAMS model and the second method was to find the flexibility directly in ADAMS using discrete links. However, cosimulation between ADAMS and Mat lab SIMULINK was performed and it has been very helpful in the model to deal with the control aspect which derives the dynamics modeling of the system. 3. System Modeling The schematic of a double acting hydraulic actuator is shown in Fig.2. The force produced by the actuator is given by FS=(𝑝𝑝1 𝐴𝐴1 − 𝑝𝑝2 𝐴𝐴2 ) − ∑ 𝐹𝐹µ

(1)

Fig.3 Modeling a double acting hydraulic

The theory behind the force produced by a cylinder is based on calculated chamber pressures and the cylinder and piston area. The principle is clearly shown in equation (2). Fs=(𝑝𝑝1 𝐴𝐴1 − 𝑝𝑝2 𝐴𝐴2 ) − ∑ 𝐹𝐹µ

(2)

Fµ= ξ(ẋ)(𝑝𝑝1 𝐴𝐴1 − 𝑝𝑝2 𝐴𝐴2 )(1-η)

(3)

Damping the vibrations of the cylinder is influenced significantly by the friction force which is caused by the contact between the seal material and the cylinder wall.The hydraulic friction force can be expressed as the force formed by pressure difference between piston chambers and velocity of the piston.

Where, ξ (ẋ) is the velocity dependent

Continuity Equations One of the typical applications of the law of conservation of energy which state that energy is neither created nor destroyed is the continuity equation. This is as results of the pipe in which the flow takes place. Figure 24 shows an example of continuity equation in a hydraulic system with different equation. Volume flows 𝑄𝑄1 = 𝐴𝐴1 𝑥𝑥̇ Where,

𝑄𝑄2 = 𝐴𝐴2 𝑥𝑥̇

(4)

𝑄𝑄1 and 𝑄𝑄2 are the flow rate, and 𝐴𝐴1 and 𝐴𝐴2 the areas of the cylinders and piston, while 𝑥𝑥̇ is the piston position.

Differential Pressures 𝑝𝑝1̇ = 𝑝𝑝2̇ =

𝐵𝐵𝑒𝑒1

𝑉𝑉1 +𝑥𝑥𝐴𝐴1 𝐵𝐵𝑒𝑒2

(𝑄𝑄𝐴𝐴 − 𝑄𝑄1 )

𝑉𝑉2 +(𝐻𝐻−𝑥𝑥)𝐴𝐴2

(𝑄𝑄2 − 𝑄𝑄𝐵𝐵 )

(5)

Where, p and p are the differential pressures 4. Co-Simulation According to Yongxian, co-simulation method is time saving and the cost of designing Mechatronic system is less, the simulation process can be easily adjusted and changes be done to ensure better mechanical control system while the results is easily observed by the designer[8].

Fig. 4 Co-simulation inputs and output The forces in ADAMS is set to VARVAL function in the state variable as: Fin = VARVAL(FMatlab ).VARVAL(variable) is the ADAMS function that returns the value of the given variable. The description of the co-simulation process is typically shown in Fig.4 This force is an output for the Simulink model and at the same time an input for the ADAMS. Velocity and Position are the inputs for the MatLab and outputs for ADAMS. The nonlinearities in the system can be attributed to deformations and strains, material behavior or the effect of contact or boundary conditions. The export creates some files in the common ADAMS-MATLAB working directory which is saved and this file is ready to be used in MATLAB.

4.1 MATLAB Interface ADAMS block is created automatically in the MATLAB/Simulink which is integrated in the control system. The control system consists of PID and the typical values used for the control systems has been described in Table 1 and these values responded perfectly, following the input pulse signal well. The input and output connections were recognized in the Simulink. The ADAMS system employs m-file which emerges together with the control system.

Fig. 5 ADAMS/MATLAB Co-Simulation Block The integration step size was important in this simulation analysis and ODE 45 Dormand-Prince integration method is recommended with variable step size using continuous mode. However, the period, amplitude, pulse width and frequency are other factors that had significant effect on the pulse signal.

Table 1. The Best PID input values for the co-simulation PID

Values

Kd Ki Kp

0 0.2 20

Fig.6 Position from Co-Simulation

Fig.7 Velocity from co-simulation

Fig.8 Pressure P1

Fig.9 Pressure P2

Fig.10 Piston Position

Fig.11 Hydraulic Force 5. Stresses There were some points have been marked on the real boom with strain gauges. These points were measured and modeled in ANSYS as nodes. The results of the stresses were based on these points marked on the boom.Fig.1 shows some of the nodes measured from the real boom and modeled in ANSYS. However due to computational difficulties all the nodes could not be used so about 100 nodes were placed on the nodes in the Ansys Software. The stresses were obtained after co-simulation in which the data produced from pressure P1, pressure P2 and the position had been stored and imported to ADAMS. ADAMS computed all the stresses based on the data from the Simulink. The model was simulated for 10 seconds and principal stresses were analyzed. As it can be seen from Table 2, the top ten hot spots for maximum principal stress were estimated. The maximum hot spot for maximum principal stress occurred at node 480 and the corresponding value was 113.133MPa.

Table 2.Ten Hot Spot for the maximum principal stresses.

Fig 12 Simulated maximum Principal Stress Similarly, the ten hot-spots for Von Mises stresses has been analyzed after running the model for 10 seconds in ADAMS. The maximum hot spot Von Mises stress was estimated as 112.546 MPa. Table 3, shows the ten hot spots for Von Mises stresses calculated. However, the maximum hot spot for Von Mises stress was lower than the maximum principal stress and these values were 112.546 and 113.133MPa respectively. The maximum hot spot for Von Mises was considered for further analysis in the neural network due to the fact that the boom was made of steel and therefore, principal stresses were not suitable for the analysis due to its inability to withstand ductile materials

Table 3.Ten Hot Spot for the Von Mises stresses.

Fig.13 Simulated Results for the hot spot Von Mises Stresses 6.0 Neural Network The accuracy of the neural network training depends on factors such as the normalization of the data, data division, type of training algorithm and many more. After successful training of the neural network using Hot Spot approximation described in Fig.14 to obtained the needed results in Fig 15 and 16 respectively.

Fig.14 Hot-Spot Stress Approximation process

Fig.15 Training Neural Network Error

Fig.16 Simulated Von Mises Stress from ADAMS and NN 7. Conclusions Recent studies have shown that most cranes do not last long due to lack of knowledge about fatigue resistance due to loading cycle. This phenomenon is more challenging in the forest industry. Therefore there is need for research in stress analysis on cranes which gives in-depth knowledge of stress mapping in cranes. In this study, co-simulation model of one DOF hydraulic boom between ADAMS and MATLAB was performed. The design interface between mechanical system and control system was good for the response performance. These two softwares were enough for enhancing dynamic performance of hydraulic boom, improving efficiency, reducing cost and saving time. The flexibility of the boom accounted for the accuracy of the results. The flexibility created in ANSYS produced more accurate results than the flexibility model in ADAMS/View using discrete link. However it is easier to use discrete link to create flexibility without using software. The compatibility between ADAMS and ANSYS softwares was paramount in the efficiency and accuracy of the results. Therefore, according to this study the ADAMS/view flexibility analysis is good enough for stress and fatigue analysis. on Mises stresses analysis was more suitable for this research work because the hydraulic boom was made from steel.Hence,ductility of the material and the repeated tensile and shear loading of steel. The neural network predictions for the maximum stresses were then compared with the co-simulation results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the maximum stresses on the boom than the co-simulation. Bibliography [1] T. J. a. H. Jianhai, "Research on Extension Element Model in Hydraulic Systems," in International Conference on Mechatronics and Automation, Changchun, China, 9-12, August, 2009. [2] N. S. Amin Yazdanpanah Goharrizi, "A Wavelet-Based Approach to Internal Seal Damage Diagnosis in Hydraulic Actuators," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,, vol. VOL. 57, no. 5, MAY 2010. [3] A. R. H. H. Aki Mikkola, "Using the Simulation Model for Predicting Fatigue Stresses of a Log Crane," SAE Technical Paper, 1999. [4] S. Makinen, "Utilization of intelligent control systems in improving the fatigue life of log crane," Lappeenranta University of Technology, Lappeenranta, 2001. [5] A. Shabaha, "Flexible multibody dynamics: Review of past and recent development," 1997. [6] A. M. J. B. T. Rantalainen, "Sub-modeling approach for obtaining structural stress," Mechanical Sciences, vol. 4, pp. 2131, 2013. [7] Hobbacher, "Recommendations for Fatigue Design of Welded Joints and Components," in International Institute of Welding, Paris, 2008. [8] A. A. Shabana., "Flexible multibody dynamics: Review of past and recent development," 1997, p. 1(2):189–222.