Weight Optimization of Girderbeam for 70t E.O.T Crane Through Fe Analysis

Weight Optimization of Girderbeam for 70t E.O.T Crane Through Fe Analysis

1 Apeksha. K. Patel, 2 Prof. V. K. Jani,

1M.E.[CAD/CAM] Student, Department Of Mechanical Engineering, C.U.Shah College Of Engineering and Technology, Wadhavan, Gujarat

2 Professors,DepartmentOf Mechanical Engineering, C.U.Shah College Of Engineering and Technology, Wadhavan, Gujarat

ABSTRACT: Main Component of Overhead Crane is Girder Beam which transfers load to structural member. In Present Practice, industries overdesign girder beam which turns costly solution. So, our aim is to reduce weight of girder which has direct effect on cost of girder and also performance Optimization is done for fatigue (life) point of view. In this paper FE analysis of girder beam is carried out for the specific load condition. Here, we used ANSYS WORK BENCH V12.1.Software for the FE analysis of the girder beam. Through this analysis we get the result in terms of stresses and deformation and this result are within the allowable limits. We are optimize the weight of girder by use of TAGUCH I method.

ABSTRACT: Main Component of Overhead Crane is Girder Beam which transfers load to structural member. In Present Practice, industries overdesign girder beam which turns costly solution. So, our aim is to reduce weight of girder which has direct effect on cost of girder and also performance Optimization is done for fatigue (life) point of view. In this paper FE analysis of girder beam is carried out for the specific load condition. Here, we used ANSYS WORK BENCH V12.1.Software for the FE analysis of the girder beam. Through this analysis we get the result in terms of stresses and deformation and this result are within the allowable limits. We are optimize the weight of girder by use of TAGUCH I method.

Keywords70T double girder electrical overhead crane, weight optimization.

1. INTRODUCTION

Optimization is a mature field due to the extensive research that has been conducted over the last about 60 years. Many types of problems have been addressed and many different types of algorithms have been investigated. The methodology has been used in different practical applications and the range of applications is continuously growing.

Transcription of an optimization problem into a mathematical formulation is a critical step in the process of solving the problem. If the formulation of the problem as an optimization problem is improper, the solution for the problem is most likely going to be unacceptable. For example, if a critical constraint is not included in the formulation, then most likely, that constraint is going to be violated at the optimum point. Therefore special attention needs to be given to the formulation of the optimization problem.

Any optimization problem has three basic ingredients:

• Optimization variables, also called design variables denoted as vector x.

• Cost function, also called the objective function, denoted as f (x).

• Constraints expressed as equalities or inequalities denoted as gi(x)

The variables for the problem can be continuous or discrete. Depending on the types of variables and functions, we obtain continuous variable, discrete variable, differentiable and no differentiable problems. These models are described next; for more details and practical applications of the models, various references can be consulted. There are other optimization methods such as Shape Optimization and Topological Optimization, which change the appearance of the geometrical domain. The optimization approach in this study involved both size and shape optimizations. As discussed earlier, the optimization stages were considered not as a defined function of variables, but based on judgment using the results of the FEA and dynamic service load. The judgment was based on mass reduction, cost reduction, and improving fatigue performance using alternative materials and considering manufacturing aspects, as well as bending stiffness of the SteelGirder Beam.

1. Objective Function

   Objective function is defined as the parameters that are attempted to be optimized. In this study the weight, manufacturing cost and fatigue performance of the component were the main objectives. Optimization attempt was to reduce the

   weight and manufacturing cost, while improving the fatigue performance and maintaining the bending stiffness within permissible limits.

   Manufacturing process and material alternatives are other design variables that were not considered in this study. Since automotive Girder Beams are mostly manufactured from micro alloyed steels, this was considered as the alternative material. Micro alloyed steels have the main advantage of eliminating the heat treatment step in the manufacturing process, which will reduce the cost of the final Girder Beam. Other manufacturing aspects that are common in manufacturing of Girder Beams Including inducing compressive residual stress at the fillets were investigated to improve the fatigue performance of the component. This improvement would allow additional changes in the geometry in order to reduce the weight of the final optimized Girder Beam.

2. Outline of Optimization Process The shape optimization of components in dynamic mechanical systems requires several quantities. These quantities are to be derived in every iteration of the optimization process. They result from various types of analyses and the optimization process is obtained by a combination of these analyses. Figure 1 outlines the stages with respect to the order in which they are carried out during the batch process. In the following section some basic aspects of each step of the process shown above are described in more detail in order to provide the reader with the necessary background for all analysis domains involved.

   Fig 1.1 Stages of Optimization Process products and which in turn is also aggravated by the noise and vibration produced.

3. ThreeParameterforOptimizat ion

   Diaphragm to diaphragm distance Thickness of web plate for girder beam Height of Girder Beam

4. Optimization Array

Table 1.1 Optimization ArrayCase6 Diaphragm to diaphragm distance: – 1200 Thickness of web plate of girder beam:-30 Height of Girder Beam:-800

1.5.1 Von Misses Stresses

Fig 1.2 Von Misses Stress of Case-9 1.5.2Maximum Shear Stresses

Fig 1.3 Maximum Shear StresofCase-9

1. Total Deformation

   Fig 1.4 Total Deformation of Case-9

   Case 5

   Pieces Length: -1200 Thickness of Girder Beam: – 22 Height of Girder Beam: – 900

2. Von misses stresses

   Fig 1.5 Von Misses Stress of Case-7

3. Maximum Shear Stresses

   Fig 1.6 Maximum Shear Stress of Case-7

4. Total Deformation

   Fig 1.7 Total Deformation of Case-7

   Case 4

   Pieces Length: -1000 Thickness of Girder Beam:-30 Height of Girder Beam: – 700

5. Von Misses Stresses

   Fig 1.8 Von Misses Stress of Case-6

6. Maximum Shear Stresses

   Fig 1.9 Maximum Shear Stress of Case-6

7. Total Deformation

   Fig 1.10 Total Deformation of Case-6

   Case 3

   Pieces Length: -1000

   Thickness of Girder Beam:-Height of Girder Beam: – 800

8. Von Misses Stresses

   Figure 1.11 Von Misses Stress of Case-4

9. Maximum Shear Stress

   Fig 1.12 Maximum Shear Stress of Case-4

10. Total Deformation

    Fig 1.13 Total Deformation of Case-4

    Case 2

    Pieces Length: -800 Thickness of Girder Beam:-26 Height of Girder Beam: – 800

11. Von Misses Stresses

    Fig 1.14 Von Misses Stress of Case-2

12. Maximum Shear Stresses

    Fig 1.15 Maximum Shear Stress of Case-2

13. Total Deformation

    Figure 1.16 Total Deformation of Case-2

    Case 1

    Pieces Length: -800 Thickness of Girder Beam:-22 Height of Girder Beam: – 800

14. Von Misses Stresses

    Figure 1.17 Von Misses Stress of Case-1

15. Maximum Shear Stresses

    Fig 1.18 Maximum shear stress of Case-1

16. Total Deformation

    Fig 1.19 Von Misses Stress of Case-1

17. Main Effect plot for Means

    Figure 1.20 Main Effect plot for Means

18. Main Effect plot for SN ratio

Figure 1.21 Main Effect plot for SN ratio

Conclusion

Pieces length

Thickne ss

Pieces length

Thickne ss

Height

Height

Von Misses Stresses

Maximu m Shear Stresses

Total Deform ation

Von Misses Stresses

Maximu m Shear Stresses

Total Deform ation

Weight

Weight

1. Optimization result.

   (mm)	(mm)	(mm)	(Mpa)	(Mpa)	(mm)	(Kg.)
   800	22	700	26.411	13.612	0.67913	8125
   800	26	800	36.016	18.562	0.92609	8614
   800	30	900	43.219	22.274	1.1113	9125
   1000	22	800	48.021	24.749	1.2348	8400
   1000	26	900	57.625	29.699	1.4817	8800
   1000	30	700	62.427	32.174	1.6052	8514
   1200	22	900	69.63	36.016	1.7904	8314
   1200	26	700	74.432	38.362	1.9139	8547
   1200	30	800	76.833	39.599	1.9757	8874

   Here from above table and graphs conclude that case 6 is optimum solution.

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