DECLARATION 2 PUBLICATIONS
3.4 Methodology
3.4.1 Applicable analytical methods
Numerical analyses have proven useful in saving costs and time associated with “trial and error”
experimentation over the years. This study employs the 3D elastic-plastic thermo-mechanical FE model with phase transformation effects to analyse the transient heat transfer process of SAW.
The significant role played by volumetric expansion in influencing the WRS fields through phase transformation during the arc welding thermal cycle was discussed in Chapter 2. This study incorporates phase transformation effects during modelling of a multi-pass circumferential weld bead, which is expected to improve the agreement between experimental and numerical results of WRS distribution in the welded structure. DoE strategies are helpful in situations where a limited number of experiments can be conducted, mainly due to cost and time constraints, such as in this study. Multi-objective evolutionary algorithms (such as the one used in this study) are capable of solving multiple objective problems by simultaneously providing a set of non- dominated feasible solutions in a single run of an algorithm. The literature review showed that most studies in the field have applied aggregating methods in parametric optimisation of the arc welding process. The challenge of that approach, however, is it leads to only one “optimal”
solution. The solution is based on pre-determined prioritisation of the effects of individual variables on the ultimate outcome. A solution obtained in this way cannot be optimal for all conditions of affected variables.
This study combines DoE strategies with a multi-objective optimisation algorithm to produce a set of solutions that hold true for all conditions of affected variables. The generated solutions are derived through the analysis of data from a limited number of experiments. This approach, as well as the results obtained through this study, are expected to add value to the relevant body of knowledge.
3.4.2 Relationships of interest to the study
It is clear from the review in the preceding chapter that several studies have successfully defined the cause-and-effect relationship between WRS and fatigue strength of a welded structure.
However, WRS itself is influenced by several factors. Anca et al. (2010) observed that several factors influence the magnitude of the residual stresses and their distributions; these factors include the type of welding, number of passes, material properties and degree of constraint or restraint. Leggatt (2008) stated that residual stress is affected by many factors, including the geometry of the parts to be joined; the use of fabrication aids such as tasks, cleats and jigs; the pass sequence for multi-pass welds and the welding sequence for structures with more than one
69 | P a g e weld. Furthermore, material properties – such as the coefficient of thermal expansion, yield strength, and metallurgical phase change – may also influence residual stresses.
This study takes the approach that the first step towards improving the fatigue properties of a welded structure in relation to WRS is to understand the welding factors that influence the generation of WRS. Secondly, such factors must be improved or optimised, thereby ensuring improved or optimal WRS conditions within the welded structure. The mean stress effect from the WRS fields will then be reduced, resulting in favourable fatigue properties of the welded structure. Figure 3.1 illustrates the cause-and-effect relationships that underpin the reasoning in this approach.
Figure 3-1: The Cause-and-Effect Relationship Between WRS and FAT
3.4.3 Hybrid model used in this study
A hybrid methodology that includes computational methods, empirical calculations, non-linear mathematical modelling and experimental measurements is used in this study. The methodological approach, and the discussion thereof in this chapter, can be divided into three parts. These parts are shown in Figure 3.2, which illustrates the framework, and are summarised below.
Part 1: Materials and experiments
i. The specification for all materials used in the study is discussed first. The preparation of the specimens and the welding conditions and procedures are also presented in this section.
Welding-Induced Residual Stress Welding
restraint
Welding parameter
s
Welding
sequence PWHT
Weld- piece geometry
Pre-heat Interpass
temperature Mechanical
properties
Fatigue Properties
70 | P a g e ii. The procedures followed in conducting the experiments, including temperature
measurements, WRS measurements, fatigue analysis, microstructure analysis and hardness testing, are presented in this section.
iii. Experimental validation of the numerical model is explained and discussed.
Part 2: Numerical analysis
iv. A finite element analysis (FEA) model is developed using the MSC software code.
v. A non-linear transient thermo-mechanical analysis, incorporating metallurgical effects, is performed in 3D formulation to establish residual stress and distortion distributions for various parametric compositions.
vi. Model is validated against the experiments performed in Part 1.
Part 3: Optimisation of parameters
vii. A multi-objective genetic algorithmic (MOGA) procedure is developed to solve the optimisation problem. The equations obtained from regression analysis are used as a multi-objective function for the MOGA.
viii. An optimal set of output responses and the required input characteristics are produced.
Figure 3-2: Research Methodology
Materials and Experiments:
- Material specification - Weld specimen geometry - SAW parameters
- Neutron diffraction
Numerical Analysis:
- 3D FE model
- Transient heat source - Thermo-mechanical model - Phase transformation
Parametric Optimisation - DoE strategy
- MOGA procedure - Pareto set
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