Mahyar Asadi

Optimizing Temper Bead Welding by Computational Weld Mechanics and Design of Experiment Matrix

Temper bead welding is usually done by experiment, i.e., trial and error. This paper describes a computational weld mechanics model to compute the transient temperature and transient microstructure evolution in temper bead welds. The computed hardness from this model, is compared to measured hardness for validation. Furthermore, the effects of power per unit length, welding current, and welding speed on final hardness, are studied by designing and implementing three design-of-experiment matrices.

Challenges in Validation of Computational Weld Mechanics Code to Compute Residual Stress and Distortion in Welds

Validation of a computational weld mechanics (CWM) code for a particular welding application requires an estimate of the difference between experimentally measured parameters and parameters computed by a computational model. This requires estimates of the uncertainty in both the experimental data and the computational data and this requires careful design of both the experiment and the CWM model. The authors experience in performing validation tests for a CWM code is summarized. An example of a validation test for a welding application that compares measured and computed transient temperatures, displacements and strains is described in detail that demonstrates that model can accurately predict this data. Challenges on both the experimental side and computational side are discussed but the greatest challenge is the limited availability of experimental data that has a measure of uncertainty. [DOI: 10.1115/1.4024458]

A Direct-Search Computational Weld Mechanics Optimization Using Least-Square Approximation

Using a frame-work for exploring a design space in Computational Weld Mechanics (CWM), a recent direct-search algorithm from Kolda, Lewis and Torczon is modified to use a least-square approximation to improve the method of following a path to the minimum in the algorithm. To compare the original and modified algorithms, a CWM optimization problem on a 152 × 1220 × 12.5 mm bar of Aluminum 5052-H32 is solved to minimize the weld distortion mitigated by a side heating technique. The CWM optimization problem is to find the best point in the space of side heater design parameters: power, heated area, longitudinal and transverse distance from the weld such that the final distortion is as low as possible (minimized). This CWM optimization problem is constrained to keep the stress level generated by the side heaters, in the elastic region to avoid adding an additional permanent plastic strain to the bar. The number of iterations, size of DOE matrix required and CPU time to find the minimum for the two algorithms are compared.Copyright

Exploring the parametric design space to manage computational weld mechanics analyses using design of experiment

Development of a computational weld mechanics (CWM) framework that automates multiple set-ups and evaluations is required to practically explore a design space by given design of experiment (DOE) matrices. Saving an expert-user’s time to prepare several analyses and allocating CPUs to be utilised efficiently make this framework cost effective and time effective to manage designer-driven optimisation and control application of CWM. A validation analysis is conducted in this framework to identify the CWM control vector that minimises the difference between the computed and experimental data. Actual CWM problems with continuous and/or discontinuous parametric design spaces are solved in this framework to minimise weld distortion using derivative-free optimisation algorithms and DOE matrices that become attractive in this framework.

A Framework for Designer-Driven Exploration of Computational Weld Mechanics Design Space

This paper demonstrates a framework for analyzing multiple analyses of welds and welded structures as a single run. For each point in the design space a 3D transient thermal-stress analyses is solved for the weld and/or a welded structure. This enables the designer to explore the design state space for the design points specified in a Design of Experiments (DOE) matrix. This makes it simpler and quicker for a human to set up tens or hundreds of analyses. Also the CPU time to solve each analysis must be sufficiently short. Examples of DOE matrices created for Computational Weld Mechanics (CWM) optimization analyses are presented; i) a discontinuous combinatorial optimization of the weld sequence to minimize distortion in a girth weld, ii) a continuous optimization to mitigate distortion of an edge welded bar using side heaters, pre-bending with prescribed deflections at isolated points and pre-bending with a smooth prescribed displacement function.Copyright

Sensitivity of Hot Cracking to Weld Procedure Parameters and Transverse Traction

The paper focuses on hot cracking susceptibility analysis and a post-processor for a computational weld mechanics (CWM) framework to identify the transient 3D region susceptible to hot cracking for a welded structure. The Sigmajig hot cracking analysis analyzed by Zacharia in [1] is used. The specimen is 50 × 25 × 0.25 mm 316 stainless steel sheet welded with a TIG process with a constant transverse force applied on the side surfaces. The tensile traction, welding power, welding speed, and 4 double ellipsoid shape parameters are varied in a sensitivity analysis of hot cracking wrt the transverse tensile traction and several welding speeds for which power per unit length is kept constant. A control problem solved to adjust 4 double ellipsoid shape parameters for different welding speeds with constant power per unit length. The analysis includes 117 analyses for the control part and 28 analyses for the sensitivity part that are implemented in an automated mode of the CWM framework to save user time in implementation. The user prepares one single base project setup and 117+28 CWM analyses by the user specifying a DOE-matrix.Copyright

Mitigation of Distortion in an Edge-Welded Bar by Clamping Parameters

The objective is to demonstrate a capability developed to explore a design space to minimize distortion and evaluate the sensitivity of the distortion of an edge weld on a 152 × 1220 × 12.5 mm bar of Aluminum 5052-H32 wrt clamping. For each point in the design space, a full computational model that includes transient 3D thermal and stress analysis is solved using VrWeld software [1]. The bar has no displacement constraints other than rigid body constraints and the resulting camber from welding bends the bar. The minimum distortion in this discrete design space is assumed to be the optimal design to minimize the final distortion, i.e., objective function. The design space parameters chosen are clamping parameters, i.e., prescribed displacements, and the release time value in the design space. The bar is fixed at both ends and subjected to a range of prescribed displacements opposite to the direction of the camber. In the first set of tests the prescribed displacement is applied directly in the middle of the bar and in the second set of tests the displacement field is prescribed as a parabolic displacement along the full length of the bottom of the bar. In addition to the effect of the prescribed displacement on final distortion is shown to be highly correlated with the delay time at which the prescribed displacement is released after the weld is finished. The best pair of the prescribed value and the release time value in the design space. The distortion and residual stress fields in the mitigated bar with a nodal prescribed displacement in the middle of the bar and the mitigated bar with a parabolic prescribed displacement along the bottom surface of the bar are compared.Copyright

The Evolution of Stress and Strain Tensors in Welds With Mitigation of Residual Stress and Distortion

By getting the data from an ordered set of Gauss points on the flow line of a material point that passes near the weld pool, the evolution of the stress/strain tensor fields is visualized. The principal plastic strain tensor, principal deviatoric stress tensor, hydrostatic stress and temperature are visualized. This is done for three weld distortion mitigation strategies: i) pre-bending by applying a prescribed displacement, ii) applying a tensile load to the weld and iii) applying side heaters to the weld. Visualizing the evolution of the principal stress and strain vectors gives interesting insight into the mechanics of plastic deformation near a weld pool.© 2011 ASME

Optimization of a Weld Overlay on a Plate Structure

An overlay weld repair procedure on a 1066.8 × 1066.8 mm 2 square plate 25.4 mm thick was simulated to compute the 3D transient temperature, microstructure, strain, stress, and displacement of the overlay weld repair procedure. The application for the overlay was the repair of cavitation erosion damage on a large Francis turbine used in a hydroelectric project. The overlay weld consisted of a 4 × 6 pattern of 100 × 100 mm 2 squares. Each square was covered by 15 weld passes. Each weld pass was 100 mm long. The total length of weld in the six squares was 36 m. The welds in each square were oriented either front-to-back or left-to-right. The welding process was shielded metal arc. The analysis shows that alternating the welding direction in each square produces the least distortion. A delay time of 950 s between the end of one weld pass and the start of the next weld pass was imposed to meet the requirement of a maximum interpass temperature to 50°C.

Challenges in Verification of CWM Software to Compute Residual Stress and Distortion in Welds

Validation of a Computational Weld Mechanics (CWM) code for a particular welding application requires an estimate of the difference between experimentally measured parameters and parameters computed by a computational model. This requires estimates of the uncertainty in both the experimental data and the computational data and this requires careful design of both the experiment and the CWM model. The authors experience in performing validation tests for a Computational Weld Mechanics (CWM) code is summarized. An example of a validation test for a welding application that compares measured and computed transient temperatures, displacements and strains is described in detail that demonstrates that model can accurately predict this data. Challenges on both the experimental side and computational side are discussed but the greatest challenge is the limited availability of experimental data that has a measure of uncertainty.Copyright