Advanced Welding Engineering
Computational Weld Mechanics (CWM) deals with the models, algorithms, and software to predict the behaviour of welds in the welded structures.
CWM uses mature algorithms with a good level of reliability for a variety of applications. Such capability is desired in product design when
our engineers need to go beyond standards and think out of the box. My team is now capable of using modern high power computing methods for
designer-driven optimization of welded structures and related welding procedures for all levels of application. Using the state of the art
computational platforms for simulation and weld modeling, combined with our extensive practical experience, our welding engineers apply
their creativity, expertise, and skill to be optimal, more productive, and innovative when developing solutions to welding problems. We have
a good track record in delivering solutions involving thermal, microstructure, and mechanical analysis for welded structures at any level
of complexity, size, and the number of weld passes.
Welding sequence and intermittent welding design, which determines the best welding pattern
in multi-pass welds, are familiar techniques to control the distortion when dealing with
multi-pass welded structures. Finding the best solution for such a design is limited by available
resources since a designer needs to pick one out of many patterns i.e. hundreds to thousands
usually based on previous experience. Optimization of this problem is not feasible through shop
trials, so we use computer modeling that automates the implementation of several patterns for minimal
distortion, residual stress, or other design objectives. My team is
a pioneer in developing and patenting
several signature techniques that can practically help engineers to deterministically define the best weld
sequence in any structures with multi-pass welding. We are famous for our techniques namely, Quick Joint
Rigidity Method, Progressive Joint Rigidity Method, Surrogate Method, Artificial Neural Network (ANN), and Evolutionary Method. We always
deliver the highest achievable quality within your project schedule.
-Weld Sequence 1 ----Weld Sequence 2 ----Weld Sequence 3 ----Weld Sequence 4
Our signature methods for defining the best weld sequence are now packaged in an Abaqus plugin called DeepWeld. The DeepWeld automates the implementation of Quick JRM, Progressive JRM, Surrogate Method, Artificial Neural Network (ANN), and Evolutionary Methods for users who are not expert in this field. Users defines the welds in a given structure and the method to use for weld sequence design. Deepweld performs all necessary calculation in the background and returns a report with the best sequence pattern for your structure.
Residual stress affects the service life and condition of weldment during
the course of service if it doesn’t
introduce immediate problems during the manufacturing process. Weld modeling is the most cost-effective
method to generate a 3D map of all stress components during and after welding as well as interaction with
operation loading condition for fatigue and creep analysis. This leads to optimal design for enhancing
service life and can avoid the further cost of rejected parts from service. We provide state-of-the-art contour
method through our partner lab in the U.S. for experimentally generating a map of weld residual stress.
Repair welding is among the most challenging aspects of welding engineering. We are capable of
modeling weld and welding procedures on existing structures for an optimal repair process that
will be validated through our experimental lab and mock-up tests to assure delivery of high
quality and risk-free repair procedure.
When general simulation-based engineering comes short for a case-specific problem-solving, we are well experienced
to integrate bespoke codes by constructing physic-based numerical recipes to create, test and program an innovative
computational application for your case-specific problems.