Investigating the feasibility of a machine learning-based framework for welding design****

SKC工程公司是加拿大著名的焊接工程服务提供商，在加拿大西部拥有250多家客户。对于许多客户来说，该公司已经观察到焊接变形是一个常见的问题。通常的方法是依靠焊接工程师的经验来优化焊接，这很难重现，或者使用模拟算法来捕获和耦合基于3D结构和热应力模拟的焊接物理。检查所有可能的模拟场景目前实际上是不可行的，因为这需要从潜在的数千种排列中进行选择。**在这个项目中，我们将与不列颠哥伦比亚大学的Abolmaesumi博士合作，研究使用机器学习框架从模拟中学习并创建实际焊接过程的具体案例实时模型的可行性。在这个项目中获得的知识将有可能在使用机器学习加速有限元建模和预测模拟结果方面取得突破

SKC Engineering is a renowned Canadian provider for welding engineering services with over 250 clients throughout Western Canada. For many clients, the company has observed that the weld distortion is a common problem. A usual approach is to either optimize welding by relying on the experience of welding engineers, which is difficult to reproduce, or use simulation algorithms to capture and couple physics of welds based on 3D structural and thermal stress simulations. Examining all possible simulation scenarios is currently practically infeasible, as this requires choosing from potentially thousands of permutations. **In this project, in collaboration with Dr. Abolmaesumi at the University of British Columbia, we will examine the feasibility of using a machine learning framework to learn from simulations and create case-specific real-time models of real-world welding processes. Knowledge gained in this project will be potentially a breakthrough in the use of machine learning in accelerating finite element modelling and prediction of simulation outcomes.**