Welding Sequence Optimization using Artificial Intelligence Techniques, an Overview

With heightened emphasis to improve the product quality and process efficiency, the welding industry is challenged to consider innovative approaches like artificial intelligence (AI) techniques. In terms of quality, deformation and residual stress are one of the major concerns. It has been proved that the welding sequence has significant effects on deformation and lesser magnitude for residual stress. On the other hand, robot path planning is a crucial factor to efficiently weld large and complex structures. In this sense, Welding Sequence Optimization (WSO) is suitable for minimizing constraints in the design phase, reworks, quality cost and overall capital expenditure. Traditionally the welding sequence is selected by experience and sometimes a design of experiments is required. However, it is practically infeasible to run a full factorial design to find the optimal one, because, the amount of experiments grows exponentially with the number of welding beads. Virtual tools like finite element analysis (FEA) and robotics simulators allow to run corresponding optimization tasks. In this paper we overview the literature on AI techniques applied to WSO. Additionally, some relevant works that use other methods are taken into account. The reviewed works are categorized by AI technique. Keywords— Welding sequence optimization, welding distortion, welding residual stress, welding process optimization.