The grey-based Taguchi method was employed to optimize the process parameters of the submerged arc welding (SAW) in hardfacing, considering multiple weld qualities (Tarng et al. 2002).
In this new approach, the grey relational analysis is adopted to solve the SAW process with multiple weld qualities.
Experimental results have shown that optimal SAW process parameters in hardfacing can be determined effectively so as to improve multiple weld qualities through this new approach.
In this paper, the use of grey-based Taguchi methods for the optimization of the submerged arc welding (SAW) process parameters in hardfacing with considerations of multiple weld qualities is reported.
The multiple linear regression analysis and back propagation neural network were then used to estimate the weld quality in combination with extracted features.
Understanding of arc types and their inherent properties can help enhance weld prediction and weld quality and reduce welding cost and production cycle time.
The most important factors that affect plug weld quality are strength, depth, and area of weld penetration (American Welding Society 2002).
Table 3 presents the relationship between welding control technologies, combinations of base metals, and expected weld quality.
The advantages of robotic welding vary from process to process but common benefits generally include improved weld quality, increased productivity, reduced weld costs, and increased repeatable consistency of welding (Lane 1987).
They offer several advantages, such as improved weld quality, increased productivity, reduced weld costs, increased repeatable consistency of welding, and minimized human input for selection of weld parameters, path of robotic motion, and fault detection and correction.
