Design of experiment based statistical approaches to optimize submerged arc welding process parameters

Area of Science:

• Materials Science and Engineering
• Manufacturing Processes

Background:

• Welding is a critical fabrication process with a long history, evolving significantly to meet modern industrial demands.
• Productivity and weld quality are paramount in industrial welding, necessitating careful selection and optimization of welding processes and parameters.

Purpose of the Study:

• To optimize the Submerged Arc Welding (SAW) process for enhanced productivity and weld quality.
• To identify optimal welding parameters that minimize defects and maximize efficiency in fabrication and manufacturing.

Main Methods:

• Utilized Signal-to-Noise (S/N) ratio analysis to determine the significant effects of key welding parameters.
• Employed Design of Experiments (DOE) with Quality Loss Function (OFM) and Desirability Function, alongside ANOVA, for process optimization.
• Investigated parameter optimization within and beyond existing code and standard tolerances.

Main Results:

• Identified significant effects of key parameters on welding responses using S/N ratio analysis.
• Determined an optimal zone for welding parameters that balances productivity and weld quality.
• Demonstrated potential for further optimization beyond current industry code tolerances.

Conclusions:

• Statistical optimization techniques, including S/N ratio and DOE, are effective for optimizing the SAW process.
• Optimized SAW parameters can lead to improved productivity without compromising weld quality.
• This research contributes to welding knowledge by providing a framework for advanced statistical optimization of welding processes.