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The Selection of the welding process is one of the most significant decision-making problems, and it involves a wide range of information following the type of product. Hence, the automation of knowledge through a knowledge-based system will significantly enhance the decision-making process and simplify for identifying the most appropriate welding processes. The aims of this paper for explicates a knowledge-based system developed for recognising the most suitable welding processes for repairing shredder hammer by using data envelopment analysis (DEA) and p-robust technique. The proposed approach is used for ranking six welding processes which are commonly used, namely shielded metal arc welding (SMAW), flux cored arc welding (FCAW), submerged arc welding (SAW), oxyacetylene gas welding (OAW), gas tungsten arc welding (GTAW), and gas metal arc welding (GMAW). In order to determine the best welding process among competitive welding processes for repairing of shredder hammer, ten parameters are used, namely the availability of consumable, welding process type (manual and automatic), flexibility of welding position, weld-ability on base metal, initial preparation required, welding procedures, post-weld cleaning, capital cost, operating factor, and deposition rate. Furthermore, the sensitivity analysis of regret value ( p ) is investigated in three cases proposed.

Selection of Welding Process for Repairing Shredder Hammer by Integrated Data Envelopment Analysis (DEA) and P-robust Technique