Bead Geometry Prediction Model for 9% Nickel Laser Weldment, Part 1: Global Regression Model vs. Modified Regression Model

Abstract

Due to its excellent toughness and stiffness in cryogenic conditions, 9% nickel steel is applied to LNG storage facilities, and its usage is increasing as a result of changes in environmental regulations. A study was conducted on the development of a predictive model to optimize the laser welding process of 9% nickel steel, and two prediction models were developed using one hundred data points obtained through experiments. A global regression model used as a general prediction model and a modified regression model using the p-value of the analysis of variance were developed, and their prediction performance was compared. It was found that the modified regression model was superior to the global regression model in terms of predicting the bead shape, including parameters such as penetration depth, bead height, and area ratio.

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

“…Linear regression models were postulated for all of these characteristics by taking into account all major and multiple interrelationships. For predicting and evaluating input variables, several scholars have been using multiple regression [41][42][43]. The data from the design of experiment in Table 1 and the responses in Table 3 were utilized to generate the formulas.…”

“…Such regression models were then used to estimate weld bead shape characteristics and dilution percentage. The postulated equations for the bead geometry are: Many researchers have used regression models for modeling and analyzing welding parameters [41][42][43][44]. It has been suggested that relative error should be less than 20% for statistical analysis to consider it to be reliable.…”