Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA

Abstract

To improve the corrosion resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding these properties can be achieved with minimum cost. The main problem faced on cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, and contact tip to work distance and pinch) to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. In this study, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.

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

“…for obtaining minimum dilution and optimal bead geometry using particle swarm mization [28] and ANN integrated with genetic algorithm [29].…”

“…They developed an optimization model and a prediction model for characteristics such as bead geometry and Dilution percentage. The research proposed optimal input param-eters for obtaining minimum dilution and optimal bead geometry using particle swarm optimization [28] and ANN integrated with genetic algorithm [29].…”

Revisión De Técnicas De Sistemas De Visión Artificial Para La Inspección De Procesos De Soldadura Tipo Gmaw

“…Additionally, it is used for prediction through Neural Network Toolbox with the transfer, training, learning and performance functions included in the toolbox. It is possible to establish relationships between the welding input parameters (weld speed, voltage, current) through ANN with this tool (Sreeraj, Kannan, & Maji, 2013;), as well as trace a 3D profile of the weld bead. In a research developed by (Ramos et.…”

“…Artificial Neural Networks (ANN) are biologically inspired techniques (Sreeraj et al, 2013), which simulates an interconnected parallel computational structure with some individual processing elements. Some areas where ANN are applicable are classification, associative memory, clustering or compression, generation of sequences or patterns and control systems (Cruz et al, 2015).…”

“…Many ANN have been proposed for ANN network algorithms but Back Propagation algorithm is actually considered the best algorithm for prediction task using ANN (Sreeraj, Kannan and Maji, 2013).…”