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Development of Optimization Methodology for Laser Welding Process Automation Using Neural Network Model and Objective Function  

Park, Young-Whan (한양대학교 BK21 혁신설계 기계인력 양성사업단)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.15, no.5, 2006 , pp. 123-130 More about this Journal
Abstract
In manufacturing, process automation and parameter optimization are required in order to improve productivity. Especially in welding process, productivity and weldablity should be considered to determine the process parameter. In this paper, optimization methodology was proposed to determine the welding conditions using the objective function in terms of productivity and weldablity. In order to conduct this, welding experiments were carried out. Tensile test was performed to evaluate the weldability. Neural network model to estimate tensile strength using the laser power, welding speed, and wire feed rate was developed. Objective function was defined using the normalized tensile strength which represented the weldablilty and welding speed and wire feed rate which represented the productivity. The optimal welding parameters which maximized the objective function were determined.
Keywords
Process automation; Process modeling; Neural network model; Objective function; Parameter optimization;
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Times Cited By KSCI : 1  (Citation Analysis)
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