Autobody Spot-Welding Optimization Based on Genetic Algorithm (GA)
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Abstract
Resistance Spot Welding (RSW) is an important technology that has extensively been applied in the automotive industry. The advantages of this technology are quick and efficient for joining two pieces of metal together. The minimum number of spot welds without decreasing strength has gained much attention from researchers in recent years. In this research, the finite element (FEM) method and the Genetic Algorithm (GA) were proposed to optimize the Spot-welding spacing on automotive aluminum alloy parts. The simulation model was developed to analyze the von Mises stress generated by the side collision on the parts. According to the Insurance Institute for Highway Safety (IIHS) collision test, the computational experiment was designed that the diameter of the steel pipe 180 millimeters perpendicularly collided with the workpiece at the velocity of 14 meters per second to study the maximum stress of the workpiece. This numerical simulation cooperated with the GA for optimizing the spot-welding spacing under the lowest von Mises stress distribution criteria. From the experimental results, it was found that The GA presented the optimal spot-weld spacing on an automotive part. It could reduce the number of spot welds, weld belong-time, and power consumption by 10%. These results demonstrate that the proposed GA is a promising approach for the spot-weld spacing optimization for the Automotive part.
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