Abstract
With the wide application of multi-layer and multi-pass welding in industry, the traditional manual welding method is difficult to meet the needs of manufacture. Welding Robot has the advantages of stable productivity, ensuring welding quality even in special environment, so the welding robots are used at a growing trend in manufacturing fields to complete different welding tasks. In this paper, an intelligence learning method for welding robot is designed, aiming at the prediction of welding process parameters and bead geometry parameters in the welding process, deep and machine learning algorithms are used for realization. It provides an instruction for the design of process parameters to realize the intellectualization and automation of welding robot. The experimental results show that automatic parameters learning based on machine learning are effective and different learning methods should be selected for different process parameter prediction tasks in order to achieve the best prediction effect.
Highlights
In the field of industrial manufacturing, due to the drawbacks of inefficient welding and poor quality stability, manual arc welding is not suitable for the needs of intelligence
Aiming at the problem of intelligent selection of process parameters in the welding process, this paper explored a variety of learning methods including machine learning, ensemble learning to predict the process parameters in the multi-layer and multi-pass welding process
This section focuses on the machine learning models used in the experiment of robot welding process: BP neural network, CatBoost, XGBoost and CNN
Summary
In the field of industrial manufacturing, due to the drawbacks of inefficient welding and poor quality stability, manual arc welding is not suitable for the needs of intelligence. As the basis of intelligent and automatic welding, robot welding has the advantages of improving production efficiency, ensuring welding quality and adapting to intelligent requirements It has gradually replaced manual welding, met the needs of large-scale production and welding automation, and has been widely used in the field of manufacturing. Kim et al [4] proposed a new method to predict the technological parameters of machine arc welding, which is composed of neural network and multiple regression. By comparing the prediction results of various learning algorithms, the appropriate model is selected to realize the intelligence and automation of robot welding, which provides an important instruction for the intelligent control design of welding robot.
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