[2M1-GS-10] AI application

[2M1-GS-10-04] Generation Model of Parameters to Control the Welding Waveform Using LightGBM

〇Naoki Furukawa¹, Keiji Kadota¹, Toyokazu Kitano¹, Tomoyuki Ueyama¹ (1. DAIHEN Corporation)

Keywords:Welding, Waveform control, LightGBM, Machine Learning

In recent years, as end products have become more diverse, welding must also be performed under a variety of conditions. Arc welding machines offer the best waveform control in the user's environment to achieve highly efficient and high-quality arc welding. As a result, slight environmental changes may affect the welding results, necessitating readjustment of waveform control parameters. However, it is difficult for users to adjust the waveform control parameters because it requires knowledge of how parameters affect the welding result and empirical prediction. Therefore, this paper proposes a welding waveform control parameter generation model in which the desired welding result can be obtained by entering the desired score. We constructed a model which generates the waveform control parameters that produce the desired welding results by training LightGBM.