Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and...
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Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the xgboostmachinelearning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 mu m, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.
In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the xgboost machine learning algorithm is used to calculate IGBT junction temperature in the ann...
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In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the xgboost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The xgboost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value. (C) 2022 The Author(s). Published by Elsevier Ltd.
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