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Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).

This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.

The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.

This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.

This study aims to empirically examine how blockchain, one of the emerging Industry 4.0 technologies, can combat climate change by improving their green innovation performance, particularly under conditions of policy uncertainty.

This study utilizes the difference-in-difference-in-difference (DDD) method to explore the effect of blockchain on enterprises' green innovation performance. The analysis is based on data from Chinese-listed enterprises spanning the period from 2013 to 2021.

First, the adoption of blockchain in enterprises registered in areas designated as low-carbon pilot cities can significantly improve their green innovation performance. Second, the enhancement of green innovation efficiency emerges as the primary driving force behind the adoption of blockchain, thereby leading to improved green innovation performance. Lastly, it is observed that blockchain adoption has a greater positive impact on improving green efficiency in private enterprises compared to state-owned enterprises in China.

For managers, the findings can provide valuable insights to help them better prepare for the challenges and opportunities presented by the era of Industry 4.0. For policymakers, this study offers valuable insights into the interaction between new technologies in Industry 4.0 and the performance of green innovation, thereby aiding in the formulation of effective policies.

This study contributes to bridging the existing gap between the adoption of new technologies, such as blockchain, and their potential impact on climate change. Moreover, this research enriches practitioners' understanding of how new technologies in the era of Industry 4.0 can be applied to address significant challenges like climate change.