Active Closed-Loop Transfer Learning-Based Surrogate Models for Telescopic Boom Forklift Optimization

The telescopic boom forklift is a very efficient engineering equipment because of its distinctive boom system. However, to speed up product development and reduce costs, surrogate models should be constructed to replace computationally expensive co-simulation models for design optimization. In this paper, telescopic boom forklift design is investigated using deep transfer learning and is coupled with a mode pursuing sampling algorithm to determine and propose optimal solutions based on observed boom amplitude. A workflow that combines uncertainty analysis-based active data development, deep surrogate modeling, and global optimization is proposed. It considers the common characteristics of the existing product simulation data, the personality characteristics of the current product, and the learning characteristics of the neural network when searching for the best surrogate. We demonstrated that tuned Multiplayer perceptron (MLP) can make accurate predictions and be used to create an acceptable surrogate model. The correlation coefficient (R) indicator can reach 9.34 when only 25 groups of data are used. Additionally, a 38.28% reduction in Root mean square error (RMSE) and a 26.25% reduction in Maximum absolute error (MAE) can be achieved when compared to benchmarks. Generated designs attain the maximal boom amplitude that is lower by more than 46% and the fluctuation range from (3-35) MPa to (8-19) MPa. Overall, we have demonstrated that the proposed framework has merit and can be used as a viable methodology in telescopic boom forklift design optimization. Moreover, it can also provide useful references for the simulation optimization of other complex electromechanical products.